Package 'lares'

Title: Lean Analytics and Robust Exploration Sidekick
Description: Auxiliary package for better/faster analytics, visualization, data mining, and machine learning tasks. With a wide variety of family functions, like Machine Learning, Data Wrangling, Marketing Mix Modeling (Robyn), Exploratory, API, and Scrapper, it helps the analyst or data scientist to get quick and robust results, without the need of repetitive coding or advanced R programming skills.
Authors: Bernardo Lares [aut, cre]
Maintainer: Bernardo Lares <[email protected]>
License: MIT + file LICENSE
Version: 5.4.0
Built: 2026-05-23 09:23:20 UTC
Source: https://github.com/laresbernardo/lares

Help Index

New Line Feed for Long Strings (Wrapper)

Description

Add a break or new line without breaking words. Automatically, the function can detect your plot's width and will dynamically set an auto width. You can adjust the relation (rel) parameter for different fonts and sizes until perfect harmony found. Quite similar to stringr::str_wrap but, if the text vector is a factor, the levels will be kept in order and transformed.

Usage

autoline(text, top = "auto", rel = 9)

Arguments

text

Character or factor vector.
top

Integer. How many characters aprox. should be on each line?
rel

Numeric. Relation of pixels and characters per line

Value

Character. String (vector) including some \n within.

See Also

Other Tools: bind_files(), bring_api(), cal_split(), chr2num(), db_download(), db_upload(), dont_sleep(), export_plot(), export_results(), files_functions(), font_exists(), formatColoured(), formatHTML(), glued(), grepm(), h2o_selectmodel(), haveInternet(), image_metadata(), importxlsx(), ip_data(), json2vector(), list_cats(), listfiles(), mail_send(), markdown2df(), move_files(), msplit(), myip(), quiet(), read.file(), statusbar(), tic(), try_require(), updateLares(), warnifnot(), what_size()

Examples

cat(autoline("This is a long text that may not fit into a single line", 8))

text <- factor(c("First value", "Second value", "First value"),
  levels = c("First value", "Second value")
)
autoline(text, 1)

path <- file.path(R.home("doc"), "THANKS")
text <- paste(readLines(path), collapse = " ")
cat(autoline(text))

Balance Binary Data by Resampling: Under-Over Sampling

Description

This function lets the user balance a given data.frame by resampling with a given relation rate and a binary feature.

Usage

balance_data(df, var, rate = 1, target = "auto", seed = 0, quiet = FALSE)

Arguments

df

Vector or Dataframe. Contains different variables in each column, separated by a specific character
var

Variable. Which variable should we used to re-sample dataset?
rate

Numeric. How many X for every Y we need? Default: 1. If there are more than 2 unique values, rate will represent percentage for number of rows
target

Character. If binary, which value should be reduced? If kept in "auto", then the most frequent value will be reduced.
seed

Numeric. Seed to replicate and obtain same values
quiet

Boolean. Keep quiet? If not, informative messages will be shown.

Value

data.frame. Reduced sampled data.frame following the rate of appearance of a specific variable.

See Also

Other Data Wrangling: categ_reducer(), cleanText(), date_cuts(), date_feats(), file_name(), formatHTML(), holidays(), impute(), left(), normalize(), num_abbr(), ohe_commas(), ohse(), quants(), removenacols(), replaceall(), replacefactor(), textFeats(), textTokenizer(), vector2text(), year_month(), zerovar()

Examples

data(dft) # Titanic dataset
df <- balance_data(dft, Survived, rate = 1)
df <- balance_data(dft, .data$Survived, rate = 0.5, target = "TRUE")

Bind Files into Dataframe

Description

This function imports and binds multiple files into a single data.frame. Files must be inserted with absolute roots files names.

Usage

bind_files(files)

Arguments

files

Character vector. Files names.

Value

data.frame with data joined from all files passed.

See Also

Other Tools: autoline(), bring_api(), cal_split(), chr2num(), db_download(), db_upload(), dont_sleep(), export_plot(), export_results(), files_functions(), font_exists(), formatColoured(), formatHTML(), glued(), grepm(), h2o_selectmodel(), haveInternet(), image_metadata(), importxlsx(), ip_data(), json2vector(), list_cats(), listfiles(), mail_send(), markdown2df(), move_files(), msplit(), myip(), quiet(), read.file(), statusbar(), tic(), try_require(), updateLares(), warnifnot(), what_size()

Get API (JSON) and Transform into data.frame

Description

This function lets the user bring API data as JSON format and transform it into data.frame.

Usage

bring_api(url, status = TRUE)

Arguments

url

Character. API's URL to GET.
status

Boolean. Display status message?

Value

data.frame of url GET results or NULL if no results returned by API.

See Also

Other Tools: autoline(), bind_files(), cal_split(), chr2num(), db_download(), db_upload(), dont_sleep(), export_plot(), export_results(), files_functions(), font_exists(), formatColoured(), formatHTML(), glued(), grepm(), h2o_selectmodel(), haveInternet(), image_metadata(), importxlsx(), ip_data(), json2vector(), list_cats(), listfiles(), mail_send(), markdown2df(), move_files(), msplit(), myip(), quiet(), read.file(), statusbar(), tic(), try_require(), updateLares(), warnifnot(), what_size()

Other API: fb_accounts(), fb_ads(), fb_creatives(), fb_insights(), fb_process(), fb_report_check(), fb_rf(), fb_token(), gemini_ask(), gpt_ask(), queryGA(), slackSend()

Cache Save and Load (Write and Read)

Description

This function lets the user save and load a cache of any R object to improve timings and UX.

Usage

cache_write(
  data,
  base = "temp",
  cache_dir = getOption("LARES_CACHE_DIR"),
  ask = FALSE,
  overwrite = TRUE,
  quiet = FALSE,
  ...
)

cache_read(
  base,
  cache_dir = getOption("LARES_CACHE_DIR"),
  ask = FALSE,
  overwrite = TRUE,
  quiet = FALSE,
  ...
)

cache_exists(base = NULL, cache_dir = getOption("LARES_CACHE_DIR"), ...)

cache_clear(cache_dir = getOption("LARES_CACHE_DIR"), quiet = FALSE, ...)

cache_pipe(data, base = "cache_pipe", read = TRUE, write = TRUE, ...)

Arguments

data

Object
base

Character vector. Unique name for your cache file. You can pass a character vector with multiple elements that will be concatenated. All cache files with start with lares_cache_* automatically to quickly detect these cache files.
cache_dir

Character. Where do you want to save you cache files? By default they'll be stored on tempdir() but you can change it using this parameter or setting a global option called "LARES_CACHE_DIR".
ask

Boolean. If cache exists, when reading: (interactive) ask the user if the cache should be used to proceed or ignored; when writing, (interactive) ask the user if the cache should be overwritten. Note that you can only ask for one cache file at a time because vectors are concatenated.
overwrite

Boolean. Set to overwrite existing cache file. When reading, this parameter answers to ask prompt instead.
quiet

Boolean. Keep quiet? If not, informative messages will be shown.
...

Additional parameters.
read, write

Boolean. Do you wish to read and or write cache?

Value

cache_write. No return value, called for side effects.

cache_read. R object. Data from cache file or NULL if no cache found.

cache_exists. Boolean. Result of base existence.

cache_clear. Invisible vector containing cache file names removed.

cache_pipe. Same as x or cached result.

Examples

x <- list(a = 1, b = 2:4)
base <- c(as.character(Sys.Date()), "A", "B")
cache_write(x, base)
cache_read(base, ask = FALSE)
cache_exists(base)
cache_clear()

# Use cache_pipe() for automatic use (saves and loads if exists)
cache_pipe({
  x <- 1
  x + 1
})
cache_pipe({
  x <- 1
  x + 1
})

Split ICS File

Description

This function splits a large ICS calendar file into smaller files.

Usage

cal_split(file, n_splits = 5, prefix = "calendar_split_", dir = NULL)

Arguments

file

Character. Path to the ICS file to split.
n_splits

Integer. Number of files to split into.
prefix

Character. Prefix for the output filenames.
dir

Character. Directory to save the output files. Defaults to same directory as input file.

Value

Invisible list. The paths of the created files.

See Also

Other Tools: autoline(), bind_files(), bring_api(), chr2num(), db_download(), db_upload(), dont_sleep(), export_plot(), export_results(), files_functions(), font_exists(), formatColoured(), formatHTML(), glued(), grepm(), h2o_selectmodel(), haveInternet(), image_metadata(), importxlsx(), ip_data(), json2vector(), list_cats(), listfiles(), mail_send(), markdown2df(), move_files(), msplit(), myip(), quiet(), read.file(), statusbar(), tic(), try_require(), updateLares(), warnifnot(), what_size()

Reduce categorical values

Description

This function lets the user reduce categorical values in a vector. It is tidyverse friendly for use on pipelines

Usage

categ_reducer(
  df,
  var,
  nmin = 0,
  pmin = 0,
  pcummax = 100,
  top = NA,
  pvalue_max = 1,
  cor_var = "tag",
  limit = 20,
  other_label = "other",
  ...
)

Arguments

df

Categorical Vector
var

Variable. Which variable do you wish to reduce?
nmin

Integer. Number of minimum times a value is repeated
pmin

Numerical. Percentage of minimum times a value is repeated
pcummax

Numerical. Top cumulative percentage of most repeated values
top

Integer. Keep the n most frequently repeated values
pvalue_max

Numeric (0-1]. Max pvalue categories
cor_var

Character. If pvalue_max < 1, you must define which column name will be compared with (numerical or binary).
limit

Integer. Limit one hot encoding to the n most frequent values of each column. Set to NA to ignore argument.
other_label

Character. With which text do you wish to replace the filtered values with?
...

Additional parameters.

Value

data.frame df on which var has been transformed

See Also

Other Data Wrangling: balance_data(), cleanText(), date_cuts(), date_feats(), file_name(), formatHTML(), holidays(), impute(), left(), normalize(), num_abbr(), ohe_commas(), ohse(), quants(), removenacols(), replaceall(), replacefactor(), textFeats(), textTokenizer(), vector2text(), year_month(), zerovar()

Examples

data(dft) # Titanic dataset
categ_reducer(dft, Embarked, top = 2) %>% freqs(Embarked)
categ_reducer(dft, Ticket, nmin = 7, other_label = "Other Ticket") %>% freqs(Ticket)
categ_reducer(dft, Ticket, pvalue_max = 0.05, cor_var = "Survived") %>% freqs(Ticket)

Validate inputs (attributions, options, ...)

Description

This function validates if inputs match all/any of your options and return error/message with possible options to use. Similar to match.arg() but more flexible.

This function checks if an object has a specific attribute and stops if not.

Check whether a value or vector is or is not following a set of rules. For example: is an URL, is an ID vector, are non-variant or constant values, are binary values... Notice that is_ will return the result for each observation and are_ for the whole vector.

Usage

check_opts(
  inputs,
  opts,
  input_name = "input",
  type = "all",
  not = "stop",
  quiet = TRUE
)

check_attr(object, attr = "type", check = NULL, stop = TRUE)

is_url(x, ...)

is_ip(x, ...)

are_id(x)

are_constant(x)

are_binary(x)

is_even(x)

is_odd(x)

Arguments

inputs

Vector character. Check options.
opts

Vector character. Valid options.
input_name

Character. Custom your message and change "input" for any other string. For example: "column names".
type

Character. Options: "all", "any."
not

Character. Options: "stop", "message", "print", "return".
quiet

Boolean. Keep quiet? If not, returns logical value.
object

Object of any kind
attr

Character. Attribute to check
check

Character. Attribute value
stop

Boolean. Stop if doesn't check?
x

Vector
...

Additional parameters passed to grepl()

Value

Boolean. Result of inputs in opts (options). Depending on type and/or stop arguments, errors or messages will be shown.

No return value, called for side effects.

is_url. Boolean. Result of checking if x is a valid URL string.

is_ip. Boolean. Result of checking if x is a valid IP string.

are_id. Boolean. Result of checking if x is a potential ID vector

are_constant. Boolean. Result of checking if x is a constant vector

are_binary. Boolean. Result of checking if x is a binary vector

Examples

opts <- c("A", "B", "C")
# Let's check the "all" logic
check_opts(inputs = c("A", "B"), opts, quiet = FALSE)
check_opts(inputs = c("X"), opts, not = "message", quiet = FALSE)
check_opts(inputs = c("A", "X"), opts, input_name = "value", not = "warning")
# Now let's check the "any" logic
check_opts(inputs = c("A", "X"), opts, type = "any")
check_opts(inputs = c("X"), opts, type = "any", not = "message")
check_opts(inputs = c("A", NA), opts, type = "any")
# Final trick: just ignore results
check_opts(inputs = "X", opts, not = "invisible")
test <- data.frame()
attributes(test)
check_attr(test, "class", "data.frame")
# check_attr(test, "class", "not.data.frame")
is_url(c("google.com", "http://google.com"))

is_ip(c("163.114.132.0", "7.114.132", "0.0.0.0", "1.1.1.1."))

are_id(1:10)
are_id(LETTERS[1:10])

are_constant(rep(1, 10))
are_constant(1:10)

are_binary(c("A", "B", "A"))

is_even(1:5)
is_odd(1:5)
is_odd(c(0, 1.5, 2.5, NA, Inf, NULL))

Check character values for date/numeric/logical and change datatype

Description

Automatically check a vector, data.frame or list for numeric, logical, date content and change their datatype. Note that factors are skipped in case the user requires character numeric values to be kept as they are.

Usage

chr2num(data)

chr2logical(data)

chr2date(data)

Arguments

data

Vector, data.frame or list

See Also

Other Tools: autoline(), bind_files(), bring_api(), cal_split(), db_download(), db_upload(), dont_sleep(), export_plot(), export_results(), files_functions(), font_exists(), formatColoured(), formatHTML(), glued(), grepm(), h2o_selectmodel(), haveInternet(), image_metadata(), importxlsx(), ip_data(), json2vector(), list_cats(), listfiles(), mail_send(), markdown2df(), move_files(), msplit(), myip(), quiet(), read.file(), statusbar(), tic(), try_require(), updateLares(), warnifnot(), what_size()

Examples

str(chr2num(c("1", "2", "3")))
df <- data.frame(A = c("1", "3"), B = c("A", "B"), c = c(pi, pi * 2))
str(chr2num(df))
lst <- list(A = c("1", "2", "3"), B = c("A", "B", "3"), C = pi, D = 3L)
str(chr2num(lst))
lst2 <- list(layer1 = ":D", layer2 = lst)
str(chr2num(lst2))
str(chr2logical(c(NA, "true", FALSE)))

Lower/Upper Confidence Intervals

Description

Calculate lower and upper confidence intervals given a mean, standard deviation, sample size, and confidence level. You may want to use ci_var() to calculate all values quickly.

Usage

ci_lower(mean, ssd, n, conf = 0.95)

ci_upper(mean, ssd, n, conf = 0.95)

Arguments

mean

Numeric. Mean: mean(var, na.rm = TRUE)
ssd

Numeric. Standard deviation: sd(var, na.rm = TRUE)
n

Integer. Amount of observations: n()
conf

Numeric (0-1). Confidence level.

Value

Vector with confidence limit value.

See Also

Other Confidence: ci_var()

Examples

ci_lower(100, 5, 10)
ci_upper(100, 5, 10)

Confidence Intervals on Dataframe

Description

Calculate confidence intervals for a continuous numerical column on a dataframe, given a confidence level. You may also group results using another variable. Tidyverse friendly.

Usage

ci_var(df, var, group_var = NULL, conf = 0.95)

Arguments

df

Dataframe
var

Variable name. Must be a numerical column.
group_var

Variable name. Group results by another variable.
conf

Numeric. Confidence level (0-1).

Value

data.frame mean, standard deviation, counter, upper and lower CIs.

See Also

Other Confidence: ci_lower()

Examples

data(dft) # Titanic dataset
ci_var(dft, Fare)
ci_var(dft, Fare, Pclass)
ci_var(dft, Fare, Pclass, conf = 0.99)

Clean text strings automatically

Description

cleanText: Clean character strings automatically. Options to keep ASCII characters only, keep certain characters, lower caps, title format, are available.

cleanNames: Resulting names are unique and consist only of the _ character, numbers, and ASCII letters. Capitalization preferences can be specified using the lower parameter.

Usage

cleanText(
  text,
  spaces = TRUE,
  keep = "",
  lower = TRUE,
  ascii = TRUE,
  title = FALSE
)

cleanNames(df, num = "x", keep = "_", ...)

Arguments

text

Character Vector
spaces

Boolean. Keep spaces? If character input, spaces will be transformed into passed argument.
keep

Character. String (concatenated or as vector) with all characters that are accepted and should be kept, in addition to alphanumeric.
lower

Boolean. Transform all to lower case?
ascii

Boolean. Only ASCII characters?
title

Boolean. Transform to title format (upper case on first letters).
df

data.frame/tibble.
num

Add character before only-numeric names.
...

Additional parameters passed to cleanText().

Details

Inspired by janitor::clean_names.

Value

Character vector with transformed strings.

data.frame/tibble with transformed column names.

See Also

Other Data Wrangling: balance_data(), categ_reducer(), date_cuts(), date_feats(), file_name(), formatHTML(), holidays(), impute(), left(), normalize(), num_abbr(), ohe_commas(), ohse(), quants(), removenacols(), replaceall(), replacefactor(), textFeats(), textTokenizer(), vector2text(), year_month(), zerovar()

Other Text Mining: ngrams(), remove_stopwords(), replaceall(), sentimentBreakdown(), textCloud(), textFeats(), textTokenizer(), topics_rake()

Examples

cleanText("Bernardo Lares 123")
cleanText("Bèrnärdo LáreS 123", lower = FALSE)
cleanText("Bernardo Lare$", spaces = ".", ascii = FALSE)
cleanText("\\@®ì÷å   %ñS  ..-X", spaces = FALSE)
cleanText(c("maría", "€", "núñez_a."), title = TRUE)
cleanText("29_Feb-92()#", keep = c("#", "_"), spaces = FALSE)

# For a data.frame directly:
df <- dft[1:5, 1:6] # Dummy data
colnames(df) <- c("ID.", "34", "x_2", "Num 123", "Nòn-äscì", "  white   Spaces  ")
print(df)
cleanNames(df)
cleanNames(df, lower = FALSE)

Automated K-Means Clustering + PCA/t-SNE

Description

This function lets the user cluster a whole data.frame automatically. As you might know, the goal of kmeans is to group data points into distinct non-overlapping subgroups. If needed, one hot encoding will be applied to categorical values automatically with this function. For consideration: Scale/standardize the data when applying kmeans. Also, kmeans assumes spherical shapes of clusters and does not work well when clusters are in different shapes such as elliptical clusters.

Usage

clusterKmeans(
  df,
  k = NULL,
  wss_var = 0,
  limit = 15,
  drop_na = TRUE,
  ignore = NULL,
  ohse = TRUE,
  norm = TRUE,
  algorithm = c("Hartigan-Wong", "Lloyd", "Forgy", "MacQueen"),
  dim_red = "PCA",
  comb = c(1, 2),
  seed = 123,
  quiet = FALSE,
  ...
)

Arguments

df

Dataframe
k

Integer. Number of clusters
wss_var

Numeric. Used to pick automatic k value, when k is NULL based on WSS variance while considering limit clusters. Values between (0, 1). Default value could be 0.05 to consider convergence.
limit

Integer. How many clusters should be considered?
drop_na

Boolean. Should NA rows be removed?
ignore

Character vector. Names of columns to ignore.
ohse

Boolean. Do you wish to automatically run one hot encoding to non-numerical columns?
norm

Boolean. Should the data be normalized?
algorithm

character: may be abbreviated. Note that "Lloyd" and "Forgy" are alternative names for one algorithm.
dim_red

Character. Select dimensionality reduction technique. Pass any of: c("PCA", "tSNE", "all", "none").
comb

Vector. Which columns do you wish to plot? Select which two variables by name or column position.
seed

Numeric. Seed for reproducibility
quiet

Boolean. Keep quiet? If not, informative messages will be shown.
...

Additional parameters to pass sub-functions.

Value

List. If no k is provided, contains nclusters and nclusters_plot to determine optimal k given their WSS (Within Groups Sum of Squares). If k is provided, additionally we get:

  • df data.frame with original df plus cluster column

  • clusters integer which is the same as k

  • fit kmeans object used to fit clusters

  • means data.frame with means and counts for each cluster

  • correlations plot with correlations grouped by clusters

  • PCA list with PCA results (when dim_red="PCA")

  • tSNE list with t-SNE results (when dim_red="tSNE")

See Also

Other Clusters: clusterOptimalK(), clusterVisualK(), reduce_pca(), reduce_tsne()

Examples

Sys.unsetenv("LARES_FONT") # Temporal
data("iris")
df <- subset(iris, select = c(-Species))

# If dataset has +5 columns, feel free to reduce dimenstionalities
# with reduce_pca() or reduce_tsne() first

# Find optimal k
check_k <- clusterKmeans(df, limit = 10)
check_k$nclusters_plot
# Or pick k automatically based on WSS variance
check_k <- clusterKmeans(df, wss_var = 0.05, limit = 10)
# You can also use our other functions:
# clusterOptimalK(df) and clusterVisualK(df)

# Run with selected k
clusters <- clusterKmeans(df, k = 3)
names(clusters)

# Cross-Correlations for each cluster
plot(clusters$correlations)

# PCA Results (when dim_red = "PCA")
plot(clusters$PCA$plot_explained)
plot(clusters$PCA$plot)

Visualize K-Means Clusters for Several K Methods

Description

Visualize cluster data for assorted values of k and methods such as WSS, Silhouette and Gap Statistic. See factoextra::fviz_nbclust for more.

Usage

clusterOptimalK(
  df,
  method = c("wss", "silhouette", "gap_stat"),
  drop_na = TRUE,
  ohse = TRUE,
  norm = TRUE,
  quiet = TRUE,
  ...
)

Arguments

df

Dataframe
method

Character vector.
drop_na

Boolean. Should NA rows be removed?
ohse

Boolean. Do you wish to automatically run one hot encoding to non-numerical columns?
norm

Boolean. Should the data be normalized?
quiet

Boolean. Keep quiet? If not, informative messages will be shown.
...

Additional parameters passed to factoextra::fviz_nbclust

Value

Plot. Optimal number of clusters of df data.frame given a selected method.

See Also

Other Clusters: clusterKmeans(), clusterVisualK(), reduce_pca(), reduce_tsne()

Examples

# You must have "factoextra" library to use this auxiliary function:
## Not run: 
data("iris")
df <- subset(iris, select = c(-Species))
# Calculate and plot optimal k clusters
clusterOptimalK(df)

## End(Not run)

Visualize K-Means Clusters for Several K

Description

Visualize cluster data for assorted values of k.

Usage

clusterVisualK(df, ks = 2:6, ...)

Arguments

df

Dataframe
ks

Integer vector. Which k should be tested?
...

Additional parameters passed to clusterKmeans

Value

List. Plot and data.frame results of clustering df data.frame into ks integer clusters.

See Also

Other Clusters: clusterKmeans(), clusterOptimalK(), reduce_pca(), reduce_tsne()

Examples

Sys.unsetenv("LARES_FONT") # Temporal
data("iris")
df <- subset(iris, select = c(-Species))
df <- df[sample(nrow(df)), ]

# Calculate and plot
result <- clusterVisualK(df, ks = 2:4)
plot(result$plot)

# You can use the data generated as well
lapply(result$data, function(x) head(x$cluster, 10))

Confussion Matrix

Description

This function calculates a Confussion Matrix using crosstab for 2 or more categories. You can either set the score and threshold or the labels you wish to cross with.

Usage

conf_mat(tag, score, thresh = 0.5, sense = ">=", diagonal = TRUE, plot = FALSE)

Arguments

tag

Vector. Real known label
score

Vector. Predicted value or model's result
thresh

Integer. Threshold for selecting binary or regression models: this number is the threshold of unique values we should have in 'tag' (more than: regression; less than: classification)
sense

Character. Inequation sense for threshold: <, <=, >=, >
diagonal

Boolean. FALSE to convert diagonal numbers to zeroes. Ideal to detect must confusing categories.
plot

Boolean. Plot result? Uses mplot_conf()

Details

You may use mplot_conf() or set plot=TRUE.

Value

data.frame. Result of counting tag and score's tag given a threshold, similar to base::table().

See Also

Other Machine Learning: ROC(), export_results(), gain_lift(), h2o_automl(), h2o_predict_MOJO(), h2o_selectmodel(), impute(), iter_seeds(), lasso_vars(), model_metrics(), model_preprocess(), msplit()

Other Model metrics: ROC(), errors(), gain_lift(), loglossBinary(), model_metrics()

Examples

data(dfr) # Results for AutoML Predictions
lapply(dfr[c(1, 2)], head)

# Results for Binomial Model
conf_mat(dfr$class2$tag, dfr$class2$scores)
conf_mat(dfr$class2$tag, dfr$class2$scores, thresh = 0.3)
conf_mat(dfr$class2$tag, dfr$class2$scores, sense = "<=")

# Results for Multi-Categorical Model
conf_mat(dfr$class3$tag, dfr$class3$score)

Correlation table

Description

This function correlates a whole dataframe, running one hot smart encoding (ohse) to transform non-numerical features. Note that it will automatically suppress columns with less than 3 non missing values and warn the user.

Usage

corr(
  df,
  method = "pearson",
  use = "pairwise.complete.obs",
  pvalue = FALSE,
  padjust = NULL,
  half = FALSE,
  dec = 6,
  ignore = NULL,
  dummy = TRUE,
  redundant = NULL,
  logs = FALSE,
  limit = 10,
  top = NA,
  ...
)

Arguments

df

Dataframe. It doesn't matter if it's got non-numerical columns: they will be filtered.
method

Character. Any of: c("pearson", "kendall", "spearman").
use

Character. Method for computing covariances in the presence of missing values. Check stats::cor for options.
pvalue

Boolean. Returns a list, with correlations and statistical significance (p-value) for each value.
padjust

Character. NULL to skip or any of p.adjust.methods to calculate adjust p-values for multiple comparisons using p.adjust().
half

Boolean. Return only half of the matrix? The redundant symmetrical correlations will be NA.
dec

Integer. Number of decimals to round correlations and p-values.
ignore

Vector or character. Which column should be ignored?
dummy

Boolean. Should One Hot (Smart) Encoding (ohse()) be applied to categorical columns?
redundant

Boolean. Should we keep redundant columns? i.e. If the column only has two different values, should we keep both new columns? Is set to NULL, only binary variables will dump redundant columns.
logs

Boolean. Calculate log(x)+1 for numerical columns?
limit

Integer. Limit one hot encoding to the n most frequent values of each column. Set to NA to ignore argument.
top

Integer. Select top N most relevant variables? Filtered and sorted by mean of each variable's correlations.
...

Additional parameters passed to ohse, corr, and/or cor.test.

Value

data.frame. Squared dimensions (N x N) to match every correlation between every df data.frame column/variable. Notice that when using ohse() you may get more dimensions.

See Also

Other Calculus: corr_cross(), dist2d(), model_metrics(), quants()

Other Correlations: corr_cross(), corr_var()

Examples

data(dft) # Titanic dataset
df <- dft[, 2:5]

# Correlation matrix (without redundancy)
corr(df, half = TRUE)

# Ignore specific column
corr(df, ignore = "Pclass")

# Calculate p-values as well
corr(df, pvalue = TRUE, limit = 1)

# Test when no more than 2 non-missing values
df$trash <- c(1, rep(NA, nrow(df) - 1))
# and another method...
corr(df, method = "spearman")

Ranked cross-correlation across all variables

Description

This function creates a correlation full study and returns a rank of the highest correlation variables obtained in a cross-table.

Usage

corr_cross(
  df,
  plot = TRUE,
  pvalue = TRUE,
  max_pvalue = 1,
  type = 1,
  max = 1,
  top = 20,
  local = 1,
  ignore = NULL,
  contains = NA,
  grid = TRUE,
  rm.na = FALSE,
  quiet = FALSE,
  ...
)

Arguments

df

data.frame
plot

Boolean. Show and return a plot?
pvalue

Boolean. Returns a list, with correlations and statistical significance (p-value) for each value.
max_pvalue

Numeric. Filter non-significant variables. Range (0, 1]
type

Integer. Plot type. 1 is for overall rank. 2 is for local rank.
max

Numeric. Maximum correlation permitted (from 0 to 1)
top

Integer. Return top n results only. Only valid when type = 1. Set value to NA to use all cross-correlations
local

Integer. Label top n local correlations. Only valid when type = 2
ignore

Vector or character. Which column should be ignored?
contains

Character vector. Filter cross-correlations with variables that contains certain strings (using any value if vector used).
grid

Boolean. Separate into grids?
rm.na

Boolean. Remove NAs?
quiet

Boolean. Keep quiet? If not, show messages
...

Additional parameters passed to corr

Details

For a detailed tutorial on using this function, see: Find Insights with Ranked Cross-Correlations

Value

Depending on input plot, we get correlation and p-value results for every combination of features, arranged by descending absolute correlation value, with a data.frame plot = FALSE or plot plot = TRUE.

See Also

Other Calculus: corr(), dist2d(), model_metrics(), quants()

Other Correlations: corr(), corr_var()

Examples

Sys.unsetenv("LARES_FONT") # Temporal
data(dft) # Titanic dataset

# Only data with no plot
corr_cross(dft, plot = FALSE, top = 10)

# Show only most relevant results filtered by pvalue
corr_cross(dft, rm.na = TRUE, max_pvalue = 0.05, top = 15)

# Cross-Correlation for certain variables
corr_cross(dft, contains = c("Survived", "Fare"))

# Cross-Correlation max values per category
corr_cross(dft, type = 2, top = NA)

Correlation between variable and dataframe

Description

This function correlates a whole dataframe with a single feature. It automatically runs ohse (one-hot-smart-encoding) so no need to input only numerical values.

Usage

corr_var(
  df,
  var,
  ignore = NULL,
  trim = 0,
  clean = FALSE,
  plot = TRUE,
  top = NA,
  ceiling = 1,
  max_pvalue = 1,
  limit = 10,
  ranks = FALSE,
  zeroes = FALSE,
  save = FALSE,
  quiet = FALSE,
  ...
)

## S3 method for class 'corr_var'
plot(x, var, max_pvalue = 1, top = NA, limit = NULL, ...)

Arguments

df

Dataframe. It doesn't matter if it's got non-numerical columns: they will be filtered.
var

Variable. Name of the variable to correlate. Note that if the variable var is not numerical, 1. you may define which category to select from using 'var_category'; 2. You may have to add redundant = TRUE to enable all categories (instead of n-1).
ignore

Character vector. Which columns do you wish to exclude?
trim

Integer. Trim words until the nth character for categorical values (applies for both, target and values)
clean

Boolean. Use lares::cleanText for categorical values (applies for both, target and values)
plot

Boolean. Do you wish to plot the result? If set to TRUE, the function will return only the plot and not the result's data
top

Integer. If you want to plot the top correlations, define how many
ceiling

Numeric. Remove all correlations above... Range: (0-1]
max_pvalue

Numeric. Filter non-significant variables. Range (0, 1]
limit

Integer. Limit one hot encoding to the n most frequent values of each column. Set to NA to ignore argument.
ranks

Boolean. Add ranking numbers?
zeroes

Do you wish to keep zeroes in correlations too?
save

Boolean. Save output plot into working directory
quiet

Boolean. Keep quiet? If not, informative messages will be shown.
...

Additional parameters passed to corr and cor.test
x

corr_var object

Value

data.frame. With variables, correlation and p-value results for each feature, arranged by descending absolute correlation value.

See Also

Other Exploratory: crosstab(), df_str(), distr(), freqs(), freqs_df(), freqs_list(), freqs_plot(), lasso_vars(), missingness(), plot_cats(), plot_df(), plot_nums(), tree_var()

Other Correlations: corr(), corr_cross()

Examples

Sys.unsetenv("LARES_FONT") # Temporal
data(dft) # Titanic dataset

corr_var(dft, Survived, method = "spearman", plot = FALSE, top = 10)

# With plots, results are easier to compare:

# Correlate Survived with everything else and show only significant results
dft %>% corr_var(Survived_TRUE, max_pvalue = 0.05)

# Top 15 with less than 50% correlation and show ranks
dft %>% corr_var(Survived_TRUE, ceiling = .6, top = 15, ranks = TRUE)

Download and plot daily downloads of CRAN packages

Description

Download daily downloads stats from CRAN for any package, and plot. It can also be used as an auxiliary function to plot (cranlogs::cran_downloads) results.

Usage

cran_logs(
  input = "lares",
  from = Sys.Date() - 31,
  to = Sys.Date() - 1,
  type = "daily",
  plot = TRUE
)

Arguments

input

Character vector with package names or data.frame product of cranlogs::cran_downloads.
from, to

Dates. Range of dates to fetch downloads metrics.
type

Character. Any of: "daily" or "total".
plot

Boolean. Create a plot?

Value

List with data.frame and plot if plot=TRUE.

Examples

cran_logs(c("lares", "dplyr"), from = "2021-05-31")

Weighted Cross Tabulation

Description

A cross-tabulation function with output similar to STATA, tidy friendly, with weighting possibility.

Usage

crosstab(
  df,
  ...,
  wt = NULL,
  prow = FALSE,
  pcol = FALSE,
  pall = FALSE,
  decimals = 2,
  rm.na = FALSE,
  total = TRUE,
  order = TRUE
)

Arguments

df

Data.frame.
...

Variables. Dependent and independent variables.
wt

Variable, numeric. Weights.
prow, pcol, pall

Boolean. Calculate percent values for rows, columns, or the whole table, respectively.
decimals

Integer. How many decimals should be returned?
rm.na

Boolean. Remove NA values?
total

Boolean. Return total values column?
order

Boolean. Sort columns and rows by frequencies? Else, will be sorted alphabetically

Value

data.frame. Result of crossing the variables provided in ... and counting how many observations (rows) fall into each criteria.

See Also

Other Exploratory: corr_var(), df_str(), distr(), freqs(), freqs_df(), freqs_list(), freqs_plot(), lasso_vars(), missingness(), plot_cats(), plot_df(), plot_nums(), tree_var()

Examples

data(dft) # Titanic dataset
crosstab(dft, Survived, Pclass, total = FALSE)
# Show values in percentages
crosstab(dft, Pclass, Survived, prow = TRUE)
crosstab(dft, Pclass, Survived, pall = TRUE)
# Weighted by another variable
crosstab(dft, Survived, Pclass, wt = Fare, prow = TRUE)

DALEX Local

Description

DALEX function for local interpretations

Usage

dalex_local(explainer, observation = NA, row = 1, type = "break_down")

Arguments

explainer

Object. Result from h2o_explainer function
observation

Data.frame. If you want to use an observation that was not in the original explainer function, add here. Else, use row
row

Dataframe. Row number from the data.frame used in explainer.
type

Character. The type of variable attributions. Either shap, oscillations, break_down or break_down_interactions.

Value

List. Containing observation, breakdown results, and breakdown plot.

See Also

Other Interpretability: dalex_residuals(), dalex_variable(), h2o_explainer()

DALEX Residuals

Description

DALEX function for residuals

Usage

dalex_residuals(explainer)

Arguments

explainer

Object. Result from h2o_explainer function

Value

Plot. Based of explainer residual results.

See Also

Other Interpretability: dalex_local(), dalex_variable(), h2o_explainer()

DALEX Partial Dependency Plots (PDP)

Description

DALEX auxiliary function for creating Partial Dependency Plots and study variable's responses vs independent vector.

Usage

dalex_variable(explainer, vars, force_class = NA, seed = 123, ...)

Arguments

explainer

Object. Result from h2o_explainer function.
vars

Character vector. Which features do you wish to study?
force_class

Character. If you wish to force a class on your vars, which one do you need?
seed

Numeric. Seed for reproducibility
...

Additional parameters passed to model_profile.

Value

List. Containing PDP results, plot and vars input.

See Also

Other Interpretability: dalex_local(), dalex_residuals(), h2o_explainer()

Examples

# You must have "DALEX" library to use this auxiliary function:
## Not run: 
# Having an "explainer" object created with \code{h2o_explainer}:
# For numerical variables
dalex_variable(explainer, vars = c("Age", "Fare"))
# For categorical variables
dalex_variable(explainer, vars = c("Pclass", "Sex"))

## End(Not run)

Convert Date into Year + Cut

Description

This function returns categorical values for any date(s) using year cuts such as bimonths, quarters, terms, and halves.

Usage

date_cuts(date = Sys.Date(), type = "Q")

Arguments

date

Date. Date we wish to transform
type

Character. Any of the following: B (2 months), Q (3 months), T (4 months), H (6 months)

Value

Vector with date cut for each date

See Also

Other Data Wrangling: balance_data(), categ_reducer(), cleanText(), date_feats(), file_name(), formatHTML(), holidays(), impute(), left(), normalize(), num_abbr(), ohe_commas(), ohse(), quants(), removenacols(), replaceall(), replacefactor(), textFeats(), textTokenizer(), vector2text(), year_month(), zerovar()

Examples

date_cuts(Sys.Date(), type = "Q")
date_cuts(Sys.Date(), type = "H")

One Hot Encoding for Date/Time Variables (Dummy Variables)

Description

This function lets the user automatically create new columns out of a dataframe or vector with date/time variables.

Usage

date_feats(
  dates,
  drop = FALSE,
  only = NA,
  append = FALSE,
  holidays = FALSE,
  country = "Venezuela",
  currency_pair = NA,
  quiet = FALSE
)

Arguments

dates

Vector or dataframe. Non-date/time columns will be automatically ignored/extracted.
drop

Boolean. Should the original date/time columns be kept in the results? Only valid when input is a dataframe.
only

Character or vector. Which columns do you wish to process? If non are explicitly defined, all will be processed
append

Boolean. Append results to existing data.frame? If FALSE, only calculated values will be returned.
holidays

Boolean. Include holidays as new columns?
country

Character or vector. For which countries should the holidays be included?
currency_pair

Character. Which currency exchange do you wish to get the history from? i.e, USD/COP, EUR/USD...
quiet

Boolean. Keep quiet? If not, informative messages will be shown.

Value

data.frame with additional features calculated out of time or date vectors.

See Also

Other Data Wrangling: balance_data(), categ_reducer(), cleanText(), date_cuts(), file_name(), formatHTML(), holidays(), impute(), left(), normalize(), num_abbr(), ohe_commas(), ohse(), quants(), removenacols(), replaceall(), replacefactor(), textFeats(), textTokenizer(), vector2text(), year_month(), zerovar()

Other Feature Engineering: holidays(), ohse()

Other One Hot Encoding: holidays(), ohe_commas(), ohse()

Examples

df <- data.frame(
  dates = sample(seq(Sys.Date() - 365, Sys.Date(), by = 1), 50),
  times = sample(seq(Sys.time() - 1e7, Sys.time(), by = 1), 50)
)

# Input as a vector or dataframe
date_feats(df, drop = TRUE, quiet = TRUE) %>% head(10)

# Holidays given a date range and country
## Not run: 
hol <- date_feats(
  seq(Sys.Date() - 365, Sys.Date(), by = 1),
  holidays = TRUE,
  country = "Venezuela"
)
head(hol[!is.na(hol$holiday_name), ])

## End(Not run)

Download/Import Dropbox File by File's Name

Description

This function lets the user download a file from Dropbox, specifying its name, using a previously created token or with interactive window.

Usage

db_download(
  query,
  local_path = NULL,
  xlsx = TRUE,
  token_dir = NA,
  token_name = "token_pers.rds",
  quiet = FALSE
)

Arguments

query

Search string. This string is split (on spaces) into individual words. Files will be used if they contain all words in the search string.
local_path

Character. Path to save file to. If NULL (the default), saves file to working directory with same name. If not, but a valid folder, file will be saved in this folder with same basename as path. If not NULL and not a folder, file will be saved to this path exactly.
xlsx

Boolean. Is it an Excel file? Can be returned as a list for each tab and not as a file if needed. Will delete downloaded file.
token_dir

Character. RDS with token local directory. You may set to NA if you already set your credentials (see get_creds())
token_name

Character. RDS file name with your token's data.
quiet

Boolean. Keep quiet? If not, informative messages will be shown.

Value

If query returns a .xlsx file and xlsx=TRUE, will return a data.frame. Else, local_path string.

See Also

Other Tools: autoline(), bind_files(), bring_api(), cal_split(), chr2num(), db_upload(), dont_sleep(), export_plot(), export_results(), files_functions(), font_exists(), formatColoured(), formatHTML(), glued(), grepm(), h2o_selectmodel(), haveInternet(), image_metadata(), importxlsx(), ip_data(), json2vector(), list_cats(), listfiles(), mail_send(), markdown2df(), move_files(), msplit(), myip(), quiet(), read.file(), statusbar(), tic(), try_require(), updateLares(), warnifnot(), what_size()

Other Credentials: db_upload(), encrypt_file(), get_credentials(), get_tweets(), mail_send(), queryDB(), queryGA(), slackSend(), stocks_file()

Other Dropbox: db_upload()

Examples

## Not run: 
# Download a specific file
db_download("stocksReport.Rmd", local_path = "~/Desktop/generic.Rmd")
# Import an Excel file from Dropbox into a data.frame
df <- db_download("Portfolio LC.xlsx", xlsx = FALSE)

## End(Not run)

Upload Local Files to Dropbox

Description

This function lets the user upload a local file to Dropbox, using a previously created token or with interactive window.

Usage

db_upload(
  filename,
  dir,
  delete_file = FALSE,
  token_dir = NA,
  token_name = "token_pers.rds"
)

Arguments

filename

String. Local file's name to upload.
dir

String. Directory you wish to upload the file to.
delete_file

Boolean. Delete local file after uploading?
token_dir

Character. RDS with token local directory. You may set to NA if you already set your credentials (see get_creds())
token_name

Character. RDS file name with your token's data.

Value

TRUE when successfully uploads file.

See Also

Other Tools: autoline(), bind_files(), bring_api(), cal_split(), chr2num(), db_download(), dont_sleep(), export_plot(), export_results(), files_functions(), font_exists(), formatColoured(), formatHTML(), glued(), grepm(), h2o_selectmodel(), haveInternet(), image_metadata(), importxlsx(), ip_data(), json2vector(), list_cats(), listfiles(), mail_send(), markdown2df(), move_files(), msplit(), myip(), quiet(), read.file(), statusbar(), tic(), try_require(), updateLares(), warnifnot(), what_size()

Other Credentials: db_download(), encrypt_file(), get_credentials(), get_tweets(), mail_send(), queryDB(), queryGA(), slackSend(), stocks_file()

Other Dropbox: db_download()

Dataset columns and rows structure

Description

This function lets the user to check quickly the structure of a dataset (data.frame). It returns multiple counters for useful metrics, a plot, and a list of column names for each of the column metrics.

Usage

df_str(df, return = "plot", subtitle = NA, quiet = FALSE)

Arguments

df

Dataframe
return

Character. Return "skimr" for skim report, "numbers" for stats and numbers, "names" for a list with the column names of each of the class types, "plot" for a nice plot with "numbers" output, "distr" for an overall summary plot showing categorical, numeric, and missing values by using plot_df distributions
subtitle

Character. Add subtitle to plot
quiet

Boolean. Keep quiet or show other options available?

Value

Depending on return input and based on your df structure:

  • list with the names of the columns classified by class

  • data.frame with numbers: total values, row, columns, complete rows

  • plot with visualizations

See Also

Other Exploratory: corr_var(), crosstab(), distr(), freqs(), freqs_df(), freqs_list(), freqs_plot(), lasso_vars(), missingness(), plot_cats(), plot_df(), plot_nums(), tree_var()

Examples

Sys.unsetenv("LARES_FONT") # Temporal
data(dft) # Titanic dataset
df_str(dft, "names")
df_str(dft, "numbers", quiet = TRUE)
df_str(dft, "plot", quiet = TRUE)

Results for AutoML Predictions

Description

List with categorical (2 and 3 classes) and continuous predictions, generated with h2o_automl() and the dft. Note that the models per se won't work to predict.

Usage

data(dfr)

Format

An object of class "list" with 3 "data.frame"

class2

Predictions for a Binomial Classification Model

class3

Predictions for a Multi-Categorical Classification Model

regr

Predictions for a Continuous Regression Model

Value

List

See Also

Other Dataset: dft

Examples

data(dfr)
lapply(dfr, head)

Titanic Dataset

Description

The sinking of the Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the widely considered "unsinkable" RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew. While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others. This dataset contains the details of a subset of the passengers on board (891 to be exact) taken from Kaggle's Titanic Train.csv.

Usage

data(dft)

Format

An object of class "data.frame"

PassengerId

Unique ID for each passenger (1-891)

Survived

Did the passenger survive? (TRUE, FALSE)

Pclass

Ticket class, from first to third (1, 2, 3)

Sex

Gender (female, male)

Age

Age for each passenger in years (0.42-80)

SibSp

Amount of siblings / spouses aboard the Titanic (0-8)

Parch

Amount of parents / children aboard the Titanic (0-6)

Ticket

Ticket IDs

Fare

Amount paid for passenger's ticket (0-512.3292)

Cabin

width of top of diamond relative to widest point (43-95)

Embarked

Port of Embarkation (43-95)

Value

data.frame

See Also

Other Dataset: dfr

Examples

data(dft)
head(dft)

Distance from specific point to line

Description

This function lets the user calculate the mathematical linear distance Between a specific point and a line (given geometrical 3 points)

Usage

dist2d(x, a = c(0, 0), b = c(1, 1))

Arguments

x

Vector. Coordinates of the point from which we want to measure the distance
a

Vector. Coordinates of 1st point over the line
b

Vector. Coordinates of 2st point over the line

Value

Numeric value result

See Also

Other Calculus: corr(), corr_cross(), model_metrics(), quants()

Examples

dist2d(x = c(5, 2))
dist2d(x = c(5, 2), a = c(0, 0), b = c(0, 1))
dist2d(x = c(5, 2), a = c(0, 0), b = c(1, 0))

Compare Variables with their Distributions

Description

Compare the distribution of a target variable vs another variable. This function automatically splits into quantiles for numerical variables. Custom and tidyverse friendly.

Usage

distr(
  data,
  ...,
  type = 1,
  ref = TRUE,
  note = NA,
  top = 10,
  breaks = 10,
  na.rm = FALSE,
  force = "none",
  trim = 0,
  clean = FALSE,
  abc = FALSE,
  custom_colours = FALSE,
  plot = TRUE,
  chords = FALSE,
  save = FALSE,
  subdir = NA
)

Arguments

data

Dataframe
...

Variables. Main (target variable) and secondary (values variable) to group by (if needed).
type

Integer. 1 for both plots, 2 for counter plot only, 3 for percentages plot only.
ref

Boolean. Show a reference line if levels = 2? Quite useful when data is unbalanced (not 50/50) because a reference line is drawn.
note

Character. Caption for the plot.
top

Integer. Filter and plot the most n frequent for categorical values.
breaks

Integer. Number of splits for numerical values.
na.rm

Boolean. Ignore NAs if needed.
force

Character. Force class on the values data. Choose between 'none', 'character', 'numeric', 'date'
trim

Integer. Trim labels until the nth character for categorical values (applies for both, target and values)
clean

Boolean. Use cleanText() for categorical values (applies for both, target and values)
abc

Boolean. Do you wish to sort by alphabetical order?
custom_colours

Boolean. Use custom colours function?
plot

Boolean. Return a plot? Otherwise, a table with results
chords

Boolean. Use a chords plot?
save

Boolean. Save the output plot in our working directory
subdir

Character. Into which subdirectory do you wish to save the plot to?

Value

Plot when plot=TRUE with two plots in one: counter distribution grouped by cuts, and proportions distribution grouped by same cuts. data.frame when plot=FALSE with counting, percentages, and cumulative percentages results. When type argument is used, single plots will be returned.

See Also

Other Exploratory: corr_var(), crosstab(), df_str(), freqs(), freqs_df(), freqs_list(), freqs_plot(), lasso_vars(), missingness(), plot_cats(), plot_df(), plot_nums(), tree_var()

Other Visualization: freqs(), freqs_df(), freqs_list(), freqs_plot(), noPlot(), plot_chord(), plot_survey(), plot_timeline(), tree_var()

Examples

Sys.unsetenv("LARES_FONT") # Temporal
data(dft) # Titanic dataset

# Relation for categorical/categorical values
distr(dft, Survived, Sex)

# Relation for categorical/numeric values
dft %>%
  distr(Survived, Fare, plot = FALSE) %>%
  head(10)
# Sort values
dft %>% distr(Survived, Fare, abc = TRUE)
# Less splits/breaks
dft %>% distr(Survived, Fare, abc = TRUE, breaks = 5)

# Distribution of numerical only
dft[dft$Fare < 20, ] %>% distr(Fare)

# Distribution of numerical/numerical
dft %>% distr(Fare, Age)

# Select only one of the two default plots of distr()
dft %>% distr(Survived, Age, type = 2)
dft %>% distr(Survived, Age, type = 3)

Prevent Computer from Sleeping by Simulating Mouse Activity

Description

Keeps the computer awake by moving the mouse cursor and clicking periodically with a human-like behavior. It automatically detects the operating system (Windows or macOS) and runs the appropriate underlying logic.

Usage

dont_sleep(sec_range = c(20, 60), off_time = 18.5, click = TRUE, quiet = FALSE)

dont_sleep_time(quiet = FALSE)

Arguments

sec_range

Numeric vector of length 2. Range (in seconds) of random intervals between mouse movements and clicks. Default is c(20, 60).
off_time

Numeric. Decimal hour (24h format) to stop the function automatically, e.g. 18.5 means 18:30 (6:30 PM). Default is 18.5.
click

Logical. If TRUE, clicks the mouse. Default is TRUE.
quiet

Logical. If TRUE, suppresses progress messages. Default is FALSE.

Details

The cursor initially moves to the upper-left corner (0,0), then performs small random movements and clicks periodically to avoid system sleep. The function exits if the user moves the mouse away from the corner or when the specified off-time is reached.

Requires cliclick installed on macOS (install via Homebrew: brew install cliclick), and the KeyboardSimulator package on Windows.

Value

Invisibly returns NULL when the function exits.

See Also

Other Tools: autoline(), bind_files(), bring_api(), cal_split(), chr2num(), db_download(), db_upload(), export_plot(), export_results(), files_functions(), font_exists(), formatColoured(), formatHTML(), glued(), grepm(), h2o_selectmodel(), haveInternet(), image_metadata(), importxlsx(), ip_data(), json2vector(), list_cats(), listfiles(), mail_send(), markdown2df(), move_files(), msplit(), myip(), quiet(), read.file(), statusbar(), tic(), try_require(), updateLares(), warnifnot(), what_size()

Examples

## Not run: 
# Keep the computer awake until 8:00 PM, with mouse actions every 30 to 60 seconds
dont_sleep(sec_range = c(30, 60), off_time = 20)
# Summary of amount of daily time running function
dont_sleep_time()

## End(Not run)

File Encryption and Decryption (AES-256-CBC)

Description

This set of functions provides utilities for encrypting and decrypting files using AES-256 in CBC mode, primarily for handling key-value pair "secrets" files.

Usage

encrypt_file(input_file, output_file, key)

read_encrypted(input_file, key)

write_encrypted(data, output_file = "encrypted.enc", key = NULL, quiet = FALSE)

hex_to_raw(x)

raw_to_hex(x)

Arguments

input_file

Character string. The path to the file to be encrypted.
output_file

Character string. The path where the encrypted file will be saved.
key

A raw vector of 32 bytes (256 bits) to be used as the encryption key.
data

List, vector or any json-able object to write into encrypted file.
quiet

Boolean. Keep quiet? If not, informative messages will be shown.
x

Character string or hex vector to convert.

Details

The encryption process pads the data, generates a random Initialization Vector (IV), and encrypts the content. The IV is prepended to the encrypted data in the output file. Decryption involves extracting the IV, decrypting, and unpadding the data. A specialized decryption function read_encrypted is provided to directly parse decrypted content (assumed to be key=value pairs) into a named R list. If no "=" sign found in the first line, text will be imported as character vector.

Value

No return value, called for side effects (writes to output_file).

See Also

Other Credentials: db_download(), db_upload(), get_credentials(), get_tweets(), mail_send(), queryDB(), queryGA(), slackSend(), stocks_file()

Examples

## Not run: 
# Create dummy files for demonstration
temp_input <- tempfile(fileext = ".txt")
temp_output <- tempfile(fileext = ".enc")
writeLines("library=lares \nversion=5.3.0 \n pass=123", temp_input)
readLines(temp_input)

# Generate a random 32-byte key (and save it in a secure place)
raw_key <- openssl::rand_bytes(32)
# You can convert from hex to raw and viceversa
raw_to_hex(raw_key)
hex_to_raw(raw_to_hex(raw_key))

# Encrypt the file
encrypt_file(temp_input, temp_output, raw_key)

# Import the data from encrypted file to list or vector
secrets <- read_encrypted(temp_output, raw_key)
unlist(secrets)

# Example using a string (JSON in this case)
writeLines(jsonlite::toJSON(list(a = 1, list(b = 2, c = 1:3))), temp_input)
encrypt_file(temp_input, temp_output, raw_key)
read_encrypted(temp_output, raw_key)

# Example writing a file from a list or vector directly from R
my_secrets <- list(
  api_key = "some_secret_api_key_123",
  username = "data_analyst",
  server = "production.server.com"
)
raw_key <- write_encrypted(my_secrets, temp_output, quiet = FALSE)
fromJSON(read_encrypted(temp_output, key = raw_key))

## End(Not run)

Calculate Continuous Values Errors

Description

This function lets the user calculate all errors and R squared simultaneously.

This function lets the user calculate Root Mean Squared Error

This function lets the user calculate Mean Absolute Error

This function lets the user calculate Mean Squared Error

This function lets the user calculate Mean Squared Error

This function lets the user calculate R Squared

This function lets the user calculate Adjusted R Squared

Usage

errors(tag, score)

rmse(tag, score)

mae(tag, score)

mse(tag, score)

mape(tag, score)

rsq(tag, score)

rsqa(tag, score)

Arguments

tag

Vector. Real known label
score

Vector. Predicted value or model's result

Value

data.frame or numeric values results for multiple error metrics on continuous numerical vectors inputs.

See Also

Other Model metrics: ROC(), conf_mat(), gain_lift(), loglossBinary(), model_metrics()

Examples

data(dfr) # Results for AutoML Predictions
head(dfr$regr)
df <- errors(dfr$regr$tag, dfr$regr$score)
head(df)

ETF's Sectors Breakdown

Description

etf_sector() scraps etf.com data for sector breakdown on ETFs. Use splot_etf() for visualization.

Usage

etf_sector(etf = "VTI", quiet = FALSE, cache = TRUE)

Arguments

etf

Character Vector. Which ETFs you wish to scrap?
quiet

Boolean. Keep quiet? If not, informative messages will be shown.
cache

Boolean. Use daily cache if available?

Value

data.frame with ETF break.down data by sector

See Also

Other Investment: splot_summary(), stocks_file(), stocks_quote()

Examples

etf_sector(etf = "VTI")

Export ggplot2, gridExtra, or any plot object into rendered file

Description

Export any ggplot2, gridExtra, or any plot object created with R into rendered png or jpg file.

Usage

export_plot(
  p,
  name = "plot",
  vars = NA,
  sep = ".vs.",
  width = 8,
  height = 6,
  format = "png",
  res = 300,
  dir = getwd(),
  subdir = NA,
  quiet = FALSE
)

Arguments

p

Plot object. Plot to render and export.
name

Character. File's name or suffix if vars is not NA. No need to include file format on file name.
vars

Vector. Variable names to identify by filename.
sep

Character. Separator for vars.
width, height, res

Numeric. Plot's width, height, and res (for grids).
format

Character. One of: png or jpeg.
dir, subdir

Character. In which directory/subdirectory do you wish to save the plot? Working directory as default dir.
quiet

Boolean. Display successful message with filename when saved?

Value

No return value, called for side effects.

See Also

Other Tools: autoline(), bind_files(), bring_api(), cal_split(), chr2num(), db_download(), db_upload(), dont_sleep(), export_results(), files_functions(), font_exists(), formatColoured(), formatHTML(), glued(), grepm(), h2o_selectmodel(), haveInternet(), image_metadata(), importxlsx(), ip_data(), json2vector(), list_cats(), listfiles(), mail_send(), markdown2df(), move_files(), msplit(), myip(), quiet(), read.file(), statusbar(), tic(), try_require(), updateLares(), warnifnot(), what_size()

Examples

p <- noPlot()
export_plot(p, name = "noplot", width = 10, height = 8, res = 300, dir = tempdir())
export_plot(p, name = "noplot2", subdir = "newplots", dir = tempdir())

Export h2o_automl's Results

Description

Export RDS, TXT, POJO, MOJO and all results from h2o_automl().

Usage

export_results(
  results,
  thresh = 10,
  which = c("txt", "csv", "rds", "binary", "mojo", "plots", "dev", "production"),
  note = NA,
  subdir = NA,
  save = TRUE,
  seed = 0
)

Arguments

results

h2o_automl or h2o model
thresh

Integer. Threshold for selecting binary or regression models: this number is the threshold of unique values we should have in 'tag' (more than: regression; less than: classification)
which

Character vector. Select which file format to export: Possible values: txt, csv, rds, binary, mojo, plots. You might also use dev (txt, csv, rds) or production (binary, mojo) or simply don't use parameter to export everything
note

Character. Add a note to the txt file. Useful when lots of models are trained and saved to remember which one is which one
subdir

Character. In which directory do you wish to save the results?
save

Boolean. Do you wish to save/export results?
seed

Numeric. For reproducible results and random splits.

Value

No return value, called for side effects.

See Also

Other Machine Learning: ROC(), conf_mat(), gain_lift(), h2o_automl(), h2o_predict_MOJO(), h2o_selectmodel(), impute(), iter_seeds(), lasso_vars(), model_metrics(), model_preprocess(), msplit()

Other Tools: autoline(), bind_files(), bring_api(), cal_split(), chr2num(), db_download(), db_upload(), dont_sleep(), export_plot(), files_functions(), font_exists(), formatColoured(), formatHTML(), glued(), grepm(), h2o_selectmodel(), haveInternet(), image_metadata(), importxlsx(), ip_data(), json2vector(), list_cats(), listfiles(), mail_send(), markdown2df(), move_files(), msplit(), myip(), quiet(), read.file(), statusbar(), tic(), try_require(), updateLares(), warnifnot(), what_size()

Facebook Ad Accounts

Description

This returns all ad accounts for a FB Business Account FB. For more information on Ad Insights' API, go to the original documentaion

Usage

fb_accounts(
  token,
  business_id = "904189322962915",
  type = c("owned", "client"),
  limit = 100,
  api_version = NULL,
  ...
)

Arguments

token

Character. Valid access token with sufficient privileges. Visit the Facebook API Graph Explorer to acquire one.
business_id

Character. Business ID.
type

Character vector. Values: owned, client.
limit

Integer. Query limit by pagination.
api_version

Character. Facebook API version.
...

Additional parameters.

Value

data.frame with un-nested processed results fetched with API.

See Also

Other API: bring_api(), fb_ads(), fb_creatives(), fb_insights(), fb_process(), fb_report_check(), fb_rf(), fb_token(), gemini_ask(), gpt_ask(), queryGA(), slackSend()

Other Meta: fb_ads(), fb_creatives(), fb_insights(), fb_process(), fb_report_check(), fb_rf(), fb_token()

Examples

## Not run: 
# Query all accounts (owned and with permissions) of a Business ID
accounts <- fb_accounts(YOURTOKEN, YOURBUSINESS)

## End(Not run)

Facebook Ads API

Description

This returns all available FB ads for any account, campaign, or ad set id. For more information on Ad' API, go to the original documentaion

Usage

fb_ads(
  token,
  which,
  start_date = Sys.Date() - 31,
  end_date = Sys.Date(),
  fields = NA,
  api_version = NULL,
  process = TRUE,
  ...
)

Arguments

token

Character. Valid access token with sufficient privileges. Visit the Facebook API Graph Explorer to acquire one.
which

Character vector. This is the accounts, campaigns, adsets, or ads IDs to be queried. Remember: if report_level = "account", you must start the ID with act_.
start_date, end_date

Character. The first and last full day to report, in the format "YYYY-MM-DD".
fields

Character, json format. Leave NA for default fields OR NULL to ignore.
api_version

Character. Facebook API version.
process

Boolean. Process GET results to a more friendly format?
...

Additional parameters.

Details

This function was based on FBinsightsR.

Value

data.frame with un-nested processed results if process=TRUE or raw API results as list when process=FALSE.

See Also

Other API: bring_api(), fb_accounts(), fb_creatives(), fb_insights(), fb_process(), fb_report_check(), fb_rf(), fb_token(), gemini_ask(), gpt_ask(), queryGA(), slackSend()

Other Meta: fb_accounts(), fb_creatives(), fb_insights(), fb_process(), fb_report_check(), fb_rf(), fb_token()

Examples

## Not run: 
token <- YOURTOKEN
account <- act_ADACCOUNT

# Query all ads for "which" (account) with results in the last 10 days
ads <- fb_ads(token, account, start_date = Sys.Date() - 10)

## End(Not run)

Facebook Creatives API

Description

For more information: Marketing API

Usage

fb_creatives(token, which, api_version = NULL, process = TRUE, ...)

Arguments

token

Character. Valid access token with sufficient privileges. Visit the Facebook API Graph Explorer to acquire one.
which

Character vector. This is the accounts, campaigns, adsets, or ads IDs to be queried. Remember: if report_level = "account", you must start the ID with act_.
api_version

Character. Facebook API version.
process

Boolean. Process GET results to a more friendly format?
...

Additional parameters.

Value

data.frame with un-nested processed results if process=TRUE or raw API results as list when process=FALSE.

See Also

Other API: bring_api(), fb_accounts(), fb_ads(), fb_insights(), fb_process(), fb_report_check(), fb_rf(), fb_token(), gemini_ask(), gpt_ask(), queryGA(), slackSend()

Other Meta: fb_accounts(), fb_ads(), fb_insights(), fb_process(), fb_report_check(), fb_rf(), fb_token()

Examples

## Not run: 
token <- YOURTOKEN
account <- act_ADACCOUNT

# Query all creatives for "which" (account in this case)
creatives <- fb_creatives(token, account)

## End(Not run)

Facebook Insights API

Description

This returns all available FB insights per day including any given breakdown to the specified report level, and place into a data frame. For more information on Ad Insights' API, go to the original documentaion.

Usage

fb_insights(
  token,
  which,
  start_date = Sys.Date() - 7,
  end_date = Sys.Date(),
  time_increment = "1",
  report_level = "campaign",
  ad_object = "insights",
  breakdowns = NA,
  fields = NA,
  filtering = NULL,
  limit = 100,
  api_version = NULL,
  process = TRUE,
  async = FALSE,
  ...
)

Arguments

token

Character. Valid access token with sufficient privileges. Visit the Facebook API Graph Explorer to acquire one.
which

Character vector. This is the accounts, campaigns, adsets, or ads IDs to be queried. Remember: if report_level = "account", you must start the ID with act_.
start_date, end_date

Character. The first and last full day to report, in the format "YYYY-MM-DD".
time_increment

Character. Group by months ("monthly"), everything together ("all_days") or an integer per days [1-90]. Default: each day separately (i.e. "1").
report_level

Character. One of "ad", "adset", "campaign", or "account"
ad_object

Character. One of: "insights" (default), "adsets", ...
breakdowns

Character Vector. One or more of breakdowns for segmentation results. Set to NA for no breakdowns
fields

Character, json format. Leave NA for default fields OR NULL to ignore.
filtering

List. Each filter will be a list containing "field", "operator", and "value". Read more about the operators in the official docs. Example: dplyr::tibble(field = "country", operator = "IN", value = list("PE"))).
limit

Integer. Query limit by pagination.
api_version

Character. Facebook API version.
process

Boolean. Process GET results to a more friendly format?
async

Boolean. Run an async query. When set to TRUE, instead of making a GET query, it'll run a POST query and will return a report run ID.
...

Additional parameters.

Value

data.frame with un-nested processed results if process=TRUE or raw API results as list when process=FALSE.

See Also

Other API: bring_api(), fb_accounts(), fb_ads(), fb_creatives(), fb_process(), fb_report_check(), fb_rf(), fb_token(), gemini_ask(), gpt_ask(), queryGA(), slackSend()

Other Meta: fb_accounts(), fb_ads(), fb_creatives(), fb_process(), fb_report_check(), fb_rf(), fb_token()

Examples

## Not run: 
token <- "YOURTOKEN"
which <- "act_20846447"

# Platforms' Insights: all ad-sets platforms of "which" account,
# aggregated, for the last 30 days
platforms <- fb_insights(
  token, which,
  start_date = Sys.Date() - 30,
  time_increment = "all_days",
  report_level = "adset",
  fields = c(
    "account_name",
    "adset_id",
    "adset_start",
    "adset_end"
  ),
  breakdowns = c(
    "publisher_platform",
    "platform_position",
    "device_platform"
  )
)

# Daily results for all campaigns of "which" account,
# with custom performance fields with no breakdowns.
insights_adset <- fb_insights(
  token, which,
  time_increment = "1",
  report_level = "campaign",
  fields = c(
    "adset_id",
    "reach",
    "frequency",
    "spend",
    "cpm",
    "objective",
    "optimization_goal"
  )
)

## End(Not run)

Paginate and Process Facebook's API Results

Description

Process and paginate raw results from Facebook's API, result of querying the API with httr::GET or by passing an API link.

Usage

fb_process(input, paginate = TRUE, sleep = 0, quiet = FALSE, ...)

Arguments

input

GET's output object (response) or link (character).
paginate

Boolean or integer. Run through all paginations? If set to FALSE, only the first one will be processed. If set to any other integer value, will process the first N paginations.
sleep

Numeric value. How much should each loop wait until until running the next pagination query?
quiet

Boolean. Keep quiet? If not, informative messages will be shown.
...

Additional parameters.

Value

data.frame with un-nested processed results or NULL if no results found.

See Also

Other API: bring_api(), fb_accounts(), fb_ads(), fb_creatives(), fb_insights(), fb_report_check(), fb_rf(), fb_token(), gemini_ask(), gpt_ask(), queryGA(), slackSend()

Other Meta: fb_accounts(), fb_ads(), fb_creatives(), fb_insights(), fb_report_check(), fb_rf(), fb_token()

Facebook API Report Status Check

Description

This returns all available FB insights per day including any given breakdown to the specified report level, and place into a data frame. For more information on Ad Insights' API, go to the original documentaion.

Usage

fb_report_check(
  token,
  report_run_id,
  api_version = NULL,
  live = FALSE,
  sleep = 10,
  quiet = FALSE
)

Arguments

token

Character. Valid access token with sufficient privileges. Visit the Facebook API Graph Explorer to acquire one.
report_run_id

Integer. Report ID to check status.
api_version

Character. Facebook API version.
live

Boolean. Run until status report is finished?
sleep

Boolean. If live=TRUE, then how many seconds should we wait until next check?
quiet

Boolean. Keep quiet? If not, informative messages will be shown.

Value

List with API status results.

See Also

Other API: bring_api(), fb_accounts(), fb_ads(), fb_creatives(), fb_insights(), fb_process(), fb_rf(), fb_token(), gemini_ask(), gpt_ask(), queryGA(), slackSend()

Other Meta: fb_accounts(), fb_ads(), fb_creatives(), fb_insights(), fb_process(), fb_rf(), fb_token()

Examples

## Not run: 
token <- "YOURTOKEN"
report_run_id <- "123456789"
fb_report_check(token, report_run_id, live = TRUE, quiet = FALSE)

## End(Not run)

Facebook Reach and Frequency API

Description

Create or query reach and frequency predictions using Facebook's Reach and Frequency API. For more information on the API and its parameters, go to the original documentaion.

Usage

fb_rf(
  token,
  ad_account = NA,
  prediction = NA,
  objective = "REACH",
  days = 28,
  budget = 2e+06,
  destination_ids = NA,
  countries = "MX",
  frequency_cap = 8,
  prediction_mode = 1,
  curve = TRUE,
  api_version = NULL,
  process = TRUE,
  ...
)

Arguments

token

Character. Valid access token with sufficient privileges. Visit the Facebook API Graph Explorer to acquire one.
ad_account

Character. Ad Account. Remember to start with act_. If you use the prediction argument, no need to provide this parameter.
prediction

Integer. Prediction ID if you already created the prediction and wish to query the curve's data. As this prediction already exists, the rest of arguments of this function will be ignored.
objective

Character. Any of: "BRAND_AWARENESS", "LINK_CLICKS", "POST_ENGAGEMENT", "MOBILE_APP_INSTALLS", "CONVERSIONS", "REACH", or "VIDEO_VIEWS".
days

Integer. Amount of days for your campaign's predictions.
budget

Integer. The budget in the Ad Account currency in cents.
destination_ids

Integer vector. Page ID and/or Instagram Account ID.
countries

Character vector. Country's acronyms.
frequency_cap

Integer. Frequency cap over all the campaign duration.
prediction_mode

Integer. "1" for predicting Reach by providing budget, "2" is for predicting Budget given a specific Reach.
curve

Boolean. Return curve data? If not, only prediction will be created.
api_version

Character. Facebook API version.
process

Boolean. Process GET results to a more friendly format?
...

Additional parameters passed to target specs.

Value

data.frame with un-nested processed results if process=TRUE or raw API results as list when process=FALSE.

See Also

Other API: bring_api(), fb_accounts(), fb_ads(), fb_creatives(), fb_insights(), fb_process(), fb_report_check(), fb_token(), gemini_ask(), gpt_ask(), queryGA(), slackSend()

Other Meta: fb_accounts(), fb_ads(), fb_creatives(), fb_insights(), fb_process(), fb_report_check(), fb_token()

Examples

## Not run: 
token <- "YOURTOKEN"
account_id <- "act_20846447"

# BASIC 1: Create and return data for a new prediction
basic1 <- fb_rf(token, account_id, destination_ids = 187071108930, countries = "AR")

# BASIC 2: Fetch data for an existing prediction ID
basic2 <- fb_rf(token, account_id, prediction = 6317720998974)

# ADVANCED (Fully custom prediction)
advanced <- fb_rf(token, account_id,
  objective = "REACH",
  days = 28,
  budget = 2000000,
  destination_ids = c(187071108930, 1142958119078556),
  age_min = 15,
  age_max = 65,
  genders = 2,
  countries = "MX",
  publisher_platforms = c(
    "facebook",
    "instagram",
    #' audience_network',
    "messenger"
  ),
  # interests_ids = NA,
  facebook_positions = c(
    "feed",
    #' instant_article',
    "marketplace",
    "video_feeds",
    "story",
    "search",
    "instream_video"
  ),
  instagram_positions = c(
    "stream",
    "story",
    "explore"
  ),
  # audience_network_positions = c(
  #  'classic',
  #  'instream_video')
  messenger_positions = c(
    "messenger_home",
    "sponsored_messages",
    "story"
  ),
  device_platforms = c(
    "mobile",
    "desktop"
  )
)

## End(Not run)

Facebook's Long-Life User API Token

Description

Using a 1-hour generic user token you can generate a 60 day token. You will need to have an App ID and App secret, and a valid token. Generate a new valid User Token with the API Graph.

Usage

fb_token(app_id, app_secret, token, api_version = NULL)

Arguments

app_id, app_secret

Character. Application ID and Secret.
token

Character. User token, created with API Graph or with this same fb_token()'s token.
api_version

Character. Facebook API version.

Details

More info: Long-Lived Access Tokens

Value

Character. String with token requested. If successful, it'll contain an attribute called "expiration" with date and time of expiration.

See Also

Other API: bring_api(), fb_accounts(), fb_ads(), fb_creatives(), fb_insights(), fb_process(), fb_report_check(), fb_rf(), gemini_ask(), gpt_ask(), queryGA(), slackSend()

Other Meta: fb_accounts(), fb_ads(), fb_creatives(), fb_insights(), fb_process(), fb_report_check(), fb_rf()

Extract file raw name and type from file names

Description

Extract file raw name and type from file names

Get file extensions without file names

Usage

file_name(filepath)

file_type(filepath)

Arguments

filepath

Character vector. File path(s) to get file raw names without extension nor path OR extension without path nor raw name.

See Also

Other Data Wrangling: balance_data(), categ_reducer(), cleanText(), date_cuts(), date_feats(), formatHTML(), holidays(), impute(), left(), normalize(), num_abbr(), ohe_commas(), ohse(), quants(), removenacols(), replaceall(), replacefactor(), textFeats(), textTokenizer(), vector2text(), year_month(), zerovar()

Other Data Wrangling: balance_data(), categ_reducer(), cleanText(), date_cuts(), date_feats(), formatHTML(), holidays(), impute(), left(), normalize(), num_abbr(), ohe_commas(), ohse(), quants(), removenacols(), replaceall(), replacefactor(), textFeats(), textTokenizer(), vector2text(), year_month(), zerovar()

Examples

file_name("file.aux")
file_name("temp/file.R")
file_name("/temp/temp3/music.mp3")
file_type("file.aux")
file_type("temp/file.R")
file_type("/temp/temp3/music.mp3")

List all functions used in R script files by package

Description

Parses all functions called by an R script and then lists them by package. Wrapper for 'getParseData'. May be of great use for those developing a package to help see what namespace 'importsFrom' calls will be required.

Usage

files_functions(filename, abc = TRUE, quiet = FALSE)

Arguments

filename

Character. Path to an R file (or directory) containing R code files.
abc

Boolean. List functions alphabetically. If FALSE, will list in order of frequency.
quiet

Boolean. Keep quiet? If not, print messages and statusbar.

Value

data.frame. Each row is a function and columns stating number of appearances, percentage, packages, and files searched.

See Also

Other Tools: autoline(), bind_files(), bring_api(), cal_split(), chr2num(), db_download(), db_upload(), dont_sleep(), export_plot(), export_results(), font_exists(), formatColoured(), formatHTML(), glued(), grepm(), h2o_selectmodel(), haveInternet(), image_metadata(), importxlsx(), ip_data(), json2vector(), list_cats(), listfiles(), mail_send(), markdown2df(), move_files(), msplit(), myip(), quiet(), read.file(), statusbar(), tic(), try_require(), updateLares(), warnifnot(), what_size()

Examples

## Not run: 
# Choose an R script file with functions
rfile <- file.choose()
files_functions(rfile)

## End(Not run)

Google Drive Files (API v4)

Description

Authenticate and find Google Drive files and IDs by name.

Usage

filesGD(title, server = FALSE, json = NULL, api_key = NULL, email = NULL)

Arguments

title

Character. Title of Google Drive file. Uses regular expressions so you may fetch with patterns instead of names.
server

Boolean. Force interacting auth process?
json

Character. JSON filename with service auth
email, api_key

Character. If you have multiple pre-authorized accounts in your machine, you may non-interactively select which one you wish to use by email and/or api_key.

Value

Vector with found file names based on title on Google Drive.

See Also

Other Scrapper: gtrends_related(), holidays(), ip_data(), mp3_get(), readGS(), splot_summary(), stocks_quote()

Other Google: gtrends_related(), queryGA(), readGS()

Check if Font is Installed

Description

This function checks if a font is installed in your machine. To list all available fonts, set font = NULL.

Usage

font_exists(font = "Arial Narrow", font_dirs = NULL, quiet = FALSE, ...)

Arguments

font

Character. Which font to check. No need to add .TFF.
font_dirs

Character vector. Additional directories to check for fonts.
quiet

Boolean. Keep quiet? If not, show message
...

Additional parameters.

Value

Boolean result of the existing fonts check.

See Also

Other Tools: autoline(), bind_files(), bring_api(), cal_split(), chr2num(), db_download(), db_upload(), dont_sleep(), export_plot(), export_results(), files_functions(), formatColoured(), formatHTML(), glued(), grepm(), h2o_selectmodel(), haveInternet(), image_metadata(), importxlsx(), ip_data(), json2vector(), list_cats(), listfiles(), mail_send(), markdown2df(), move_files(), msplit(), myip(), quiet(), read.file(), statusbar(), tic(), try_require(), updateLares(), warnifnot(), what_size()

Examples

font_exists(font = "Arial")
font_exists(font = "arial")
font_exists(font = "")
font_exists(font = NULL)

ARIMA Forecast

Description

This function automates the ARIMA iterations and modeling for time forecasting. For the moment, units can only be days.

Usage

forecast_arima(
  time,
  values,
  n_future = 30,
  ARMA = 8,
  ARMA_min = 5,
  AR = NA,
  MA = NA,
  wd_excluded = NA,
  plot = TRUE,
  plot_days = 90,
  project = NA
)

Arguments

time

POSIX. Vector with date values
values

Numeric. Vector with numerical values
n_future

Integer. How many steps do you wish to forecast?
ARMA

Integer. How many days should the model look back for ARMA? Between 5 and 10 days recommmended. If set to 0 then it will forecast until the end of max date's month; if set to -1, until the end of max date's following month
ARMA_min

Integer. How many days should the model look back for ARMA? Between 5 and 10 days recommmended. If set to 0 then it will forecast until the end of max date's month; if set to -1, until the end of max date's following month
AR

Integer. Force AR value if known
MA

Integer. Force MA value if known
wd_excluded

Character vector. Which weekdays are excluded in your training set. If there are, please define know which ones. Example: c('Sunday','Thursday'). If set to 'auto' then it will detect automatically which weekdays have no data and forcast without these days.
plot

Boolean. If you wish to plot your results
plot_days

Integer. How many days back you wish to plot?
project

Character. Name of your forecast project

Details

The ARIMA method is appropriate only for a time series that is stationary (i.e., its mean, variance, and autocorrelation should be approximately constant through time) and it is recommended that there are at least 50 observations in the input data.

The model consists of two parts, an autoregressive (AR) part and a moving average (MA) part. The AR part involves regressing the variable on its own lagged (i.e., past) values. The MA part involves modeling the error term as a linear combination of error terms occurring contemporaneously and at various times in the past.

One thing to keep in mind when we think about ARIMA models is given by the great power to capture very complex patters of temporal correlation (Cochrane, 1997: 25)

Value

List. Containing the trained model, forecast accuracy results, data.frame for forecast (test) and train, and if plot=TRUE, a plot.

See Also

Other Forecast: prophesize()

Print Coloured Messages

Description

Print Coloured Messages

Usage

formatColoured(
  txt,
  colour = c("yellow", "blue", "grey"),
  bold = FALSE,
  cat = TRUE
)

Arguments

txt

Character. Text to print or transform.
colour

Character. Any of: grey, red, green, yellow, blue, or purple.
bold

Boolean. Set bold text?
cat

Boolean. Print with cat? If not, raw string

Value

Depends on cat: NULL if TRUE or character string if FALSE.

See Also

Other Tools: autoline(), bind_files(), bring_api(), cal_split(), chr2num(), db_download(), db_upload(), dont_sleep(), export_plot(), export_results(), files_functions(), font_exists(), formatHTML(), glued(), grepm(), h2o_selectmodel(), haveInternet(), image_metadata(), importxlsx(), ip_data(), json2vector(), list_cats(), listfiles(), mail_send(), markdown2df(), move_files(), msplit(), myip(), quiet(), read.file(), statusbar(), tic(), try_require(), updateLares(), warnifnot(), what_size()

Examples

opts <- c("GREY", "RED", "GREEN", "YELLOW", "BLUE", "PURPLE")
for (colour in opts) formatColoured(paste("Colour:", colour, "\n"), colour)
formatColoured("my bold coloured text", bold = TRUE, cat = TRUE)

Format a string text as markdown/HTML

Description

Format any character string to HTML or markdown format. We recommend using this format with the ggtext::geom_richtext function to format text in ggplot2 objects.

This function lets the user format numerical values nicely

Usage

formatHTML(text, color = "black", size = 20, bold = FALSE)

formatNum(
  x,
  decimals = 2,
  signif = NULL,
  type = Sys.getenv("LARES_NUMFORMAT"),
  pre = "",
  pos = "",
  sign = FALSE,
  abbr = FALSE,
  ...
)

Arguments

text

Character. Strings to format.
color

Character. Hex colour code.
size

Numeric. Text size.
bold

Boolean. Should the text be bold?
x

Numerical Vector
decimals

Integer. Amount of decimals to display. If set to NULL, then getOption("digits") will be used.
signif

Integer. Rounds the values in its first argument to the specified number of significant digits.
type

Integer. 1 for International standards. 2 for American Standards. Use Sys.setenv("LARES_NUMFORMAT" = 2) to set this parameter globally.
pre, pos

Character. Add string before or after number.
sign

Boolean. Add + sign to positive values.
abbr

Boolean. Abbreviate using num_abbr()? You can use the ‘decimals' parameter to set abbr’s n(-1) parameter.
...

Additional lazy eval parameters.

Value

String with format characters included.

Character. String vector with reformatted continuous numbers

See Also

Other Tools: autoline(), bind_files(), bring_api(), cal_split(), chr2num(), db_download(), db_upload(), dont_sleep(), export_plot(), export_results(), files_functions(), font_exists(), formatColoured(), glued(), grepm(), h2o_selectmodel(), haveInternet(), image_metadata(), importxlsx(), ip_data(), json2vector(), list_cats(), listfiles(), mail_send(), markdown2df(), move_files(), msplit(), myip(), quiet(), read.file(), statusbar(), tic(), try_require(), updateLares(), warnifnot(), what_size()

Other Data Wrangling: balance_data(), categ_reducer(), cleanText(), date_cuts(), date_feats(), file_name(), holidays(), impute(), left(), normalize(), num_abbr(), ohe_commas(), ohse(), quants(), removenacols(), replaceall(), replacefactor(), textFeats(), textTokenizer(), vector2text(), year_month(), zerovar()

Examples

formatHTML("Text test", color = "#000000")
formatHTML(c(123, 456), color = "orange", size = 120, bold = TRUE)

# If you want to use it with \code{ggtext}:
## Not run: 
col1 <- "grey"
col2 <- "orange"
pt <- data.frame(
  label = paste0(
    formatHTML(123, color = col2, size = 120, bold = TRUE), "<br/>",
    formatHTML("of children had a", col1), "<br/>",
    formatHTML("traditional stay-at-home mom", color = col2, bold = TRUE), "<br/>",
    formatHTML(paste0("in 2012, compared to ", 321, " in 1970"), color = col1)
  )
)
ggplot(pt, aes(x = 0, y = 0)) +
  ggtext::geom_richtext(
    aes(label = label),
    hjust = 0,
    label.color = NA,
    lineheight = 1.5
  ) +
  xlim(0, 0.01) +
  theme_void()

## End(Not run)
formatNum(1.23456, decimals = 3)
formatNum(1.23456, type = 1)
formatNum(1.23456, pre = "$", pos = "/person")
formatNum(123456, abbr = TRUE)
formatNum(c(123123, 123.123, 0.123123), signif = 2)
formatNum(1234567890, abbr = TRUE, signif = 3)
formatNum(1234567890, decimals = 0, abbr = TRUE)
formatNum(c(-3:3), sign = TRUE)

Frequencies Calculations and Plot

Description

This function lets the user group, count, calculate percentages and cumulatives. It also plots results if needed. Tidyverse friendly.

Usage

freqs(
  df,
  ...,
  wt = NULL,
  rel = FALSE,
  results = TRUE,
  variable_name = NA,
  plot = FALSE,
  rm.na = FALSE,
  title = NA,
  subtitle = NA,
  top = 20,
  abc = FALSE,
  save = FALSE,
  subdir = NA,
  quiet = FALSE
)

Arguments

df

Data.frame
...

Variables. Variables you wish to process. Order matters. If no variables are passed, the whole data.frame will be considered
wt

Variable, numeric. Weights.
rel

Boolean. Relative percentages (or absolute)?
results

Boolean. Return results in a dataframe?
variable_name

Character. Overwrite the main variable's name
plot

Boolean. Do you want to see a plot? Three variables tops.
rm.na

Boolean. Remove NA values in the plot? (not filtered for numerical output; use na.omit() or filter() if needed)
title

Character. Overwrite plot's title with.
subtitle

Character. Overwrite plot's subtitle with.
top

Integer. Filter and plot the most n frequent for categorical values. Set to NA to return all values
abc

Boolean. Do you wish to sort by alphabetical order?
save

Boolean. Save the output plot in our working directory
subdir

Character. Into which subdirectory do you wish to save the plot to?
quiet

Boolean. Keep quiet? If not, informative messages will be shown.

Value

Plot when plot=TRUE and data.frame with grouped frequency results when plot=FALSE.

See Also

Other Frequency: freqs_df(), freqs_list(), freqs_plot()

Other Exploratory: corr_var(), crosstab(), df_str(), distr(), freqs_df(), freqs_list(), freqs_plot(), lasso_vars(), missingness(), plot_cats(), plot_df(), plot_nums(), tree_var()

Other Visualization: distr(), freqs_df(), freqs_list(), freqs_plot(), noPlot(), plot_chord(), plot_survey(), plot_timeline(), tree_var()

Examples

Sys.unsetenv("LARES_FONT") # Temporal
data(dft) # Titanic dataset

# How many survived?
dft %>% freqs(Survived)

# How many survived per Class?
dft %>% freqs(Pclass, Survived, abc = TRUE)

# How many survived per Class with relative percentages?
dft %>% freqs(Pclass, Survived, abc = TRUE, rel = TRUE)

# Using a weighted feature
dft %>% freqs(Pclass, Survived, wt = Fare / 100)

### Let's check the results with plots:

# How many survived and see plot?
dft %>% freqs(Survived, plot = TRUE)

# How many survived per class?
dft %>% freqs(Survived, Pclass, plot = TRUE)

# Per class, how many survived?
dft %>% freqs(Pclass, Survived, plot = TRUE)

# Per sex and class, how many survived?
dft %>% freqs(Sex, Pclass, Survived, plot = TRUE)

# Frequency of tickets + Survived
dft %>% freqs(Survived, Ticket, plot = TRUE)

# Frequency of tickets: top 10 only and order them alphabetically
dft %>% freqs(Ticket, plot = TRUE, top = 10, abc = TRUE)

Plot for All Frequencies on Dataframe

Description

This function lets the user analize data by visualizing the frequency of each value of each column from a whole data frame.

Usage

freqs_df(
  df,
  max = 0.9,
  min = 0,
  novar = TRUE,
  plot = FALSE,
  top = 30,
  quiet = FALSE,
  save = FALSE,
  subdir = NA
)

Arguments

df

Data.frame
max

Numeric. Top variance threshold. Range: (0-1]. These variables will be excluded
min

Numeric. Minimum variance threshold. Range: [0-1). These values will be grouped into a high frequency (HF) value
novar

Boolean. Remove no variance columns?
plot

Boolean. Do you want to see a plot? Three variables tops
top

Integer. Plot most relevant (less categories) variables
quiet

Boolean. Keep quiet? If not, informative messages will be shown.
save

Boolean. Save the output plot in our working directory
subdir

Character. Into which subdirectory do you wish to save the plot to?

Value

Plot when plot=TRUE and data.frame with grouped frequency results when plot=FALSE.

See Also

Other Frequency: freqs(), freqs_list(), freqs_plot()

Other Exploratory: corr_var(), crosstab(), df_str(), distr(), freqs(), freqs_list(), freqs_plot(), lasso_vars(), missingness(), plot_cats(), plot_df(), plot_nums(), tree_var()

Other Visualization: distr(), freqs(), freqs_list(), freqs_plot(), noPlot(), plot_chord(), plot_survey(), plot_timeline(), tree_var()

Examples

data(dft) # Titanic dataset
freqs_df(dft)
freqs_df(dft, plot = TRUE)

Frequencies on Lists and UpSet Plot

Description

Visualize frequency of elements on a list, list vector, or vector with comma separated values. Detect which combinations and elements are the most frequent and how much they represent of your total observations. This is similar to the UpSet Plots which may be used as an alternative to Venn diagrams.

Usage

freqs_list(
  df,
  var = NULL,
  wt = NULL,
  fx = "mean",
  rm.na = FALSE,
  min_elements = 1,
  limit = 10,
  limit_x = NA,
  limit_y = NA,
  tail = TRUE,
  size = 10,
  unique = TRUE,
  abc = FALSE,
  title = "",
  plot = TRUE
)

Arguments

df

Data.frame
var

Variable. Variables you wish to process.
wt

Variable, numeric. Select a numeric column to use in the colour scale, used as sum, mean... of those values for each of the combinations.
fx

Character. Set operation: mean, sum
rm.na

Boolean. Remove NA value from wt?
min_elements

Integer. Exclude combinations with less than n elements
limit, limit_x, limit_y

Integer. Show top n combinations (x) and/or elements (y). The rest will be grouped into a single element. Set argument to 0 to ignore. limit_x/limit_y answer to limit's argument.
tail

Boolean. Show tail grouped into "..." on the plots?
size

Numeric. Text base size
unique

Boolean. a,b = b,a?
abc

Boolean. Do you wish to sort by alphabetical order?
title

Character. Overwrite plot's title with.
plot

Boolean. Plot viz? Will be generated anyways in the output object

Value

List. data.frame with the data results, elements and combinations.

See Also

Other Frequency: freqs(), freqs_df(), freqs_plot()

Other Exploratory: corr_var(), crosstab(), df_str(), distr(), freqs(), freqs_df(), freqs_plot(), lasso_vars(), missingness(), plot_cats(), plot_df(), plot_nums(), tree_var()

Other Visualization: distr(), freqs(), freqs_df(), freqs_plot(), noPlot(), plot_chord(), plot_survey(), plot_timeline(), tree_var()

Examples

## Not run: 
df <- dplyr::starwars
head(df[, c(1, 4, 5, 12)], 10)

# Characters per movies combinations in a list column
head(df$films, 2)
freqs_list(df, films)

# Skin colours in a comma-separated column
head(df$skin_color)
x <- freqs_list(df, skin_color, min_elements = 2, limit = 5, plot = FALSE)
# Inside "x" we'll have:
names(x)

# Using the 'wt' argument to add a continuous value metric
# into an already one-hot encoded columns dataset (and hide tail)
csv <- "https://raw.githubusercontent.com/hms-dbmi/UpSetR/master/inst/extdata/movies.csv"
movies <- read.csv(csv, sep = ";")
head(movies)
freqs_list(movies,
  wt = AvgRating, min_elements = 2, tail = FALSE,
  title = "Movies\nMixed Genres\nRanking"
)
# So, please: no more Comedy+SciFi and more Drama+Horror films (based on ~50 movies)!

## End(Not run)

Combined Frequencies Plot for Categorical Features

Description

Plot frequencies of multiple categories within a data.frame in a new fancy way. Tidyverse friendly, based on lares::freqs(), no limits on amount of features to evaluate.

Usage

freqs_plot(
  df,
  ...,
  top = 10,
  rm.na = FALSE,
  abc = FALSE,
  title = NA,
  subtitle = NA,
  quiet = FALSE
)

Arguments

df

Data.frame
...

Variables. Variables you wish to process. Order matters. If no variables are passed, the whole data.frame will be considered
top

Integer. Filter and plot the most n frequent for categorical values. Set to NA to return all values
rm.na

Boolean. Remove NA values in the plot? (not filtered for numerical output; use na.omit() or filter() if needed)
abc

Boolean. Do you wish to sort by alphabetical order?
title

Character. Overwrite plot's title with.
subtitle

Character. Overwrite plot's subtitle with.
quiet

Boolean. Keep quiet? If not, informative messages will be shown.

Value

Plot. Result of the frequency of combined variables.

See Also

Other Frequency: freqs(), freqs_df(), freqs_list()

Other Exploratory: corr_var(), crosstab(), df_str(), distr(), freqs(), freqs_df(), freqs_list(), lasso_vars(), missingness(), plot_cats(), plot_df(), plot_nums(), tree_var()

Other Visualization: distr(), freqs(), freqs_df(), freqs_list(), noPlot(), plot_chord(), plot_survey(), plot_timeline(), tree_var()

Examples

Sys.unsetenv("LARES_FONT") # Temporal
data(dft) # Titanic dataset

x <- freqs_plot(dft, Pclass, Survived)
x$data
plot(x)

freqs_plot(dft, Pclass, Survived, Sex, Embarked)

freqs_plot(dft, Pclass, Survived, Sex, Embarked, top = 15)

Cumulative Gain, Lift and Response

Description

This function calculates cumulative gain, lift, and response values for a predictive score of a specific target. You can use the mplot_gain() function to create a plot.

Usage

gain_lift(
  tag,
  score,
  target = "auto",
  splits = 10,
  plot = FALSE,
  quiet = FALSE
)

Arguments

tag

Vector. Real known label
score

Vector. Predicted value or model's result
target

Value. Which is your target positive value? If set to 'auto', the target with largest mean(score) will be selected. Change the value to overwrite. Only used when binary categorical model.
splits

Integer. Number of percentiles to split the data
plot

Boolean. Plot results? Uses mplot_gain()
quiet

Boolean. Keep quiet? If not, informative messages will be shown.

Value

data.frame when plot=FALSE or plot when plot=TRUE.

See Also

Other Machine Learning: ROC(), conf_mat(), export_results(), h2o_automl(), h2o_predict_MOJO(), h2o_selectmodel(), impute(), iter_seeds(), lasso_vars(), model_metrics(), model_preprocess(), msplit()

Other Model metrics: ROC(), conf_mat(), errors(), loglossBinary(), model_metrics()

Examples

data(dfr) # Results for AutoML Predictions
head(dfr$class2)

# Results for Binomial Model
gain_lift(dfr$class2$tag, dfr$class2$scores, target = "FALSE")
gain_lift(dfr$class2$tag, dfr$class2$scores, target = "TRUE", splits = 5)

Gemini API Interaction with R

Description

This function lets the user interact with Google's Gemini LLM Model using its API, and returns the rendered reply.

Usage

gemini_ask(
  ask,
  secret_key = get_creds("gemini")$api_key,
  url = Sys.getenv("LARES_GEMINI_API"),
  temperature = 0.5,
  max_tokens = 1024,
  quiet = FALSE,
  ...
)

gemini_image(
  ask,
  image,
  secret_key = get_creds("gemini")$api_key,
  url = Sys.getenv("LARES_GEMINI_API"),
  temperature = 0.5,
  max_tokens = 1024,
  quiet = FALSE,
  ...
)

Arguments

ask

Character. Redacted prompt to ask. If multiple asks are requested, they will be concatenated with "+" into a single request.
secret_key

Character. Secret Key. Get yours in: platform.openai.com for OpenAI or makersuite.google.com/app/apikey for Gemini.
url

Character. Base API URL.
temperature

Numeric. The temperature to use for generating the response. Default is 0.5. The lower the temperature, the more deterministic the results in the sense that the highest probable next token is always picked. Increasing temperature could lead to more randomness, which encourages more diverse or creative outputs. You are essentially increasing the weights of the other possible tokens. In terms of application, you might want to use a lower temperature value for tasks like fact-based QA to encourage more factual and concise responses. For poem generation or other creative tasks, it might be beneficial to increase the temperature value.
max_tokens

Integer. The maximum number of tokens in the response.
quiet

Boolean. Keep quiet? If not, informative messages will be shown.
...

Additional parameters.
image

Character. Data to be encoded/decoded. It can be a raw vector, text connection or file name.

Value

(Invisible) list. Content returned from API POST and processed.

See Also

Other API: bring_api(), fb_accounts(), fb_ads(), fb_creatives(), fb_insights(), fb_process(), fb_report_check(), fb_rf(), fb_token(), gpt_ask(), queryGA(), slackSend()

Other LLM: gpt_ask(), gpt_prompter()

Examples

## Not run: 
api_key <- get_credentials()$gemini$api_key
# Open question:
gemini_ask("Can you write an R function to plot a dummy histogram?", api_key)
# Image question
image <- "man/figures/automl_map.png"
gemini_image("Can you explain this flow with more detail?", image, api_key)

## End(Not run)

Load Credentials from a YML File

Description

Load credentials from a local YML file. You can set your .Renviron and the LARES_CREDS parameter to remember (forever) the directory of your credentials' file. To use it later, you may leave dir = NA. You may also use this function for external (non-lares) code/use.

Usage

get_credentials(
  from = NA,
  dir = NA,
  filename = "config.yml",
  env = "LARES_CREDS",
  ...
)

get_creds(
  from = NA,
  dir = NA,
  filename = "config.yml",
  env = "LARES_CREDS",
  ...
)

Arguments

from

Character. Family of values to import from the YML file. If you don't know these names, set from = NA and a warning will display all possible values, depending on your YML file.
dir

Character. Credentials directory where your YML file is. If used frequently, set your directory by using the .Renviron file. To do so, leave dir as NA and follow the steps. If dir is a list, it'll return dir (manual credentials input). If dir ends with .yml or .yaml, filename will be ignored.
filename

Character. YML filename with your credentials. Ignored if dir contains the file path.
env

Character. Environment variable name. No need to set differently for any function that uses this library. Only for external use.
...

Additional parameters.

Value

List. Result of reading your credential's YML file, filtered by your from input if provided.

Set the default directory

The first time you use any function that has the creds parameter, if the dir parameter is set to NA, this function will ask you to set the directory where you save your YML local file with your credentials. This will be asked once and will be set for further R sessions. Remember to reset your session for this setup to start working properly.

YML file format

A YML file is a text file, with .yml file format. You may start from the dummy YML file shared which shows the structure you must follow to set your credentials file. Check it out here or find it locally using system.file("docs", "config.yml", package = "lares").

See Also

Other Credentials: db_download(), db_upload(), encrypt_file(), get_tweets(), mail_send(), queryDB(), queryGA(), slackSend(), stocks_file()

Examples

## Not run: 
# Load dummy config.yml file from the library
# Recommendation: set dir with NA (read documentation)
# We need the directory, not the file
yml <- dirname(system.file("docs", "config.yml", package = "lares"))

# Let's see which credentials we have in our file
get_credentials(dir = yml)
# Warning message: No credentials for NA found in your YML file.
# Try any of the following: 'service1', 'service2', 'service3'

# Get credentials for service2
get_credentials("service2", dir = yml)

## End(Not run)

Download Historical Currency Exchange Rate

Description

This function lets the user download historical currency exchange rate between two currencies.

Usage

get_currency(
  currency_pair,
  from = Sys.Date() - 99,
  to = Sys.Date(),
  fill = FALSE,
  ...
)

Arguments

currency_pair

Character. Which currency exchange do you wish to get the history from? i.e, USD/COP, EUR/USD...
from

Date. From date
to

Date. To date
fill

Boolean. Fill weekends and non-quoted dates with previous values?
...

Additional parameters.

Value

data.frame. Result of fetching online data for currency_pair grouped by date.

Examples

# For today (or any one single date)
get_currency("USD/COP", from = Sys.Date())
# For multiple dates
get_currency("EUR/USD", from = Sys.Date() - 7, fill = TRUE)

Get Tweets

Description

This function downloads tweets with personal credentials

Usage

get_tweets(q, n = 10000, creds = NA)

Arguments

q

Query. Check for ?rtweet::search_tweets()
n

Integer. Total of tweets to return
creds

Character. Credential's user (see get_creds())

Value

data.frame with API response results.

See Also

Other Credentials: db_download(), db_upload(), encrypt_file(), get_credentials(), mail_send(), queryDB(), queryGA(), slackSend(), stocks_file()

Custom fill, colour and text colours for ggplot2

Description

This function lets the user use pre-defined default colours. Check your lares_pal()$labels scale. Feel free to use gg_vals() to debug colours used in latest plot.

Usage

gg_fill_customs(column = "fill", ...)

gg_colour_customs(column = "colour", ...)

gg_text_customs(column = "colour", ...)

gg_vals(layer = "fill", column = layer, cols = NULL, ...)

Arguments

column

Character. Select any of "fill" or "colour" to use on your lares_pal()$labels palette.
...

Allow additional parameters not used.
layer

Character. Select any of "fill", "colour", or "label" to get the layer containing the colours labels you wish to colour.
cols

Data.frame. Customize colour palette with a data.frame. Must contain values, fill, and colour columns.

Details

Notice that when the layer defined is any of GeomPoint, GeomLine, GeomText or GeomLabel, gg_colour_customs() will force column = "fill" parameter.

Value

Same as scale_fill_manual or scale_colour_manual but with custom palette applied.

See Also

Other Themes: lares_pal(), plot_palette(), theme_lares()

Examples

library("ggplot2")
# Generic plot function to run examples to
run_plot <- function(add_fxs = TRUE) {
  p <- data.frame(station = c("spring", "summer", "fall", "winter"), num = 1:4) %>%
    ggplot(aes(x = station, y = num, fill = station)) +
    geom_col() +
    geom_text(aes(y = 0.5, label = num, colour = station), size = 6)
  if (add_fxs) p <- p + gg_fill_customs() + gg_colour_customs()
  return(p)
}
# Default colours
run_plot()
# Check last colours used
gg_vals("fill", "fill")
gg_vals("colour", "colour")
# Change any default colour
options("lares.colours.custom" = data.frame(
  values = c("summer", "winter"),
  fill = c("pink", "black"),
  colour = c("black", "white")
))
run_plot()
# Check last colours used
gg_vals("fill", "fill")
gg_vals("colour", "colour")
# Reset to default colours
options("lares.colours.custom" = NULL)
# Notice you can use 'pal = 4' argument on theme_lares() too
run_plot(add_fxs = FALSE) + theme_lares(pal = 4)

Interpolate a string [glue wrapper]

Description

Format and interpolate a string using a glue wrapper. Allows simple operations, NULL values as input, and interactions with internal (created within glued) and external (environment) objects.

Usage

glued(..., .sep = "", empty_lines = "keep", .envir = parent.frame())

Arguments

...

[expressions]
Unnamed arguments are taken to be expression string(s) to format. Multiple inputs are concatenated together before formatting. Named arguments are taken to be temporary variables available for substitution.

For glue_data(), elements in ... override the values in .x.
.sep

[character(1): ‘""’]
Separator used to separate elements.
empty_lines

Character. Set to "keep" to keep or "drop" to drop empty lines.
.envir

[environment: parent.frame()]
Environment to evaluate each expression in. Expressions are evaluated from left to right. If .x is an environment, the expressions are evaluated in that environment and .envir is ignored. If NULL is passed, it is equivalent to emptyenv().

Value

Same as input but transformed (glued).

See Also

Other Tools: autoline(), bind_files(), bring_api(), cal_split(), chr2num(), db_download(), db_upload(), dont_sleep(), export_plot(), export_results(), files_functions(), font_exists(), formatColoured(), formatHTML(), grepm(), h2o_selectmodel(), haveInternet(), image_metadata(), importxlsx(), ip_data(), json2vector(), list_cats(), listfiles(), mail_send(), markdown2df(), move_files(), msplit(), myip(), quiet(), read.file(), statusbar(), tic(), try_require(), updateLares(), warnifnot(), what_size()

Examples

name <- "Bernardo"
age <- 29
anniversary <- as.Date("2016-04-30")
glued("
  My name is {name},
  my age next year will be {age + 1},
  and I got married on {format(anniversary, '%A, %B %d, %Y')}.")

# Single braces can be inserted by doubling them
glued("My name is {name}, not {{name}}.")

# You can also used named arguments
glued(
  "Her name is {name}, ",
  "and her age next year will be {age + 1}.",
  name = "Maru",
  age = 6
)

# And run operations with memories (beware!)
glued("My name, {name}, has {n <- nchar(name); n} characters.
       If we multiply by ten, we'll have {10 * n} characters!")

# If you pass a vector, the operation will be repeated for each element
glued("Here's the value #{1:3}")

ChatGPT API Interaction with R

Description

This function lets the user ask ChatGPT via its API, and returns the rendered reply. There are a couple of specific verbs (functions) with a preset prompt to help fetch the data in specific formats. We also store the prompts and replies in current session with their respective time-stamps so user can gather historical results.

Usage

gpt_ask(
  ask,
  secret_key = get_credentials()$openai$secret_key,
  url = Sys.getenv("LARES_GPT_URL"),
  model = Sys.getenv("LARES_GPT_MODEL"),
  num_retries = 3,
  temperature = 0.5,
  max_tokens = NULL,
  pause_base = 1,
  quiet = FALSE,
  ...
)

gpt_history(quiet = TRUE, ...)

gpt_table(x, cols = NULL, quiet = TRUE, ...)

gpt_classify(x, categories, quiet = TRUE, ...)

gpt_tag(x, tags, quiet = TRUE, ...)

gpt_extract(x, extract, quiet = TRUE, ...)

gpt_format(x, format, quiet = TRUE, ...)

gpt_convert(x, unit, quiet = TRUE, ...)

gpt_translate(x, language, quiet = TRUE, ...)

Arguments

ask

Character. Redacted prompt to ask. If multiple asks are requested, they will be concatenated with "+" into a single request.
secret_key

Character. Secret Key. Get yours in: platform.openai.com for OpenAI or makersuite.google.com/app/apikey for Gemini.
url

Character. Base API URL.
model

Character. OpenAI model to use. This can be adjusted according to the available models in the OpenAI API (such as "gpt-4").
num_retries

Integer. Number of times to retry the request in case of failure. Default is 3.
temperature

Numeric. The temperature to use for generating the response. Default is 0.5. The lower the temperature, the more deterministic the results in the sense that the highest probable next token is always picked. Increasing temperature could lead to more randomness, which encourages more diverse or creative outputs. You are essentially increasing the weights of the other possible tokens. In terms of application, you might want to use a lower temperature value for tasks like fact-based QA to encourage more factual and concise responses. For poem generation or other creative tasks, it might be beneficial to increase the temperature value.
max_tokens

Integer. The maximum number of tokens in the response.
pause_base

Numeric. The number of seconds to wait between retries. Default is 1.
quiet

Boolean. Keep quiet? If not, informative messages will be shown.
...

Additional parameters.
x

Vector. List items you wish to process in your instruction
cols

Vector. Force column names for your table results.
categories, tags

Vector. List of possible categories/tags to consider.
extract, format, unit

Character. Length 1 or same as x to extract/format/unit information from x. For example: email, country of phone number, country, amount as number, currency ISO code, ISO, Fahrenheit, etc.
language

Character. Language to translate to

Value

(Invisible) list. Content returned from API POST and processed.

See Also

Other API: bring_api(), fb_accounts(), fb_ads(), fb_creatives(), fb_insights(), fb_process(), fb_report_check(), fb_rf(), fb_token(), gemini_ask(), queryGA(), slackSend()

Other ChatGPT: gpt_prompter()

Other LLM: gemini_ask(), gpt_prompter()

Examples

## Not run: 
api_key <- get_credentials()$openai$secret_key
# Open question:
gpt_ask("Can you write an R function to plot a dummy histogram?", api_key)

##### The following examples return dataframes:
# Classify each element based on categories:
gpt_classify(1:10, c("odd", "even"))

# Add all tags that apply to each element based on tags:
gpt_tag(
  c("I love chocolate", "I hate chocolate", "I like Coke"),
  c("food", "positive", "negative", "beverage")
)

# Extract specific information:
gpt_extract(
  c("My mail is [email protected]", "30 Main Street, Brooklyn, NY, USA", "+82 2-312-3456", "$1.5M"),
  c("email", "full state name", "country of phone number", "amount as number")
)

# Format values
gpt_format(
  c("March 27th, 2021", "12-25-2023 3:45PM", "01.01.2000", "29 Feb 92"),
  format = "ISO Date getting rid of time stamps"
)

# Convert temperature units
gpt_convert(c("50C", "300K"), "Fahrenheit")

# Create a table with data
gpt_table("5 random people's address in South America, email, phone, age between 18-30")
gpt_table(
  ask = "5 largest cities, their countries, and population",
  cols = c("city_name", "where", "POP")
)

# Translate text to any language
gpt_translate(
  rep("I love you with all my heart", 5),
  language = c("spanish", "chinese", "japanese", "russian", "german")
)

# Now let's read the historical prompts, replies, ano more from current session
gpt_history()

## End(Not run)

Structured Prompt Builder for LLM (ChatGPT)

Description

Build standard prompts to get the right outcomes using the four theoretical elements required to build standard and cleaner asks: instruction, input, context, and output. Inspired by the Prompt Engineering Guide free guide. Remember to start simple and be very specific to get exactly what you need.

Usage

gpt_prompter(
  instruction = NULL,
  input = NULL,
  context = NULL,
  output = NULL,
  quiet = TRUE,
  ...
)

Arguments

instruction, input, context, output

Character or vector. You do not need all the four elements for a prompt and the format depends on the task at hand.
quiet

Boolean. Should the written prompt be printed or not?
...

Additional parameters. You can pass cols parameter to explicitly set column names.

Value

(Invisible) list with written prompt and elements provided.

Elements of a Prompt

Instruction

a specific task or instruction you want the model to perform. It usually starts with a verb given its an instruction.

Input

input data, elements or question that we are interested to find a response for,

Context

external information, additional context or references to steer the model to better responses.

Output

type or format of the output.

See Also

Other ChatGPT: gpt_ask()

Other LLM: gemini_ask(), gpt_ask()

Examples

# A simple formatted table with data
# Note: I mostly use output = "table" and enabled an auxiliary enrichment prompt
(p <- gpt_prompter(instruction = "Capitals of the world", output = "table"))

# Classify
p <- gpt_prompter(
  instruction = "For each of the inputs, classify using only the options in context",
  input = c("Molecule", "Elephant", "Milky Way", "Cat", "Planet Earth"),
  context = c("Big", "Medium", "Small"),
  output = "table",
  # This cols parameter is auxiliary
  cols = c("Input", "Category"),
  quiet = FALSE
)

# Tag all categories that apply
p <- gpt_prompter(
  instruction = paste(
    "For each of the inputs, provide which of the",
    "context values apply as correct tags using TRUE/FALSE"
  ),
  input = c("I love chocolate", "I hate chocolate", "I like Coke", "Who am I?", "T-REX"),
  context = c("food", "positive", "negative", "beverage"),
  output = "table",
  quiet = FALSE
)

# Extract information from strings
p <- gpt_prompter(
  instruction = "For each of the inputs, extract each of the information asked in context",
  input = c("My mail is [email protected]", "30 Main St, NY, USA", "+82 2-312-3456", "$1.5M"),
  context = c("email", "full state name", "country of phone", "full non-abbreviated number"),
  output = "table",
  cols = c("Input", "Element_to_extract", "Value"),
  quiet = FALSE
)

# Translate to several languages
p <- gpt_prompter(
  instruction = "For each of the inputs, translate to the respective languages in context",
  input = rep("I love you with all my heart", 5),
  context = c("spanish", "chinese", "japanese", "russian", "german"),
  output = "table",
  cols = c("Input", "Language", "Translation"),
  quiet = FALSE
)

# Format date values
p <- gpt_prompter(
  instruction = paste(
    "For each of the inputs,",
    "standardize and format all values to the format in context"
  ),
  input = c("March 27th, 2021", "12-25-2023 3:45PM", "01.01.2000", "29 Feb 92"),
  context = "ISO Date getting rid of time stamps",
  output = "table",
  cols = c("Input", "Formatted"),
  quiet = FALSE
)

# Convert units
p <- gpt_prompter(
  instruction = paste(
    "For each of the inputs,",
    "provide new converted values using the units in context"
  ),
  input = c("50C", "300K", "100F", "0F", "32C", "0K"),
  context = "Fahrenheit",
  output = "table",
  cols = c("Input", "Original_Unit", "Total_Value", "Converted_Value", "New_Unit"),
  quiet = FALSE
)

# Read a text and answer a question related to it
gpt_prompter(
  instruction = "read",
  context = "Long text here",
  input = "Question here"
)$prompt

Pattern Matching for Letters considering Blanks

Description

Match pattern of letters considering blanks within each element of a character vector, allowing counted characters between and around each letter. Used as an auxiliary function for the Scrabble family of functions.

Usage

grepl_letters(x, pattern, blank = "_")

Arguments

x

Character vector
pattern

Character. Character string containing a semi-regular expression which uses the following logic: "a_b" means any character that contains "a" followed by something followed by "b", anywhere in the string.
blank

Character. String to use between letters.

Value

Boolean check for each value on x.

Examples

x <- c("aaaa", "bbbb", "baba", "aabb", "a", "ab")
grepl_letters(x, "ab")
grepl_letters(x, "_ab")
grepl_letters(x, "a_a")
grepl_letters(x, "c")

Pattern Matching for Any or All Multiple Matches

Description

This function returns a boolean vector of the same length as 'x', each element of which is the result of applying the 'type' of matches to the corresponding element of 'x', using regular expressions.

Usage

grepm(pattern, x, type = "all", ...)

Arguments

pattern

character string containing a regular expression (or character string for fixed = TRUE) to be matched in the given character vector. Coerced by as.character to a character string if possible. If a character vector of length 2 or more is supplied, the first element is used with a warning. Missing values are allowed except for regexpr, gregexpr and regexec.
x

Character vector. Text where matches are sought, or an object which can be coerced by as.character to a character vector. Long vectors are supported.
type

Character. Type of match. Choose one of: any, all
...

Additional arguments to pass to grepl

Value

Boolean of same length as x

See Also

Other Tools: autoline(), bind_files(), bring_api(), cal_split(), chr2num(), db_download(), db_upload(), dont_sleep(), export_plot(), export_results(), files_functions(), font_exists(), formatColoured(), formatHTML(), glued(), h2o_selectmodel(), haveInternet(), image_metadata(), importxlsx(), ip_data(), json2vector(), list_cats(), listfiles(), mail_send(), markdown2df(), move_files(), msplit(), myip(), quiet(), read.file(), statusbar(), tic(), try_require(), updateLares(), warnifnot(), what_size()

Examples

x <- c(123, 876, 18761)
patterns <- c(1, 2)
grepm(patterns, x, type = "any")
grepm(patterns, x, type = "all")

Google Trends: Related and Time Plots

Description

This function creates a plot with Google Trend's related topics and queries, and let the user compare different keywords.

This function creates a plot with google trend's data on timelines and let the user compare different keywords.

Usage

gtrends_related(gtrend, top = NA, title = NA, note = NA, exclude = NULL)

gtrends_time(gtrend, title = NA)

Arguments

gtrend

List. Result from gtrendsR::gtrends(keyword, geo, time)
top

Integer. Filter top n results only.
title

Character. Custom title for the plot.
note

Character. Add a note to the plot if needed.
exclude

Character vector. Which observations do you wish to exclude?

Value

plot for Google Trend's results input gtrend.

Plot for Google Trend's results input gtrend.

See Also

Other Scrapper: filesGD(), holidays(), ip_data(), mp3_get(), readGS(), splot_summary(), stocks_quote()

Other Google: filesGD(), queryGA(), readGS()

Automated H2O's AutoML

Description

This function lets the user create a robust and fast model, using H2O's AutoML function. The result is a list with the best model, its parameters, datasets, performance metrics, variables importance, and plots. Read more about the h2o_automl() pipeline here.

Usage

h2o_automl(
  df,
  y = "tag",
  ignore = NULL,
  train_test = NA,
  split = 0.7,
  weight = NULL,
  target = "auto",
  balance = FALSE,
  impute = FALSE,
  no_outliers = TRUE,
  unique_train = TRUE,
  center = FALSE,
  scale = FALSE,
  thresh = 10,
  seed = 0,
  nfolds = 5,
  max_models = 3,
  max_time = 10 * 60,
  start_clean = FALSE,
  exclude_algos = c("StackedEnsemble", "DeepLearning"),
  include_algos = NULL,
  plots = TRUE,
  alarm = TRUE,
  quiet = FALSE,
  print = TRUE,
  save = FALSE,
  subdir = NA,
  project = "AutoML Results",
  model_name = NULL,
  verbosity = NULL,
  ...
)

## S3 method for class 'h2o_automl'
plot(x, ...)

## S3 method for class 'h2o_automl'
print(x, importance = TRUE, ...)

Arguments

df

Dataframe. Dataframe containing all your data, including the dependent variable labeled as 'tag'. If you want to define which variable should be used instead, use the y parameter.
y

Variable or Character. Name of the dependent variable or response.
ignore

Character vector. Force columns for the model to ignore
train_test

Character. If needed, df's column name with 'test' and 'train' values to split
split

Numeric. Value between 0 and 1 to split as train/test datasets. Value is for training set. Set value to 1 to train with all available data and test with same data (cross-validation will still be used when training). If train_test is set, value will be overwritten with its real split rate.
weight

Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed.
target

Value. Which is your target positive value? If set to 'auto', the target with largest mean(score) will be selected. Change the value to overwrite. Only used when binary categorical model.
balance

Boolean. Auto-balance train dataset with under-sampling?
impute

Boolean. Fill NA values with MICE?
no_outliers

Boolean/Numeric. Remove y's outliers from the dataset? Will remove those values that are farther than n standard deviations from the dependent variable's mean (Z-score). Set to TRUE for default (3) or numeric to set a different multiplier.
unique_train

Boolean. Keep only unique row observations for training data?
center, scale

Boolean. Using the base function scale, do you wish to center and/or scale all numerical values?
thresh

Integer. Threshold for selecting binary or regression models: this number is the threshold of unique values we should have in 'tag' (more than: regression; less than: classification)
seed

Integer. Set a seed for reproducibility. AutoML can only guarantee reproducibility if max_models is used because max_time is resource limited.
nfolds

Number of folds for k-fold cross-validation. Must be >= 2; defaults to 5. Use 0 to disable cross-validation; this will also disable Stacked Ensemble (thus decreasing the overall model performance).
max_models, max_time

Numeric. Max number of models and seconds you wish for the function to iterate. Note that max_models guarantees reproducibility and max_time not (because it depends entirely on your machine's computational characteristics)
start_clean

Boolean. Erase everything in the current h2o instance before we start to train models? You may want to keep other models or not. To group results into a custom common AutoML project, you may use project_name argument.
exclude_algos, include_algos

Vector of character strings. Algorithms to skip or include during the model-building phase. Set NULL to ignore. When both are defined, only include_algos will be valid.
plots

Boolean. Create plots objects?
alarm

Boolean. Ping (sound) when done. Requires beepr.
quiet

Boolean. Keep quiet? If not, informative messages will be shown.
print

Boolean. Print summary when process ends?
save

Boolean. Do you wish to save/export results into your working directory?
subdir

Character. In which directory do you wish to save the results? Working directory as default.
project

Character. Your project's name
model_name

Character. Optional custom name for the model. If provided, this name will be used when saving the model with export_results(). If NULL (default), the H2O-generated model ID will be used.
verbosity

Character. Verbosity of the backend messages printed during training. Must be one of NULL (live log disabled), "debug", "info", "warn", "error". Defaults to NULL.
...

Additional parameters on h2o::h2o.automl
x

h2o_automl object
importance

Boolean. Print important variables?

Details

For additional tutorials and examples:

Value

List. Trained model, predicted scores and datasets used, performance metrics, parameters, importance data.frame, seed, and plots when plots=TRUE.

List of algorithms

-> Read more here

DRF

Distributed Random Forest, including Random Forest (RF) and Extremely-Randomized Trees (XRT)

GLM

Generalized Linear Model

XGBoost

eXtreme Grading Boosting

GBM

Gradient Boosting Machine

DeepLearning

Fully-connected multi-layer artificial neural network

StackedEnsemble

Stacked Ensemble

Methods

print

Use print method to print models stats and summary

plot

Use plot method to plot results using mplot_full()

See Also

Other Machine Learning: ROC(), conf_mat(), export_results(), gain_lift(), h2o_predict_MOJO(), h2o_selectmodel(), impute(), iter_seeds(), lasso_vars(), model_metrics(), model_preprocess(), msplit()

Examples

## Not run: 
# CRAN
data(dft) # Titanic dataset
dft <- subset(dft, select = -c(Ticket, PassengerId, Cabin))

# Classification: Binomial - 2 Classes
r <- h2o_automl(dft, y = Survived, max_models = 1, impute = FALSE, target = "TRUE", alarm = FALSE)

# Let's see all the stuff we have inside:
lapply(r, names)

# Classification: Multi-Categorical - 3 Classes
r <- h2o_automl(dft, Pclass, ignore = c("Fare", "Cabin"), max_time = 30, plots = FALSE)

# Regression: Continuous Values
r <- h2o_automl(dft, y = "Fare", ignore = c("Pclass"), exclude_algos = NULL, quiet = TRUE)
print(r)

# WITH PRE-DEFINED TRAIN/TEST DATAFRAMES
splits <- msplit(dft, size = 0.8)
splits$train$split <- "train"
splits$test$split <- "test"
df <- rbind(splits$train, splits$test)
r <- h2o_automl(df, "Survived", max_models = 1, train_test = "split")

## End(Not run)

DALEX Explainer for H2O

Description

DALEX helper function to create an explainer object using a h2o trained model.

Usage

h2o_explainer(df, model, y = "tag", ignore = NULL, ...)

Arguments

df

Dataframe. Must contain all columns and predictions
model

Model object (H2O)
y

Character or Variable name. Variable's column name.
ignore

Character vector. Which columns should be ignored?
...

Additional parameters to pass to h2o_predict_model or h2o_predict_MOJO.

Value

List; explainer. Containing the model, data, y, predict_function, y_hat, residuals, class, label, model_info, residual_function, and weights.

See Also

Other Interpretability: dalex_local(), dalex_residuals(), dalex_variable()

Examples

# You must have "DALEX" library to use this auxiliary function:
## Not run: 
data(dft) # Titanic dataset

# TRAIN A SIMPLE MODEL
dfm <- h2o_automl(dft,
  y = "Survived",
  ignore = c("Ticket", "PassengerId", "Cabin"),
  max_models = 1
)

# EXPLAINER
explainer <- h2o_explainer(df = dfm$datasets$test, model = dfm$model, y = "Survived")
explainer$data <- na.omit(explainer$data)

# CATEGORICAL EXAMPLE
class <- dalex_variable(explainer, vars = c("Pclass", "Sex"))
class$plot

# NUMERICAL EXAMPLE
num <- dalex_variable(explainer, vars = c("Fare", "Age"))
num$plot

# LOCAL EXAMPLE
local <- dalex_local(explainer, row = 1)
# OR YOU COULD MANUALLY INPUT THE OBSERVATION
local <- dalex_local(explainer, observation = explainer$data[1, ])
local$plot

# xai2shiny's UI (needs to be installed from ModelOriented/xai2shiny)
xai2shiny(explainer, run = TRUE)

## End(Not run)

Calculate predictions of h2o Models

Description

h2o_predict_MOJO lets the user predict using the h2o .zip file containing the MOJO files. Note that it works with the files generated when using the function export_results()

h2o_predict_binary lets the user predict using the h2o binary file. Note that it works with the files generated when using the function export_results(). Recommendation: use the h2o_predict_MOJO() function when possible - it let's you change h2o's version without problem.

h2o_predict_model lets the user get scores from a H2O Model Object.

h2o_predict_API lets the user get the score from an API service

Usage

h2o_predict_MOJO(df, model_path, method = "mojo", batch = 300)

h2o_predict_binary(df, model_path, sample = NA)

h2o_predict_model(df, model)

h2o_predict_API(df, api, exclude = "tag")

Arguments

df

Dataframe/Vector. Data to insert into the model.
model_path

Character. Relative model path directory or zip file.
method

Character. One of "mojo" or "json".
batch

Integer. Run n batches at a time for "json" method.
sample

Integer. How many rows should the function predict?
model

h2o model Object
api

Character. API URL.
exclude

Character. Name of the variables to exclude.

Value

data.frame with predicted results.

vector with predicted results.

data.frame with predicted results.

vector with predicted results.

See Also

Other Machine Learning: ROC(), conf_mat(), export_results(), gain_lift(), h2o_automl(), h2o_selectmodel(), impute(), iter_seeds(), lasso_vars(), model_metrics(), model_preprocess(), msplit()

Automated H2O's AutoML Results

Description

This is an auxiliary function to calculate predictions and results when using the h2o_automl() function.

Usage

h2o_results(
  h2o_object,
  test,
  train,
  y = "tag",
  which = 1,
  model_type,
  target = "auto",
  split = 0.7,
  ignore = NULL,
  quiet = FALSE,
  project = "ML Project",
  seed = 0,
  leaderboard = list(),
  plots = TRUE,
  model_name = NULL,
  ...
)

Arguments

h2o_object

H2O Leaderboard (H2OFrame/H2OAutoML) or Model (h2o)
test, train

Dataframe. Must have the same columns
y

Variable or Character. Name of the dependent variable or response.
which

Integer. Which model to select from leaderboard
model_type

Character. Select "Classification" or "Regression"
target

Value. Which is your target positive value? If set to 'auto', the target with largest mean(score) will be selected. Change the value to overwrite. Only used when binary categorical model.
split

Numeric. Value between 0 and 1 to split as train/test datasets. Value is for training set. Set value to 1 to train with all available data and test with same data (cross-validation will still be used when training). If train_test is set, value will be overwritten with its real split rate.
ignore

Character vector. Columns too ignore
quiet

Boolean. Keep quiet? If not, informative messages will be shown.
project

Character. Your project's name
seed

Integer. Set a seed for reproducibility. AutoML can only guarantee reproducibility if max_models is used because max_time is resource limited.
leaderboard

H2O's Leaderboard. Passed when using h2o_selectmodel as it contains plain model and no leader board.
plots

Boolean. Create plots objects?
model_name

Character. Optional custom name for the model. If provided, this name will be used when saving the model with export_results(). If NULL (default), the H2O-generated model ID will be used.
...

Additional parameters on h2o::h2o.automl

Value

List. Trained model, predicted scores and datasets used, performance metrics, parameters, importance data.frame, seed, and plots when plots=TRUE.

Select Model from h2o_automl's Leaderboard

Description

Select wich model from the h2o_automl function to use

Usage

h2o_selectmodel(results, which_model = 1, quiet = FALSE, ...)

Arguments

results

h2o_automl() object.
which_model

Integer. Which model from the leaderboard you wish to use?
quiet

Boolean. Keep quiet? If not, informative messages will be shown.
...

Additional parameters on h2o::h2o.automl

Value

H2O processed model

See Also

Other Machine Learning: ROC(), conf_mat(), export_results(), gain_lift(), h2o_automl(), h2o_predict_MOJO(), impute(), iter_seeds(), lasso_vars(), model_metrics(), model_preprocess(), msplit()

Other Tools: autoline(), bind_files(), bring_api(), cal_split(), chr2num(), db_download(), db_upload(), dont_sleep(), export_plot(), export_results(), files_functions(), font_exists(), formatColoured(), formatHTML(), glued(), grepm(), haveInternet(), image_metadata(), importxlsx(), ip_data(), json2vector(), list_cats(), listfiles(), mail_send(), markdown2df(), move_files(), msplit(), myip(), quiet(), read.file(), statusbar(), tic(), try_require(), updateLares(), warnifnot(), what_size()

SHAP values for H2O Models

Description

SHAP (SHapley Additive exPlanations) by Lundberg and Lee (2016) is a method to explain individual predictions. SHAP is based on the game theoretically optimal Shapley Values. Calculate SHAP values for h2o models in which each row is an observation and each column a feature. Use plot method to visualize features importance and distributions.

Usage

h2o_shap(model, test = "auto", scores = "auto", y = "y", ...)

## S3 method for class 'h2o_shap'
plot(x, relevant = TRUE, top = 15, quiet = FALSE, ...)

Arguments

model

h2o_automl object or h2o model.
test

String or Dataframe. Leave "auto" to use h2o_automl's test dataset or pass a valid dataframe.
scores

Numeric vector. If test != "auto", you must provide predicted values
y

Character. If test != "auto", you must provide y variable's name
...

Additional argument for predict_contributions.H2OModel
x

h2o_shap object
relevant

Boolean. Keep only relevant non-trivial (>0) features
top

Integer. Plot only top n values (as in importance)
quiet

Boolean. Keep quiet? If not, informative messages will be shown.

Value

H2OFrame with shap values for every observation and feature.

See Also

Other SHAP: shap_var()

Examples

## Not run: 
# Train a h2o_automl model
model <- h2o_automl(dft, Survived,
  max_models = 1, target = TRUE,
  ignore = c("Ticket", "Cabin", "PassengerId"),
  quiet = TRUE
)

# Calculate SHAP values
SHAP_values <- h2o_shap(model)
# Equivalent to:
# SHAP_values <- h2o_shap(
#  model = model$model,
#  test = model$datasets$test,
#  scores = model$scores_test$scores)

# Check SHAP results
head(SHAP_values)

# You must have "ggbeeswarm" library to use this auxiliary function:
# Plot SHAP values (feature importance)
plot(SHAP_values)

# Plot some of the variables (categorical)
shap_var(SHAP_values, Pclass)

# Plot some of the variables (numerical)
shap_var(SHAP_values, Fare)

## End(Not run)

Internet Connection Check

Description

This function checks if your R session currently have Wifi or Internet connection.

Usage

haveInternet(thresh = 3, url = "http://www.google.com")

Arguments

thresh

Numeric. How many seconds to consider a slow connection?
url

Character. URL to test the readLines 1 command

Value

Boolean. Result of checking if device has internet connection.

See Also

Other Tools: autoline(), bind_files(), bring_api(), cal_split(), chr2num(), db_download(), db_upload(), dont_sleep(), export_plot(), export_results(), files_functions(), font_exists(), formatColoured(), formatHTML(), glued(), grepm(), h2o_selectmodel(), image_metadata(), importxlsx(), ip_data(), json2vector(), list_cats(), listfiles(), mail_send(), markdown2df(), move_files(), msplit(), myip(), quiet(), read.file(), statusbar(), tic(), try_require(), updateLares(), warnifnot(), what_size()

Holidays in your Country

Description

This function lets the user automatically retrieve public holiday dates for any country supported by the Nager.Date API. Accepts country names (e.g., "Portugal") or ISO 3166-1 alpha-2 codes (e.g., "PT"). Thanks to Nager.Date!

Usage

holidays(
  countries = "Venezuela",
  years = year(Sys.Date()),
  quiet = FALSE,
  include_regions = FALSE
)

Arguments

countries

Character or vector. For which country(ies) should the holidays be imported? Accepts country names or ISO 3166-1 alpha-2 codes.
years

Character or vector. For which year(s) do you wish to import holiday dates?
quiet

Boolean. Keep quiet? If not, informative messages will be shown.
include_regions

Boolean. Default FALSE. If TRUE, for countries with internal subdivisions, it will provide details on which sub-state the found holidays apply.

Value

data.frame with holidays data for given countries and years.

See Also

Other Data Wrangling: balance_data(), categ_reducer(), cleanText(), date_cuts(), date_feats(), file_name(), formatHTML(), impute(), left(), normalize(), num_abbr(), ohe_commas(), ohse(), quants(), removenacols(), replaceall(), replacefactor(), textFeats(), textTokenizer(), vector2text(), year_month(), zerovar()

Other Feature Engineering: date_feats(), ohse()

Other Scrapper: filesGD(), gtrends_related(), ip_data(), mp3_get(), readGS(), splot_summary(), stocks_quote()

Other One Hot Encoding: date_feats(), ohe_commas(), ohse()

Examples

holidays(countries = "Argentina")
year <- as.integer(format(Sys.Date(), format = "%Y"))
holidays(countries = c("Spain", "Venezuela"), years = year)
holidays(countries = "Germany", include_regions = TRUE)
holidays(countries = "PT") # Also accepts ISO country codes

Get Meta Data from Image Files

Description

This function lets the user get meta data from image files or directory.

Usage

image_metadata(files)

Arguments

files

Character vector. Files or directory which contains files.

Value

data.frame with meta-data for each image file.

See Also

Other Tools: autoline(), bind_files(), bring_api(), cal_split(), chr2num(), db_download(), db_upload(), dont_sleep(), export_plot(), export_results(), files_functions(), font_exists(), formatColoured(), formatHTML(), glued(), grepm(), h2o_selectmodel(), haveInternet(), importxlsx(), ip_data(), json2vector(), list_cats(), listfiles(), mail_send(), markdown2df(), move_files(), msplit(), myip(), quiet(), read.file(), statusbar(), tic(), try_require(), updateLares(), warnifnot(), what_size()

Import Excel File with All Its Tabs

Description

This function lets the user import an Excel file's tabs into a list

Usage

importxlsx(file)

Arguments

file

String. Local Excel file name

Value

List or data.frame. If single tab is found, a data.frame; if multiple tabs are found on file, a list of data.frames.

See Also

Other Tools: autoline(), bind_files(), bring_api(), cal_split(), chr2num(), db_download(), db_upload(), dont_sleep(), export_plot(), export_results(), files_functions(), font_exists(), formatColoured(), formatHTML(), glued(), grepm(), h2o_selectmodel(), haveInternet(), image_metadata(), ip_data(), json2vector(), list_cats(), listfiles(), mail_send(), markdown2df(), move_files(), msplit(), myip(), quiet(), read.file(), statusbar(), tic(), try_require(), updateLares(), warnifnot(), what_size()

Impute Missing Values (using MICE)

Description

This function uses the MICE methodology to impute missing values.

Usage

impute(df, m = 5, iters = 5, seed = 0, quiet = FALSE)

Arguments

df

Dataframe. Dataframe to transform.
m

Integer. Number of multiple imputations.
iters

Integer. Number of iterations.
seed

Integer. Set a seed for reproducibility.
quiet

Boolean. Keep quiet? If not, informative messages will be shown.

Value

data.frame with imputed values.

See Also

Other Data Wrangling: balance_data(), categ_reducer(), cleanText(), date_cuts(), date_feats(), file_name(), formatHTML(), holidays(), left(), normalize(), num_abbr(), ohe_commas(), ohse(), quants(), removenacols(), replaceall(), replacefactor(), textFeats(), textTokenizer(), vector2text(), year_month(), zerovar()

Other Machine Learning: ROC(), conf_mat(), export_results(), gain_lift(), h2o_automl(), h2o_predict_MOJO(), h2o_selectmodel(), iter_seeds(), lasso_vars(), model_metrics(), model_preprocess(), msplit()

Other Missing Values: missingness()

Install/Update Additional Recommended Libraries

Description

All needed libraries to use (most) lares are already a dependency. There are some functions that many people won't event know exist that will require other additional libraries. Also, this may be used as a Docker way of installing useful libraries on an new instance.

Usage

install_recommended(progress = TRUE, all = FALSE)

Arguments

progress

Boolean. Show status bar?
all

Boolean. All packages? If not, only the ones not installed yet.

Scrap data based on IP address

Description

This function lets the user scrap https://db-ip.com/ given IP address(es) to get their associated address type, ASN, ISP, organization, country, state or region, county, city, ZIP postal code, weather station, coordinates, Timezone, local time, languages, and currency.

Usage

ip_data(ip = myip(), quiet = FALSE)

Arguments

ip

Vector. Vector with all IP's we wish to search.
quiet

Boolean. Do not show the loading statusbar?

Value

data.frame. Each row is an unique ip address, and columns will bee created for all the additional information found.

See Also

Other Tools: autoline(), bind_files(), bring_api(), cal_split(), chr2num(), db_download(), db_upload(), dont_sleep(), export_plot(), export_results(), files_functions(), font_exists(), formatColoured(), formatHTML(), glued(), grepm(), h2o_selectmodel(), haveInternet(), image_metadata(), importxlsx(), json2vector(), list_cats(), listfiles(), mail_send(), markdown2df(), move_files(), msplit(), myip(), quiet(), read.file(), statusbar(), tic(), try_require(), updateLares(), warnifnot(), what_size()

Other Scrapper: filesGD(), gtrends_related(), holidays(), mp3_get(), readGS(), splot_summary(), stocks_quote()

Examples

ip_data("163.114.132.0")
ip_data(ip = c(myip(), "201.244.197.199"), quiet = TRUE)

Iterate Seeds on AutoML

Description

This functions lets the user iterate and search for best seed. Note that if the results change a lot, you are having a high variance in your data.

Usage

iter_seeds(df, y, tries = 10, ...)

Arguments

df

Dataframe. Dataframe containing all your data, including the dependent variable labeled as 'tag'. If you want to define which variable should be used instead, use the y parameter.
y

Variable or Character. Name of the dependent variable or response.
tries

Integer. Number of iterations
...

Additional arguments passed to h2o_automl

Value

data.frame with performance results by seed tried on every row.

See Also

Other Machine Learning: ROC(), conf_mat(), export_results(), gain_lift(), h2o_automl(), h2o_predict_MOJO(), h2o_selectmodel(), impute(), lasso_vars(), model_metrics(), model_preprocess(), msplit()

Convert Python JSON string to R vector (data.frame with 1 row)

Description

This function lets the user transform a JSON string into vector (data.frame with 1 row). You can also pass a Python's dictionary. For any other JSON transformation, jsonlite is recommended.

Usage

json2vector(json)

Arguments

json

Character. JSON string.

Value

List, data.frame, or vector. Depends on the json string.

See Also

Other Tools: autoline(), bind_files(), bring_api(), cal_split(), chr2num(), db_download(), db_upload(), dont_sleep(), export_plot(), export_results(), files_functions(), font_exists(), formatColoured(), formatHTML(), glued(), grepm(), h2o_selectmodel(), haveInternet(), image_metadata(), importxlsx(), ip_data(), list_cats(), listfiles(), mail_send(), markdown2df(), move_files(), msplit(), myip(), quiet(), read.file(), statusbar(), tic(), try_require(), updateLares(), warnifnot(), what_size()

Examples

json2vector('{"id": 1, "nodata": null, "gender": "M"}')

Lean Analytics and Robust Exploration Sidekick

Description

R library for better/faster analytics, visualization, data mining, and machine learning tasks.

Author(s)

Bernardo Lares ([email protected])

See Also

Useful links:

Personal Colours Palette

Description

Fetch customizable palettes for the library's usage. The package has its own default colour-blind friendly colours but can be customized using R internal options (i.e. options("lares.palette" = c("#FF8303" = "#000", "#40A4D8" = "#FFF", ...)). There are 3 options you can use to customize all colour palletes: "lares.palette" (vector, will be used in the same order as passed, and must have a counter colour defined), "lares.colours" (vector, simple colour names and their HEX codes), and "lares.colours.custom" (data.frame, containing "values" to use dynamically, "fill" for main colour, and "colour" (not obligatory) for counter colour).

Usage

lares_pal(return = "list")

Arguments

return

Character. Get only what you need. Select any of: "all" or "list" (list), "colors" or "colours" (vector), "pal" or "palette" (named vector), "simple" (named vector), "custom" or "personal" (data.frame)

Value

Depending on the return input, we get a:

  • vector with palette results vector

  • vector with palette results vector's names

  • list with palette results vector, labels results data.frame, and simple results named vector

See Also

Other Themes: gg_fill_customs(), plot_palette(), theme_lares()

Examples

# Simple colour-named palette
lares_pal("simple")

# Raw colours and counter-colours
# OR simply: lares_pal("palette")
nice_palette <- lares_pal("colours")
nice_palette_ctr <- as.vector(lares_pal()$palette)
lapply(list(nice_palette, nice_palette_ctr), head)

# Personal colours by name
df <- lares_pal("custom")
df[sample(nrow(df), 5), ]

Pipe operator

Description

Pipe operator

Most Relevant Features Using Lasso Regression

Description

Use Lasso regression to identify the most relevant variables that can predict/identify another variable. You might want to compare with corr_var() and/or x2y() results to compliment the analysis No need to standardize, center or scale your data. Tidyverse friendly.

Usage

lasso_vars(
  df,
  variable,
  ignore = NULL,
  nlambdas = 100,
  nfolds = 10,
  top = 20,
  quiet = FALSE,
  seed = 123,
  ...
)

Arguments

df

Dataframe. Any dataframe is valid as ohse will be applied to process categorical values, and values will be standardize automatically.
variable

Variable. Dependent variable or response.
ignore

Character vector. Variables to exclude from study.
nlambdas

Integer. Number of lambdas to be used in a search.
nfolds

Integer. Number of folds for K-fold cross-validation (>= 2).
top

Integer. Plot top n results only.
quiet

Boolean. Keep quiet? If not, informative messages will be shown.
seed

Numeric.
...

Additional parameters passed to ohse().

Value

List. Contains lasso model coefficients, performance metrics, the actual model fitted and a plot.

See Also

Other Machine Learning: ROC(), conf_mat(), export_results(), gain_lift(), h2o_automl(), h2o_predict_MOJO(), h2o_selectmodel(), impute(), iter_seeds(), model_metrics(), model_preprocess(), msplit()

Other Exploratory: corr_var(), crosstab(), df_str(), distr(), freqs(), freqs_df(), freqs_list(), freqs_plot(), missingness(), plot_cats(), plot_df(), plot_nums(), tree_var()

Examples

## Not run: 
# CRAN
Sys.unsetenv("LARES_FONT") # Temporal
data(dft) # Titanic dataset

m <- lasso_vars(dft, Survived, ignore = c("Cabin"))
print(m$coef)
print(m$metrics)
plot(m$plot)

## End(Not run)

Left or Right N characters of a string

Description

This functions lets the user extract the first or last n characters of a string or vector of strings.

Usage

left(string, n = 1)

right(string, n = 1)

Arguments

string

String or Vector.
n

Integer. How many characters starting on right/left?

Value

Character. Trimmed strings.

See Also

Other Data Wrangling: balance_data(), categ_reducer(), cleanText(), date_cuts(), date_feats(), file_name(), formatHTML(), holidays(), impute(), normalize(), num_abbr(), ohe_commas(), ohse(), quants(), removenacols(), replaceall(), replacefactor(), textFeats(), textTokenizer(), vector2text(), year_month(), zerovar()

Examples

left("Bernardo", 3)
right(c("Bernardo", "Lares", "V"), 3)

List categorical values for data.frame

Description

Make a list with all categorical values and

Usage

list_cats(df, ..., abc = TRUE)

Arguments

df

data.frame
...

Variables to segment counters
abc

Boolean. Sort alphabetically?

Value

List. Length same as number of categorical columns, each with a frequency data.frame using freqs().

See Also

Other Tools: autoline(), bind_files(), bring_api(), cal_split(), chr2num(), db_download(), db_upload(), dont_sleep(), export_plot(), export_results(), files_functions(), font_exists(), formatColoured(), formatHTML(), glued(), grepm(), h2o_selectmodel(), haveInternet(), image_metadata(), importxlsx(), ip_data(), json2vector(), listfiles(), mail_send(), markdown2df(), move_files(), msplit(), myip(), quiet(), read.file(), statusbar(), tic(), try_require(), updateLares(), warnifnot(), what_size()

Examples

data(dft) # Titanic dataset
df <- dft[, 1:5]
head(df)
list_cats(df)

List files in a directory

Description

This function lets the user list all files on a given directory. It also lets filter files which contains a string.

Usage

listfiles(folder = getwd(), recursive = TRUE, regex = NA, images = FALSE)

Arguments

folder

Character. Directory which contains files
recursive

Boolean. Should the listing recurse into directories?
regex

Character. String to use for filtering files
images

Boolean. Bring only image files?

Value

data.frame with relevant data for each file on folder directory.

See Also

Other Tools: autoline(), bind_files(), bring_api(), cal_split(), chr2num(), db_download(), db_upload(), dont_sleep(), export_plot(), export_results(), files_functions(), font_exists(), formatColoured(), formatHTML(), glued(), grepm(), h2o_selectmodel(), haveInternet(), image_metadata(), importxlsx(), ip_data(), json2vector(), list_cats(), mail_send(), markdown2df(), move_files(), msplit(), myip(), quiet(), read.file(), statusbar(), tic(), try_require(), updateLares(), warnifnot(), what_size()

Examples

# All files in current directory (without recursive files)
df <- listfiles(recursive = TRUE)
head(df, 3)

# All files in current directory (with recursive files)
df <- listfiles(recursive = TRUE)
tail(df, 3)

# Check R files using regex
df <- listfiles(regex = "\\.R$")

Logarithmic Loss Function for Binary Models

Description

This function calculates log loss/cross-entropy loss for binary models. NOTE: when result is 0.69315, the classification is neutral; it assigns equal probability to both classes.

Usage

loglossBinary(tag, score, eps = 0.001)

Arguments

tag

Vector. Real known label
score

Vector. Predicted value or model's result
eps

Numeric. Epsilon value

See Also

Other Model metrics: ROC(), conf_mat(), errors(), gain_lift(), model_metrics()

Send Emails with Attachments (POST)

Description

This function lets the user send Emails with Attachments using MailGun's API service.

Usage

mail_send(
  from = "RMail <[email protected]>",
  to = "[email protected]",
  cc = NULL,
  bcc = NULL,
  subject = "Mail from R",
  text = " \n",
  html = NULL,
  attachment = NULL,
  service = "mailgun",
  creds = NULL,
  quiet = FALSE,
  ...
)

Arguments

from, to, cc, bcc

Character. Emails
subject

Character. Subject for the email.
text, html

Character. Text or HTML to send in the body.
attachment

Character, plot or data.frame. Will send the file, plot as PNG or data.frame as CSV, respectively.
service

Character. Service platform to search on creds.
creds

Character. Credential's user (see get_creds()). Must contain: url (POST address), api (API key).
quiet

Boolean. Keep quiet? If not, informative messages will be shown.
...

Additional parameters.

Value

No return value, called for side effects.

See Also

Other Tools: autoline(), bind_files(), bring_api(), cal_split(), chr2num(), db_download(), db_upload(), dont_sleep(), export_plot(), export_results(), files_functions(), font_exists(), formatColoured(), formatHTML(), glued(), grepm(), h2o_selectmodel(), haveInternet(), image_metadata(), importxlsx(), ip_data(), json2vector(), list_cats(), listfiles(), markdown2df(), move_files(), msplit(), myip(), quiet(), read.file(), statusbar(), tic(), try_require(), updateLares(), warnifnot(), what_size()

Other Credentials: db_download(), db_upload(), encrypt_file(), get_credentials(), get_tweets(), queryDB(), queryGA(), slackSend(), stocks_file()

Examples

## Not run: 
myPlot <- noPlot("My plot")
mail_send(
  from = "BLV <[email protected]>",
  to = "[email protected]",
  cc = "[email protected]",
  subject = paste("Daily report:", Sys.Date()),
  attachment = myPlot
)

## End(Not run)

Convert markdown string tables to data.frame

Description

Convert markdown string tables to data.frame

Usage

markdown2df(text, autoformat = TRUE)

Arguments

text

Character. Markdown text representing a table.
autoformat

Boolean. Automatically format numerical, logical and date values to their classes?

See Also

Other Tools: autoline(), bind_files(), bring_api(), cal_split(), chr2num(), db_download(), db_upload(), dont_sleep(), export_plot(), export_results(), files_functions(), font_exists(), formatColoured(), formatHTML(), glued(), grepm(), h2o_selectmodel(), haveInternet(), image_metadata(), importxlsx(), ip_data(), json2vector(), list_cats(), listfiles(), mail_send(), move_files(), msplit(), myip(), quiet(), read.file(), statusbar(), tic(), try_require(), updateLares(), warnifnot(), what_size()

Examples

txt <- "| Item | Value |\n|------|-------|\n| 50C  | 122F  |\n| 300K | 80.33F |"
markdown2df(txt)

Maze Solver, inspired by Micromouse competitions

Description

Modified recursive depth-first search (DFS) algorithm to solve mazes. It explores the maze by recursively moving to adjacent cells until it finds a path from the starting point to the destination. Contains options to maximize paths by trying to turn less, allowing diagonal turns, prioritizing turns that chooses next step pointing towards the end point, and a grid search combining parameters to find best route.

Usage

maze_solve(
  maze,
  start = c(1, 1),
  end = dim(maze),
  inertia = FALSE,
  aim = TRUE,
  diagonal = TRUE,
  random = FALSE,
  timeout = 4,
  quiet = FALSE,
  seed = NULL,
  ...
)

## S3 method for class 'maze_solve'
print(x, ...)

maze_gridsearch(
  maze,
  start = c(2, 2),
  end = round(dim(maze)/2),
  quiet = TRUE,
  seed = 123,
  ...
)

Arguments

maze

Matrix. Using 0 for open space and 1 for walls.
start, end

Integer vector, length 2. Start and end coordinates.
inertia

Boolean. When enabled, algorithm will check for new directions only when impossible to continue in a straight line.
aim

Boolean. When enabled, algorithm will try first the directions closer to the end point, ranked and sorted by shorter distances.
diagonal

Boolean. When enabled, algorithm will have 8 degrees of freedom to move, if not, only 4 (up, down, left, right).
random

Boolean. When enabled, algorithm will pick next direction randomly.
timeout

Numeric. How many seconds set for timeout to force algorithm to stop trying new paths?
quiet

Boolean. Keep quiet? If not, print results
seed

Numeric. Seed to replicate random results.
...

Additional parameters passed to corr
x

maze_solve object

Value

List with data.frame containing solved solution, data.frame with path coordinates and directions, steps counter and turns counter.

See Also

Other Games: scrabble_dictionary(), sudoku_solver(), wordle_check()

Examples

micromouse <- matrix(c(
  1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
  1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
  1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,
  1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
  1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,
  1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1,
  1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1,
  1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1,
  1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1,
  1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1,
  1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
  1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
), nrow = 12, byrow = TRUE)
maze_solve(micromouse, start = c(2, 2), end = c(7, 7))

Calculate and Visualize Missingness

Description

This function lets the user calculate the percentage of NAs or missingness in a data.frame. It also plots the results if needed.

Usage

missingness(df, plot = FALSE, full = FALSE, subtitle = NA, summary = TRUE)

Arguments

df

Dataframe. Dataframe to study
plot

Boolean. Do you wish to plot results?
full

Boolean. Return all variables (or only with missings)?
subtitle

Character. Subtitle to show in plot
summary

Boolean. Show numerical summary text?

Value

data.frame with each variable, number of missing values and percentage. If plot=TRUE, a plot with the same information reflected.

See Also

Other Exploratory: corr_var(), crosstab(), df_str(), distr(), freqs(), freqs_df(), freqs_list(), freqs_plot(), lasso_vars(), plot_cats(), plot_df(), plot_nums(), tree_var()

Other Missing Values: impute()

Examples

Sys.unsetenv("LARES_FONT") # Temporal

# Dummy data
df <- data.frame(
  A = c(1:5),
  B = c(NA, NA, 1, 1, 1),
  C = rep(NA, 5),
  D = c(NA, LETTERS[1:4])
)

# Missing values summary
missingness(df)

# Visual results

missingness(df, plot = TRUE)

# Show all variables (including those with no missing values)
missingness(df, plot = TRUE, full = TRUE)

Model Metrics and Performance

Description

This function lets the user get a confusion matrix and accuracy, and for for binary classification models: AUC, Precision, Sensitivity, and Specificity, given the expected (tags) values and predicted values (scores).

Usage

model_metrics(
  tag,
  score,
  multis = NA,
  abc = TRUE,
  thresh = 10,
  auto_n = TRUE,
  thresh_cm = 0.5,
  target = "auto",
  type = "test",
  model_name = NA,
  plots = TRUE,
  quiet = FALSE,
  subtitle = NA
)

Arguments

tag

Vector. Real known label
score

Vector. Predicted value or model's result
multis

Data.frame. Containing columns with each category score (only used when more than 2 categories coexist)
abc

Boolean. Arrange columns and rows alphabetically when categorical values?
thresh

Integer. Threshold for selecting binary or regression models: this number is the threshold of unique values we should have in 'tag' (more than: regression; less than: classification)
auto_n

Add n_ before digits when it's categorical and not numerical, even though seems numerical?
thresh_cm

Numeric. Value to splits the results for the confusion matrix. Range of values: (0-1)
target

Value. Which is your target positive value? If set to 'auto', the target with largest mean(score) will be selected. Change the value to overwrite. Only used when binary categorical model.
type

Character. One of: "train", "test".
model_name

Character. Model's name for reference.
plots

Boolean. Create plots objects?
quiet

Boolean. Keep quiet? If not, informative messages will be shown.
subtitle

Character. Subtitle for plots

Value

List. Multiple performance metrics that vary depending on the type of model (classification or regression). If plot=TRUE, multiple plots are also returned.

See Also

Other Machine Learning: ROC(), conf_mat(), export_results(), gain_lift(), h2o_automl(), h2o_predict_MOJO(), h2o_selectmodel(), impute(), iter_seeds(), lasso_vars(), model_preprocess(), msplit()

Other Model metrics: ROC(), conf_mat(), errors(), gain_lift(), loglossBinary()

Other Calculus: corr(), corr_cross(), dist2d(), quants()

Examples

data(dfr) # Results for AutoML Predictions
lapply(dfr, head)

# Metrics for Binomial Model
met1 <- model_metrics(dfr$class2$tag, dfr$class2$scores,
  model_name = "Titanic Survived Model",
  plots = FALSE
)
print(met1)

# Metrics for Multi-Categorical Model
met2 <- model_metrics(dfr$class3$tag, dfr$class3$score,
  multis = subset(dfr$class3, select = -c(tag, score)),
  model_name = "Titanic Class Model",
  plots = FALSE
)
print(met2)

# Metrics for Regression Model
met3 <- model_metrics(dfr$regr$tag, dfr$regr$score,
  model_name = "Titanic Fare Model",
  plots = FALSE
)
print(met3)

Automate Data Preprocess for Modeling

Description

Pre-process your data before training a model. This is the prior step on the h2o_automl() function's pipeline. Enabling for other use cases when wanting too use any other framework, library, or custom algorithm.

Usage

model_preprocess(
  df,
  y = "tag",
  ignore = NULL,
  train_test = NA,
  split = 0.7,
  weight = NULL,
  target = "auto",
  balance = FALSE,
  impute = FALSE,
  no_outliers = TRUE,
  unique_train = TRUE,
  center = FALSE,
  scale = FALSE,
  thresh = 10,
  seed = 0,
  quiet = FALSE
)

Arguments

df

Dataframe. Dataframe containing all your data, including the dependent variable labeled as 'tag'. If you want to define which variable should be used instead, use the y parameter.
y

Character. Column name for dependent variable or response.
ignore

Character vector. Force columns for the model to ignore
train_test

Character. If needed, df's column name with 'test' and 'train' values to split
split

Numeric. Value between 0 and 1 to split as train/test datasets. Value is for training set. Set value to 1 to train with all available data and test with same data (cross-validation will still be used when training). If train_test is set, value will be overwritten with its real split rate.
weight

Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed.
target

Value. Which is your target positive value? If set to 'auto', the target with largest mean(score) will be selected. Change the value to overwrite. Only used when binary categorical model.
balance

Boolean. Auto-balance train dataset with under-sampling?
impute

Boolean. Fill NA values with MICE?
no_outliers

Boolean/Numeric. Remove y's outliers from the dataset? Will remove those values that are farther than n standard deviations from the dependent variable's mean (Z-score). Set to TRUE for default (3) or numeric to set a different multiplier.
unique_train

Boolean. Keep only unique row observations for training data?
center, scale

Boolean. Using the base function scale, do you wish to center and/or scale all numerical values?
thresh

Integer. Threshold for selecting binary or regression models: this number is the threshold of unique values we should have in 'tag' (more than: regression; less than: classification)
seed

Integer. Set a seed for reproducibility. AutoML can only guarantee reproducibility if max_models is used because max_time is resource limited.
quiet

Boolean. Keep quiet? If not, informative messages will be shown.

Value

List. Contains original data.frame df, an index to identify which observations with be part of the train dataset train_index, and which model type should be model_type.

See Also

Other Machine Learning: ROC(), conf_mat(), export_results(), gain_lift(), h2o_automl(), h2o_predict_MOJO(), h2o_selectmodel(), impute(), iter_seeds(), lasso_vars(), model_metrics(), msplit()

Examples

data(dft) # Titanic dataset

model_preprocess(dft, "Survived", balance = TRUE)

model_preprocess(dft, "Fare", split = 0.5, scale = TRUE)

model_preprocess(dft, "Pclass", ignore = c("Fare", "Cabin"))

model_preprocess(dft, "Pclass", quiet = TRUE)

Move files from A to B

Description

Move one or more files from a directory to another using R.

Usage

move_files(from, to)

Arguments

from

Character. File names and directories. All files will be moved recursively.
to

Character. File names for each from file or directory. If directory does not exist, it will be created.

Value

No return value, called for side effects.

See Also

Other Tools: autoline(), bind_files(), bring_api(), cal_split(), chr2num(), db_download(), db_upload(), dont_sleep(), export_plot(), export_results(), files_functions(), font_exists(), formatColoured(), formatHTML(), glued(), grepm(), h2o_selectmodel(), haveInternet(), image_metadata(), importxlsx(), ip_data(), json2vector(), list_cats(), listfiles(), mail_send(), markdown2df(), msplit(), myip(), quiet(), read.file(), statusbar(), tic(), try_require(), updateLares(), warnifnot(), what_size()

Download MP3 from URL

Description

This function downloads YouTube videos or Soundcloud or any other platform supported by the youtube-dl library, and converts them into high quality MP3 files. The URL can be for a single video or a whole playlist. It also returns metadata into an (invisible) list.

Usage

mp3_get(
  id,
  mp3 = TRUE,
  repo = "yt-dlp",
  params = "--no-check-certificate",
  start_time = 0,
  end_time = NA,
  overwrite = TRUE,
  open = FALSE,
  delete = open,
  info = TRUE,
  title = NULL,
  cover = FALSE,
  metadata = FALSE,
  quiet = FALSE
)

Arguments

id

Character. YouTube URL or ID to search for.
mp3

Boolean. Add mp3 optimal parameters?
repo

Character. Chose repository you installed youtube-dl from. Any of: "youtube-dl" (latest stable version), "yt-dlp" (latest dev version).
params

Character. Additional parameters.
start_time, end_time

Numeric. Start and end time to trim the audio output in seconds.
overwrite

Boolean. Overwrite original file?
open, delete

Boolean. After everything's done, should the file be opened? Should it be deleted?
info

Boolean. Import and return metadata?
title

Character. Title of the track to search for cover, metadata, and name the file.
cover

Boolean. Google Search its squared cover? Uses title input when provided.
metadata

Boolean. Use iTunes Search API (free) to extract "track" data and high-res cover art. Falls back to "spotifyr" if no results are found (requires credentials and, since 2026-03-09, Spotify Premium linked to the client id). Uses title input when provided.
quiet

Boolean. Keep quiet? If not, informative messages will be shown.

Value

(Invisible) list with id's meta-data.

youtube-dl

More info from the original developers and its code: youtube-dl's Github

See Also

Other Scrapper: filesGD(), gtrends_related(), holidays(), ip_data(), readGS(), splot_summary(), stocks_quote()

Other Audio: mp3_trim(), mp3_update_tags()

Examples

# You must have "youtube-dl" installed in your OS:
## Not run: 
# Download video from YouTube and convert to MP3
mp3_get("https://www.youtube.com/watch?v=lrlKcCdVw9Q")
# OR simply
mp3_get("lrlKcCdVw9Q")
# For dev version, use:
mp3_get("m3RX4LJh0iI", repo = "yt-dlp")

## End(Not run)

Trim MP3 Audio File

Description

This function trims MP3 files given a start and/or end numeric timestamp. Requires ffmpeg installed in your machine.

Usage

mp3_trim(
  file,
  start_time = 0,
  end_time = NA,
  overwrite = FALSE,
  ext = "mp3",
  quiet = FALSE
)

Arguments

file

Character. File name to trim.
start_time, end_time

Numeric. Start and end time to trim the audio output in seconds.
overwrite

Boolean. Overwrite original file?
ext

Character. File extension/type.
quiet

Boolean. Keep quiet? If not, informative messages will be shown.

See Also

Other Audio: mp3_get(), mp3_update_tags()

Update MP3 Metadata Tags

Description

Updates the ID3 metadata tags of an MP3 file using the Python 'eyeD3' library via the 'reticulate' package. You can modify standard fields such as title, artist, album, and genre, as well as additional tags passed through ... if they exist in the MP3 file.

Usage

mp3_update_tags(
  filename,
  title = NULL,
  artist = NULL,
  album = NULL,
  genre = NULL,
  ...
)

Arguments

filename

Character. Path to the MP3 file to update.
title

Character. New title of the track (optional).
artist

Character. New artist name (optional).
album

Character. New album name (optional).
genre

Character. New genre name (optional).
...

Additional named arguments corresponding to other ID3 tags. Only tags that exist in the MP3 file will be updated.

Details

The function requires Python and the eyeD3 package installed. It will automatically initialize ID3 tags if they do not exist. Tags provided via ... are checked against the existing tag names to avoid errors.

Value

Invisibly returns NULL. The MP3 file is updated in-place.

See Also

Other Audio: mp3_get(), mp3_trim()

Examples

## Not run: 
mp3_update_tags(
  "song.mp3",
  title = "My Jazz Song",
  artist = "Bernardo",
  album = "Smooth Album",
  genre = "Jazz"
)

## End(Not run)

Confussion Matrix Plot

Description

This function plots a confussion matrix.

Usage

mplot_conf(
  tag,
  score,
  thresh = 0.5,
  abc = TRUE,
  squared = FALSE,
  diagonal = TRUE,
  top = 20,
  subtitle = NA,
  model_name = NULL,
  save = FALSE,
  subdir = NA,
  file_name = "viz_conf_mat.png"
)

Arguments

tag

Vector. Real known label.
score

Vector. Predicted value or model's result.
thresh

Integer. Threshold for selecting binary or regression models: this number is the threshold of unique values we should have in 'tag' (more than: regression; less than: classification)
abc

Boolean. Arrange columns and rows alphabetically?
squared

Boolean. Force plot to be squared?
diagonal

Boolean. FALSE to convert diagonal numbers to zeroes. Ideal to detect must confusing categories.
top

Integer. Plot only the most n frequent variables. Set to NA to plot all.
subtitle

Character. Subtitle to show in plot
model_name

Character. Model's name
save

Boolean. Save output plot into working directory
subdir

Character. Sub directory on which you wish to save the plot
file_name

Character. File name as you wish to save the plot

Details

You may use conf_mat() to get calculate values.

Value

Plot with confusion matrix results.

See Also

Other ML Visualization: mplot_cuts(), mplot_cuts_error(), mplot_density(), mplot_full(), mplot_gain(), mplot_importance(), mplot_lineal(), mplot_metrics(), mplot_response(), mplot_roc(), mplot_splits(), mplot_topcats()

Examples

Sys.unsetenv("LARES_FONT") # Temporal
data(dfr) # Results for AutoML Predictions
lapply(dfr, head)

# Plot for Binomial Model
mplot_conf(dfr$class2$tag, dfr$class2$scores,
  model_name = "Titanic Survived Model"
)

# Plot for Multi-Categorical Model
mplot_conf(dfr$class3$tag, dfr$class3$score,
  model_name = "Titanic Class Model"
)

Cuts by quantiles for score plot

Description

This function cuts by quantiles any score or prediction.

Usage

mplot_cuts(
  score,
  splits = 10,
  model_name = NA,
  subtitle = NA,
  table = FALSE,
  save = FALSE,
  subdir = NA,
  file_name = "viz_ncuts.png"
)

Arguments

score

Vector. Predicted value or model's result.
splits

Integer. Numer of separations to plot
model_name

Character. Model's name
subtitle

Character. Subtitle to show in plot
table

Boolean. Do you wish to return a table with results?
save

Boolean. Save output plot into working directory
subdir

Character. Sub directory on which you wish to save the plot
file_name

Character. File name as you wish to save the plot

Value

Plot with performance results by cuts.

See Also

Other ML Visualization: mplot_conf(), mplot_cuts_error(), mplot_density(), mplot_full(), mplot_gain(), mplot_importance(), mplot_lineal(), mplot_metrics(), mplot_response(), mplot_roc(), mplot_splits(), mplot_topcats()

Examples

Sys.unsetenv("LARES_FONT") # Temporal
data(dfr) # Results for AutoML Predictions
head(dfr$class2)

# Data
mplot_cuts(dfr$class2$scores, splits = 5, table = TRUE)

# Plot
mplot_cuts(dfr$class2$scores, model_name = "Titanic Survived Model")

Cuts by quantiles on absolute and percentual errors plot

Description

This function cuts by quantiles on absolute and percentual errors

Usage

mplot_cuts_error(
  tag,
  score,
  splits = 10,
  title = NA,
  model_name = NA,
  save = FALSE,
  subdir = NA,
  file_name = "viz_ncuts_error.png"
)

Arguments

tag

Vector. Real known label.
score

Vector. Predicted value or model's result.
splits

Integer. Number of separations to plot
title

Character. Title to show in plot
model_name

Character. Model's name
save

Boolean. Save output plot into working directory
subdir

Character. Sub directory on which you wish to save the plot
file_name

Character. File name as you wish to save the plot

Value

Plot with error results by cuts.

See Also

Other ML Visualization: mplot_conf(), mplot_cuts(), mplot_density(), mplot_full(), mplot_gain(), mplot_importance(), mplot_lineal(), mplot_metrics(), mplot_response(), mplot_roc(), mplot_splits(), mplot_topcats()

Examples

Sys.unsetenv("LARES_FONT") # Temporal
data(dfr) # Results for AutoML Predictions
head(dfr$regr)
mplot_cuts_error(dfr$regr$tag, dfr$regr$score,
  model_name = "Titanic Fare Model"
)

Density plot for discrete and continuous values

Description

This function plots discrete and continuous values results

Usage

mplot_density(
  tag,
  score,
  thresh = 6,
  model_name = NA,
  subtitle = NA,
  save = FALSE,
  subdir = NA,
  file_name = "viz_distribution.png"
)

Arguments

tag

Vector. Real known label
score

Vector. Predicted value or model's result
thresh

Integer. Threshold for selecting binary or regression models: this number is the threshold of unique values we should have in 'tag' (more than: regression; less than: classification)
model_name

Character. Model's name
subtitle

Character. Subtitle to show in plot
save

Boolean. Save output plot into working directory
subdir

Character. Sub directory on which you wish to save the plot
file_name

Character. File name as you wish to save the plot

Value

Plot with distribution and performance results.

See Also

Other ML Visualization: mplot_conf(), mplot_cuts(), mplot_cuts_error(), mplot_full(), mplot_gain(), mplot_importance(), mplot_lineal(), mplot_metrics(), mplot_response(), mplot_roc(), mplot_splits(), mplot_topcats()

Examples

Sys.unsetenv("LARES_FONT") # Temporal
data(dfr) # Results for AutoML Predictions
lapply(dfr[c(1, 3)], head)

# Plot for binomial results
mplot_density(dfr$class2$tag, dfr$class2$scores, subtitle = "Titanic Survived Model")

# Plot for regression results
mplot_density(dfr$regr$tag, dfr$regr$score, model_name = "Titanic Fare Model")

MPLOTS Score Full Report Plots

Description

This function plots a whole dashboard with a model's results. It will automatically detect if it's a categorical or regression's model by checking how many different unique values the dependent variable (tag) has.

Usage

mplot_full(
  tag,
  score,
  multis = NA,
  splits = 8,
  thresh = 6,
  subtitle = NA,
  model_name = NA,
  plot = TRUE,
  save = FALSE,
  subdir = NA,
  file_name = "viz_full.png"
)

Arguments

tag

Vector. Real known label.
score

Vector. Predicted value or model's result.
multis

Data.frame. Containing columns with each category probability or score (only used when more than 2 categories coexist).
splits

Integer. Number of separations to plot
thresh

Integer. Threshold for selecting binary or regression models: this number is the threshold of unique values we should have in 'tag' (more than: regression; less than: classification)
subtitle

Character. Subtitle to show in plot
model_name

Character. Model's name
plot

Boolean. Plot results? If not, plot grid object returned
save

Boolean. Save output plot into working directory
subdir

Character. Sub directory on which you wish to save the plot
file_name

Character. File name as you wish to save the plot

Value

Multiple plots gathered into one, showing tag vs score performance results.

See Also

Other ML Visualization: mplot_conf(), mplot_cuts(), mplot_cuts_error(), mplot_density(), mplot_gain(), mplot_importance(), mplot_lineal(), mplot_metrics(), mplot_response(), mplot_roc(), mplot_splits(), mplot_topcats()

Examples

Sys.unsetenv("LARES_FONT") # Temporal
data(dfr) # Results for AutoML Predictions
lapply(dfr, head)

# Dasboard for Binomial Model
mplot_full(dfr$class2$tag, dfr$class2$scores,
  model_name = "Titanic Survived Model"
)

# Dasboard for Multi-Categorical Model
mplot_full(dfr$class3$tag, dfr$class3$score,
  multis = subset(dfr$class3, select = -c(tag, score)),
  model_name = "Titanic Class Model"
)

# Dasboard for Regression Model
mplot_full(dfr$regr$tag, dfr$regr$score,
  model_name = "Titanic Fare Model"
)

Cumulative Gain Plot

Description

The cumulative gains plot, often named ‘gains plot’, helps us answer the question: When we apply the model and select the best X deciles, what expect to target? The cumulative gains chart shows the percentage of the overall number of cases in a given category "gained" by targeting a percentage of the total number of cases.

Usage

mplot_gain(
  tag,
  score,
  multis = NA,
  target = "auto",
  splits = 10,
  highlight = "auto",
  caption = NA,
  save = FALSE,
  subdir = NA,
  file_name = "viz_gain.png",
  quiet = FALSE
)

Arguments

tag

Vector. Real known label.
score

Vector. Predicted value or model's result.
multis

Data.frame. Containing columns with each category probability or score (only used when more than 2 categories coexist).
target

Value. Which is your target positive value? If set to 'auto', the target with largest mean(score) will be selected. Change the value to overwrite. Only works for binary classes
splits

Integer. Numer of quantiles to split the data
highlight

Character or Integer. Which split should be used for the automatic conclussion in the plot? Set to "auto" for best value, "none" to turn off or the number of split.
caption

Character. Caption to show in plot
save

Boolean. Save output plot into working directory
subdir

Character. Sub directory on which you wish to save the plot
file_name

Character. File name as you wish to save the plot
quiet

Boolean. Keep quiet? If not, informative messages will be shown.

Value

Plot with gain and performance results by cuts.

See Also

Other ML Visualization: mplot_conf(), mplot_cuts(), mplot_cuts_error(), mplot_density(), mplot_full(), mplot_importance(), mplot_lineal(), mplot_metrics(), mplot_response(), mplot_roc(), mplot_splits(), mplot_topcats()

Examples

Sys.unsetenv("LARES_FONT") # Temporal
data(dfr) # Results for AutoML Predictions
lapply(dfr, head)

# Plot for Binomial Model
mplot_gain(dfr$class2$tag, dfr$class2$scores,
  caption = "Titanic Survived Model",
  target = "FALSE"
)
mplot_gain(dfr$class2$tag, dfr$class2$scores,
  caption = "Titanic Survived Model",
  target = "TRUE"
)

# Plot for Multi-Categorical Model
mplot_gain(dfr$class3$tag, dfr$class3$score,
  multis = subset(dfr$class3, select = -c(tag, score)),
  caption = "Titanic Class Model"
)

Variables Importances Plot

Description

This function plots Variable Importances

Usage

mplot_importance(
  var,
  imp,
  colours = NA,
  limit = 15,
  model_name = NA,
  subtitle = NA,
  save = FALSE,
  subdir = NA,
  file_name = "viz_importance.png"
)

Arguments

var

Vector. Variable or column's names
imp

Vector. Importance of said variables. Must have same length as var
colours

If positive and negative contribution is known
limit

Integer. Limit how many variables you wish to plot
model_name

Character. Model's name
subtitle

Character. Subtitle to show in plot
save

Boolean. Save output plot into working directory
subdir

Character. Sub directory on which you wish to save the plot
file_name

Character. File name as you wish to save the plot

Value

Plot with ranked importance variables results.

See Also

Other ML Visualization: mplot_conf(), mplot_cuts(), mplot_cuts_error(), mplot_density(), mplot_full(), mplot_gain(), mplot_lineal(), mplot_metrics(), mplot_response(), mplot_roc(), mplot_splits(), mplot_topcats()

Examples

Sys.unsetenv("LARES_FONT") # Temporal
df <- data.frame(
  variable = LETTERS[1:6],
  importance = c(4, 6, 6.7, 3, 4.8, 6.2) / 100,
  positive = c(TRUE, TRUE, FALSE, TRUE, FALSE, FALSE)
)
head(df)

mplot_importance(
  var = df$variable,
  imp = df$importance,
  model_name = "Random values model"
)

# Add a colour for categories
mplot_importance(
  var = df$variable,
  imp = df$importance,
  colours = df$positive,
  limit = 4
)

Linear Regression Results Plot

Description

This function plots a Linear Regression Result

Usage

mplot_lineal(
  tag,
  score,
  subtitle = NA,
  model_name = NA,
  save = FALSE,
  subdir = NA,
  file_name = "viz_lineal.png"
)

Arguments

tag

Vector. Real known label.
score

Vector. Predicted value or model's result.
subtitle

Character. Subtitle to show in plot
model_name

Character. Model's name
save

Boolean. Save output plot into working directory
subdir

Character. Sub directory on which you wish to save the plot
file_name

Character. File name as you wish to save the plot

Value

Plot with linear distribution and performance results.

See Also

Other ML Visualization: mplot_conf(), mplot_cuts(), mplot_cuts_error(), mplot_density(), mplot_full(), mplot_gain(), mplot_importance(), mplot_metrics(), mplot_response(), mplot_roc(), mplot_splits(), mplot_topcats()

Examples

Sys.unsetenv("LARES_FONT") # Temporal
data(dfr) # Results for AutoML Predictions
lapply(dfr, head)
mplot_lineal(dfr$regr$tag, dfr$regr$score, model_name = "Titanic Fare Model")

Model Metrics and Performance Plots

Description

This function generates plots of the metrics of a predictive model. This is an auxiliary function used in model_metrics() when the parameter plot is set to TRUE.

Usage

mplot_metrics(
  results,
  subtitle = NA,
  model_name = NA,
  save = FALSE,
  subdir = NA,
  file_name = "viz_metrics.png"
)

Arguments

results

Object. Results object from h2o_automl function
subtitle

Character. Subtitle to show in plot
model_name

Character. Model's name
save

Boolean. Save output plot into working directory
subdir

Character. Sub directory on which you wish to save the plot
file_name

Character. File name as you wish to save the plot

Value

Plot with results performance.

See Also

Other ML Visualization: mplot_conf(), mplot_cuts(), mplot_cuts_error(), mplot_density(), mplot_full(), mplot_gain(), mplot_importance(), mplot_lineal(), mplot_response(), mplot_roc(), mplot_splits(), mplot_topcats()

Cumulative Response Plot

Description

The response gains plot helps us answer the question: When we apply the model and select up until ntile X, what is the expected

Usage

mplot_response(
  tag,
  score,
  multis = NA,
  target = "auto",
  splits = 10,
  highlight = "auto",
  caption = NA,
  save = FALSE,
  subdir = NA,
  file_name = "viz_response.png",
  quiet = FALSE
)

Arguments

tag

Vector. Real known label.
score

Vector. Predicted value or model's result.
multis

Data.frame. Containing columns with each category probability or score (only used when more than 2 categories coexist).
target

Value. Which is your target positive value? If set to 'auto', the target with largest mean(score) will be selected. Change the value to overwrite. Only works for binary classes
splits

Integer. Numer of quantiles to split the data
highlight

Character or Integer. Which split should be used for the automatic conclussion in the plot? Set to "auto" for best value, "none" to turn off or the number of split.
caption

Character. Caption to show in plot
save

Boolean. Save output plot into working directory
subdir

Character. Sub directory on which you wish to save the plot
file_name

Character. File name as you wish to save the plot
quiet

Boolean. Keep quiet? If not, informative messages will be shown.

Value

Plot with cumulative response and performance results by cuts.

See Also

Other ML Visualization: mplot_conf(), mplot_cuts(), mplot_cuts_error(), mplot_density(), mplot_full(), mplot_gain(), mplot_importance(), mplot_lineal(), mplot_metrics(), mplot_roc(), mplot_splits(), mplot_topcats()

Examples

Sys.unsetenv("LARES_FONT") # Temporal
data(dfr) # Results for AutoML Predictions
lapply(dfr, head)

# Plot for Binomial Model
mplot_response(dfr$class2$tag, dfr$class2$scores,
  caption = "Titanic Survived Model",
  target = "TRUE"
)
mplot_response(dfr$class2$tag, dfr$class2$scores,
  caption = "Titanic Survived Model",
  target = "FALSE"
)

# Plot for Multi-Categorical Model
mplot_response(dfr$class3$tag, dfr$class3$score,
  multis = subset(dfr$class3, select = -c(tag, score)),
  caption = "Titanic Class Model"
)

ROC Curve Plot

Description

This function plots ROC Curves with AUC values with 95% confidence range. It also works for multi-categorical models.

Usage

mplot_roc(
  tag,
  score,
  multis = NA,
  sample = 1000,
  model_name = NA,
  subtitle = NA,
  interval = 0.2,
  squared = TRUE,
  plotly = FALSE,
  save = FALSE,
  subdir = NA,
  file_name = "viz_roc.png"
)

Arguments

tag

Vector. Real known label.
score

Vector. Predicted value or model's result.
multis

Data.frame. Containing columns with each category probability or score (only used when more than 2 categories coexist).
sample

Integer. Number of samples to use for rendering plot.
model_name

Character. Model's name
subtitle

Character. Subtitle to show in plot
interval

Numeric. Interval for breaks in plot
squared

Boolean. Keep proportions?
plotly

Boolean. Use plotly for plot's output for an interactive plot
save

Boolean. Save output plot into working directory
subdir

Character. Sub directory on which you wish to save the plot
file_name

Character. File name as you wish to save the plot

Value

Plot with ROC curve and AUC performance results.

See Also

Other ML Visualization: mplot_conf(), mplot_cuts(), mplot_cuts_error(), mplot_density(), mplot_full(), mplot_gain(), mplot_importance(), mplot_lineal(), mplot_metrics(), mplot_response(), mplot_splits(), mplot_topcats()

Examples

Sys.unsetenv("LARES_FONT") # Temporal
data(dfr) # Results for AutoML Predictions
lapply(dfr[c(1, 2)], head)

# ROC Curve for Binomial Model
mplot_roc(dfr$class2$tag, dfr$class2$scores,
  model_name = "Titanic Survived Model"
)

# ROC Curves for Multi-Categorical Model
mplot_roc(dfr$class3$tag, dfr$class3$score,
  multis = subset(dfr$class3, select = -c(tag, score)),
  squared = FALSE,
  model_name = "Titanic Class Model"
)

Split and compare quantiles plot

Description

This function lets us split and compare quantiles on a given prediction to compare different categorical values vs scores grouped by equal sized buckets.

Usage

mplot_splits(
  tag,
  score,
  splits = 5,
  subtitle = NA,
  model_name = NA,
  save = FALSE,
  subdir = NA,
  file_name = "viz_splits.png"
)

Arguments

tag

Vector. Real known label.
score

Vector. Predicted value or model's result.
splits

Integer. Number of separations to plot
subtitle

Character. Subtitle to show in plot
model_name

Character. Model's name
save

Boolean. Save output plot into working directory
subdir

Character. Sub directory on which you wish to save the plot
file_name

Character. File name as you wish to save the plot

Value

Plot with distribution and performance results by splits.

See Also

Other ML Visualization: mplot_conf(), mplot_cuts(), mplot_cuts_error(), mplot_density(), mplot_full(), mplot_gain(), mplot_importance(), mplot_lineal(), mplot_metrics(), mplot_response(), mplot_roc(), mplot_topcats()

Examples

Sys.unsetenv("LARES_FONT") # Temporal
data(dfr) # Results for AutoML Predictions
lapply(dfr, head)

# For categorical (binary) values
mplot_splits(dfr$class2$tag, dfr$class2$scores,
  splits = 4,
  model_name = "Titanic Survived Model"
)

# For categorical (+2) values
mplot_splits(dfr$class3$tag, dfr$class2$scores,
  model_name = "Titanic Class Model"
)

# For continuous values
mplot_splits(dfr$regr$tag, dfr$regr$score,
  splits = 4,
  model_name = "Titanic Fare Model"
)

Top Hit Ratios for Multi-Classification Models

Description

Calculate and plot a multi-class model's predictions accuracy based on top N predictions and distribution of probabilities.

Usage

mplot_topcats(tag, score, multis, model_name = NA)

Arguments

tag

Vector. Real known label.
score

Vector. Predicted value or model's result.
multis

Data.frame. Containing columns with each category probability or score (only used when more than 2 categories coexist).
model_name

Character. Model's name

Value

Plot with performance results over most frequent categories.

See Also

Other ML Visualization: mplot_conf(), mplot_cuts(), mplot_cuts_error(), mplot_density(), mplot_full(), mplot_gain(), mplot_importance(), mplot_lineal(), mplot_metrics(), mplot_response(), mplot_roc(), mplot_splits()

Examples

Sys.unsetenv("LARES_FONT") # Temporal
data(dfr) # Results for AutoML Predictions
mplot_topcats(dfr$class3$tag, dfr$class3$score,
  multis = subset(dfr$class3, select = -c(tag, score)),
  model_name = "Titanic Class Model"
)

Split a dataframe for training and testing sets

Description

This function splits automatically a dataframe into train and test datasets. You can define a seed to get the same results every time, but has a default value. You can prevent it from printing the split counter result.

Usage

msplit(df, size = 0.7, seed = 0, print = TRUE)

Arguments

df

Dataframe
size

Numeric. Split rate value, between 0 and 1. If set to 1, the train and test set will be the same.
seed

Integer. Seed for random split
print

Boolean. Print summary results?

Value

List with both datasets, summary, and split rate.

See Also

Other Machine Learning: ROC(), conf_mat(), export_results(), gain_lift(), h2o_automl(), h2o_predict_MOJO(), h2o_selectmodel(), impute(), iter_seeds(), lasso_vars(), model_metrics(), model_preprocess()

Other Tools: autoline(), bind_files(), bring_api(), cal_split(), chr2num(), db_download(), db_upload(), dont_sleep(), export_plot(), export_results(), files_functions(), font_exists(), formatColoured(), formatHTML(), glued(), grepm(), h2o_selectmodel(), haveInternet(), image_metadata(), importxlsx(), ip_data(), json2vector(), list_cats(), listfiles(), mail_send(), markdown2df(), move_files(), myip(), quiet(), read.file(), statusbar(), tic(), try_require(), updateLares(), warnifnot(), what_size()

Examples

data(dft) # Titanic dataset
splits <- msplit(dft, size = 0.7, seed = 123)
names(splits)

What's my IP?

Description

Reveal your current IP address.

Usage

myip()

Value

Character. Result of your IP address based on ipify.org

See Also

Other Tools: autoline(), bind_files(), bring_api(), cal_split(), chr2num(), db_download(), db_upload(), dont_sleep(), export_plot(), export_results(), files_functions(), font_exists(), formatColoured(), formatHTML(), glued(), grepm(), h2o_selectmodel(), haveInternet(), image_metadata(), importxlsx(), ip_data(), json2vector(), list_cats(), listfiles(), mail_send(), markdown2df(), move_files(), msplit(), quiet(), read.file(), statusbar(), tic(), try_require(), updateLares(), warnifnot(), what_size()

Examples

myip()

Build N-grams and keep most frequent

Description

Build out n-grams for multiple text inputs and keep the n most frequent combinations.

Usage

ngrams(text, ngram = c(2, 3), top = 10, stop_words = NULL, ...)

Arguments

text

Character vector
ngram

Integer vector. Number of continuous n items in text.
top

Integer. Keep n most frequent ngrams only.
stop_words

Character vector. Words to exclude from text. Example: if you want to exclude "a", whenever that word appears it will be excluded, but when the letter "a" appears in a word, it will remain.
...

Additional parameters passed to remove_stopwords.

Value

data.frame with ngrams and counters, sorted by frequency.

See Also

Other Text Mining: cleanText(), remove_stopwords(), replaceall(), sentimentBreakdown(), textCloud(), textFeats(), textTokenizer(), topics_rake()

Examples

# You must have "tidytext" library to use this auxiliary function:
## Not run: 
women <- read.csv("https://bit.ly/3mXJOOi")
x <- women$description
ngrams(x, ngram = c(2, 3), top = 3)
ngrams(x, ngram = 2, top = 6, stop_words = c("a", "is", "of", "the"))

## End(Not run)

Plot Result with Nothing to Plot

Description

This function lets the user print a plot without plot, with a customizable message. It is quite useful for Shiny renderPlot when using filters and no data is returned.

Usage

noPlot(message = "Nothing to show here!", size = 4.5, ...)

Arguments

message

Character. What message do you wish to show?
size

Numeric. Font size for message input.
...

Additional parameters passed to theme_lares().

Value

Empty ggplot2 object (with a message if set).

See Also

Other Visualization: distr(), freqs(), freqs_df(), freqs_list(), freqs_plot(), plot_chord(), plot_survey(), plot_timeline(), tree_var()

Examples

Sys.unsetenv("LARES_FONT") # Temporal
noPlot(message = "No plot to show!")
noPlot(background = "#FF5500", size = 7)

Normalize Vector

Description

This function normalizes numerical values into a specified range, defaulting to the 0 to 1 range.

Usage

normalize(x, range = c(0, 1), ...)

Arguments

x

Numeric vector. The numbers to be normalized.
range

A numeric vector of length 2 specifying the desired range for normalization. Default is c(0, 1).
...

Additional parameters.

Value

A numeric vector with normalized x values.

See Also

Other Data Wrangling: balance_data(), categ_reducer(), cleanText(), date_cuts(), date_feats(), file_name(), formatHTML(), holidays(), impute(), left(), num_abbr(), ohe_commas(), ohse(), quants(), removenacols(), replaceall(), replacefactor(), textFeats(), textTokenizer(), vector2text(), year_month(), zerovar()

Examples

x <- c(0, 1, 4, 7.5, 10)
normalize(x)
normalize(x, range = c(-1, 1))

Abbreviate or dis-abbreviate numerical values

Description

This function converts a numeric vector's values into their abbreviated character equivalent, i.e. 100E6 into 100M and viceversa.

Usage

num_abbr(x, n = 3, numeric = FALSE, ...)

Arguments

x

Numeric vector
n

Integer. Single numeric value, specifying number of significant figures to show. Range 1 to 6.
numeric

Boolean. Transform abbreviated number into numeric?
...

Additional parameters.

Value

Vector of character or numeric values that contain converted values

See Also

Other Data Wrangling: balance_data(), categ_reducer(), cleanText(), date_cuts(), date_feats(), file_name(), formatHTML(), holidays(), impute(), left(), normalize(), ohe_commas(), ohse(), quants(), removenacols(), replaceall(), replacefactor(), textFeats(), textTokenizer(), vector2text(), year_month(), zerovar()

Examples

num_abbr(rnorm(10) * 1e6)
num_abbr(rnorm(10) * 1e6, n = 1)
num_abbr(c("3K", "-58.3M", NA, 1), numeric = TRUE)

One Hot Encoding for a Vector with Comma Separated Values

Description

This function lets the user do one hot encoding on a variable with comma separated values

Usage

ohe_commas(df, ..., sep = ",", noval = "NoVal", remove = FALSE)

Arguments

df

Dataframe. May contain one or more columns with comma separated values which will be separated as one hot encoding
...

Variables. Which variables to split into new columns?
sep

Character. Which regular expression separates the elements?
noval

Character. No value text
remove

Boolean. Remove original variables?

Value

data.frame on which all features are numerical by nature or transformed with one hot encoding.

See Also

Other Data Wrangling: balance_data(), categ_reducer(), cleanText(), date_cuts(), date_feats(), file_name(), formatHTML(), holidays(), impute(), left(), normalize(), num_abbr(), ohse(), quants(), removenacols(), replaceall(), replacefactor(), textFeats(), textTokenizer(), vector2text(), year_month(), zerovar()

Other One Hot Encoding: date_feats(), holidays(), ohse()

Examples

df <- data.frame(
  id = c(1:5),
  x = c("AA, D", "AA,B", "B,  D", "A,D,B", NA),
  z = c("AA+BB+AA", "AA", "BB,  AA", NA, "BB+AA")
)
ohe_commas(df, x, remove = TRUE)
ohe_commas(df, z, sep = "\\+")
ohe_commas(df, x, z)

One Hot Smart Encoding (Dummy Variables)

Description

This function lets the user automatically transform a dataframe with categorical columns into numerical by one hot encoding technic.

Usage

ohse(
  df,
  redundant = FALSE,
  drop = TRUE,
  ignore = NULL,
  dates = FALSE,
  holidays = FALSE,
  country = "Venezuela",
  currency_pair = NA,
  trim = 0,
  limit = 10,
  variance = 0.9,
  other_label = "OTHER",
  sep = "_",
  quiet = FALSE,
  ...
)

Arguments

df

Dataframe
redundant

Boolean. Should we keep redundant columns? i.e. If the column only has two different values, should we keep both new columns? Is set to NULL, only binary variables will dump redundant columns.
drop

Boolean. Drop automatically some useless features?
ignore

Vector or character. Which column should be ignored?
dates

Boolean. Do you want the function to create more features out of the date/time columns?
holidays

Boolean. Include holidays as new columns?
country

Character or vector. For which countries should the holidays be included?
currency_pair

Character. Which currency exchange do you wish to get the history from? i.e, USD/COP, EUR/USD...
trim

Integer. Trim names until the nth character
limit

Integer. Limit one hot encoding to the n most frequent values of each column. Set to NA to ignore argument.
variance

Numeric. Drop columns with more than n variance. Range: 0-1. For example: if a variable contains 91 unique different values out of 100 observations, this column will be suppressed if value is set to 0.9
other_label

Character. With which text do you wish to replace the filtered values with?
sep

Character. Separator's string
quiet

Boolean. Keep quiet? If not, informative messages will be shown.
...

Additional parameters.

Value

data.frame on which all features are numerical by nature or transformed with one hot encoding.

See Also

Other Data Wrangling: balance_data(), categ_reducer(), cleanText(), date_cuts(), date_feats(), file_name(), formatHTML(), holidays(), impute(), left(), normalize(), num_abbr(), ohe_commas(), quants(), removenacols(), replaceall(), replacefactor(), textFeats(), textTokenizer(), vector2text(), year_month(), zerovar()

Other Feature Engineering: date_feats(), holidays()

Other One Hot Encoding: date_feats(), holidays(), ohe_commas()

Examples

data(dft)
dft <- dft[, c(2, 3, 5, 9, 11)]

ohse(dft, limit = 3) %>% head(3)
ohse(dft, limit = 3, redundant = NULL) %>% head(3)

# Getting rid of columns with no (or too much) variance
dft$no_variance1 <- 0
dft$no_variance2 <- c("A", rep("B", nrow(dft) - 1))
dft$no_variance3 <- as.character(rnorm(nrow(dft)))
dft$no_variance4 <- c(rep("A", 20), round(rnorm(nrow(dft) - 20), 4))
ohse(dft, limit = 3) %>% head(3)

Outliers: Tukey’s fences

Description

Tukey’s fences is a technique used in box plots. The non-outlier range is defined with [Q1-k(Q3-Q1), Q3+k(Q3-Q1)], where Q1 and Q3 are the lower and upper quartiles respectively, k - some non-negative constant (popular choice is 1.5). A value is an outlier based on Tukey’s fences when its value does not lie in non-outlier range.

Usage

outlier_tukey(x, k = 1.5)

outlier_turkey(x, k = 1.5)

Arguments

x

Numeric. Distribution
k

Positive Numeric. K-multiplier.

Value

Boolean vector detecting outliers.

See Also

Other Outliers: outlier_zscore(), outlier_zscore_plot(), winsorize()

Outliers: Z-score method

Description

Z-score, also called a standard score, of an observation is a distance from the population center measured in number of normalization units. The default choice for center is sample mean and for normalization unit is standard deviation. Values are considered outliers based on z-score if its absolute value of default z-score is higher then the threshold (popular choice is 3).

Usage

outlier_zscore(x, thresh = 3, mad = FALSE)

Arguments

x

Numeric. Distribution
thresh

Numeric. Z-Score threshold for n standard deviations.
mad

Boolean. Use median absolute deviation instead?

Value

data.frame. Each row is an x observation with its respective std/mean or mad/med calculations depending on mad input.

See Also

Other Outliers: outlier_tukey(), outlier_zscore_plot(), winsorize()

Outliers: Z-score method plot

Description

Test several Z-score thresholds to visualize outliers. Tidyverse friendly.

Usage

outlier_zscore_plot(df, var, group = NULL, thresh = c(2, 3, 5), top = 5)

Arguments

df

Dataframe.
var

Numeric variable.
group

Categorical variable. Grouping variable.
thresh

Numeric vector. Z-Score threshold for n standard deviations.
top

Integer. Show only n most frequent categorical values when using the group argument.

Value

ggplot2 object

See Also

Other Outliers: outlier_tukey(), outlier_zscore(), winsorize()

Examples

Sys.unsetenv("LARES_FONT") # Temporal
data(dft) # Titanic dataset
outlier_zscore_plot(dft, Fare)
p <- outlier_zscore_plot(dft, Fare, Pclass, thresh = c(3, 5))
plot(p)
attr(p, "z_values")
head(attr(p, "z_labels"))

Plot All Categorical Features (Frequencies)

Description

This function filters categorical columns and plots the frequency for each value on every feature.

Usage

plot_cats(df)

Arguments

df

Dataframe

Value

Plot. Result of df categorical features.

See Also

Other Exploratory: corr_var(), crosstab(), df_str(), distr(), freqs(), freqs_df(), freqs_list(), freqs_plot(), lasso_vars(), missingness(), plot_df(), plot_nums(), tree_var()

Chords Plot

Description

This auxiliary function plots discrete and continuous values results

Usage

plot_chord(
  origin,
  dest,
  weight = 1,
  mg = 3,
  title = "Chord Diagram",
  subtitle = "",
  pal = NA
)

Arguments

origin, dest

Vectors. Origin and destination vectors
weight

Vector. Weight for each chord.
mg

Numeric. Margin adjust for plot in case of need
title

Character. Title for the plot
subtitle

Character. Subtitle for the plot
pal

Vector. Colour pallete. Order matters.

Value

chordDiagram object

See Also

Other Visualization: distr(), freqs(), freqs_df(), freqs_list(), freqs_plot(), noPlot(), plot_survey(), plot_timeline(), tree_var()

Examples

# You must have "circlize" library to use this auxiliary function:
## Not run: 
df <- data.frame(from = c(1, 1, 2, 3, 4, 1, 6), to = c(4, 4, 4, 2, 2, NA, NA))
plot_chord(df$from, df$to)

## End(Not run)

Plot Summary of Numerical and Categorical Features

Description

This function plots all columns frequencies and boxplots, for categorical and numerical respectively.

Usage

plot_df(df)

Arguments

df

Dataframe

Value

Plot. Result of df categorical and numerical features.

See Also

Other Exploratory: corr_var(), crosstab(), df_str(), distr(), freqs(), freqs_df(), freqs_list(), freqs_plot(), lasso_vars(), missingness(), plot_cats(), plot_nums(), tree_var()

Plot All Numerical Features (Boxplots)

Description

This function filters numerical columns and plots boxplots.

Usage

plot_nums(df)

Arguments

df

Dataframe

Value

Plot. Result of df numerical features.

See Also

Other Exploratory: corr_var(), crosstab(), df_str(), distr(), freqs(), freqs_df(), freqs_list(), freqs_plot(), lasso_vars(), missingness(), plot_cats(), plot_df(), tree_var()

Examples

Sys.unsetenv("LARES_FONT") # Temporal
data(dft) # Titanic dataset
plot_nums(dft)

Plot Palette Colours

Description

This function plots a list of colours

Usage

plot_palette(fill, colour = "black", id = NA, limit = 12, ...)

Arguments

fill

Vector. List of colours for fills.
colour

Vector. List of colours for colours.
id

Vector. ID for each color.
limit

Integer. Show only first n values.
...

Additional parameters.

Value

Plot with fill colours and colour counter-colours if provided.

See Also

Other Themes: gg_fill_customs(), lares_pal(), theme_lares()

Examples

# Simply pass a vector
pal <- lares_pal("simple")
plot_palette(pal)
# Or fill + color named vector
pal <- lares_pal("pal")
plot_palette(fill = names(pal), colour = as.vector(pal))

Visualize Survey Results

Description

This function lets the user plot a survey's result.

Usage

plot_survey(answers, ignore = 1, title = NA, subtitle = NA)

Arguments

answers

Dataframe. Answers. Each row a different person. Each column a different answer.
ignore

Numeric Vector. Which columns are NOT answers?
title

Character. Title for your plot
subtitle

Character. Subtitle for your plot.

Value

ggplot2 object

See Also

Other Visualization: distr(), freqs(), freqs_df(), freqs_list(), freqs_plot(), noPlot(), plot_chord(), plot_timeline(), tree_var()

Plot timeline as Gantt Plot

Description

This function plots groups of observartions with timelines in a Gantt Plot way. Only works if start and end are date format values.

Usage

plot_timeline(
  event,
  start,
  end = start + 1,
  label = NA,
  group = NA,
  title = "Curriculum Vitae Timeline",
  subtitle = "Bernardo Lares",
  interactive = FALSE,
  save = FALSE,
  subdir = NA
)

Arguments

event

Vector. Event, role, label, or row.
start

Vector. Start date.
end

Vector. End date. Only one day be default if not defined
label

Vector. Place, institution, or label.
group

Vector. Academic, Work, Extracurricular... Pass as factor to keep a specific order
title

Character. Title for the plot
subtitle

Character. Subtitle for the plot
interactive

Boolean. Run with plotly?
save

Boolean. Save the output plot in our working directory
subdir

Character. Into which subdirectory do you wish to save the plot to?

Value

ggplot2 object

See Also

Other Visualization: distr(), freqs(), freqs_df(), freqs_list(), freqs_plot(), noPlot(), plot_chord(), plot_survey(), tree_var()

Examples

Sys.unsetenv("LARES_FONT") # Temporal
cols <- c("Role", "Place", "Type", "Start", "End")
today <- as.character(Sys.Date())
cv <- data.frame(rbind(
  c("Marketing Science Partner", "Facebook", "Work Experience", "2019-12-09", today),
  c("Data Scientist Consultant", "MatrixDS", "Work Experience", "2018-09-01", today),
  c("R Community Contributor", "lares library", "Extra", "2018-07-18", today),
  c("Lead Data Scientist", "MEG", "Work Experience", "2019-01-15", "2019-12-09"),
  c("Head of Analytics", "Comparamejor/R5", "Work Experience", "2016-08-01", "2019-01-15"),
  c("Big Data & Data Science Programme", "UdC", "Academic", "2017-09-01", "2018-02-28"),
  c("Project Engineer", "Polytex", "Work Experience", "2016-05-15", "2016-09-01"),
  c("Big Data Analyst", "MEG", "Work Experience", "2016-01-01", "2016-04-30"),
  c("Advanced Excel Instructor", "ARTS", "Work Experience", "2015-11-01", "2016-04-30"),
  c("Continuous Improvement Intern", "PAVCO", "Work Experience", "2015-04-01", "2015-08-30"),
  c("Mechanical Design Intern", "SIGALCA", "Work Experience", "2013-07-01", "2013-09-30"),
  c("DJs Online Community Owner", "LaresDJ.com / SoloParaDJs", "Extra", "2010-01-05", "2020-05-20"),
  c("Mechanical Engineer Degree", "USB", "Academic", "2009-09-15", "2015-11-20"),
  c("DJ and Composer/Producer", "Legacy Discplay", "Extra", "2009-05-01", "2015-04-30")
))
colnames(cv) <- cols
plot_timeline(
  event = cv$Role,
  start = cv$Start,
  end = cv$End,
  label = cv$Place,
  # Simple trick to re-arrange the grids
  group = factor(cv$Type, levels = c("Work Experience", "Academic", "Extra"))
)

Facebook's Prophet Forecast

Description

Prophet is Facebook's procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.

Usage

prophesize(
  df,
  n_future = 60,
  country = NULL,
  trend.param = 0.05,
  logged = FALSE,
  pout = 0.03,
  project = "Prophet Forecast"
)

Arguments

df

Data frame. Must contain date/time column and values column, in that order.
n_future

Integer. How many steps do you wish to forecast?
country

Character. Country code for holidays.
trend.param

Numeric. Flexibility of trend component. Default is 0.05, and as this value becomes larger, the trend component will be more flexible.
logged

Boolean. Convert values into logs?
pout

Numeric. Get rid of pout % of outliers.
project

Character. Name of your forecast project for plot title

Details

Official documentation: https://github.com/facebook/prophet

Value

List. Containing the forecast results, the prophet model, and a plot.

See Also

Other Forecast: forecast_arima()

Calculate cuts by quantiles

Description

This function lets the user quickly calculate cuts for quantiles and discretize numerical values into categorical values.

Usage

quants(values, splits = 10, return = "labels", n = 2)

Arguments

values

Vector. Values to calculate quantile cuts
splits

Integer. How many cuts should split the values?
return

Character. Return "summary" or "labels"
n

Integer. Determines the number of digits used in formatting the break numbers.

Value

Factor vector or data.frame. Depending on return input:

  • labels a factor ordered vector with each observation's quantile

  • summary a data.frame with information on each quantile cut

See Also

Other Data Wrangling: balance_data(), categ_reducer(), cleanText(), date_cuts(), date_feats(), file_name(), formatHTML(), holidays(), impute(), left(), normalize(), num_abbr(), ohe_commas(), ohse(), removenacols(), replaceall(), replacefactor(), textFeats(), textTokenizer(), vector2text(), year_month(), zerovar()

Other Calculus: corr(), corr_cross(), dist2d(), model_metrics()

Examples

data(dft) # Titanic dataset
quants(dft$Age, splits = 5, "summary")
quants(dft$Age, splits = 5, "labels")[1:10]

PostgreSQL Queries on Database (Read)

Description

This function lets the user query a PostgreSQL database. Previously was called queryDummy but was replaced and deprecated for a more general function by using the from parameter.

Usage

queryDB(query, from, creds = NA)

Arguments

query

Character. SQL Query
from

Character. Credential's user (see get_creds())
creds

Character. Credential's directory (see get_creds())

Value

data.frame. Result of fetching the query data.

See Also

Other Credentials: db_download(), db_upload(), encrypt_file(), get_credentials(), get_tweets(), mail_send(), queryGA(), slackSend(), stocks_file()

Examples

## Not run: 
# Query a PostgreSQL database
query <- "SELECT * FROM my_table LIMIT 10"
result <- queryDB(query, from = "my_database")
head(result)

## End(Not run)

Queries on Google Analytics

Description

This function lets the user query Google Analytics with its API. More about the documentation and parameters in googleAnalyticsR::google_analytics() or Google Analytics' API.

Usage

queryGA(
  account,
  creds = NA,
  token_dir = NA,
  metrics = "sessions",
  dimensions = "date",
  met_filters = NULL,
  dim_filters = NULL,
  start = lubridate::floor_date(Sys.Date(), "month"),
  end = Sys.Date()
)

Arguments

account

Character. Personal named accounts
creds

Character. Credential's user (see get_creds())
token_dir

Character. Credential's directory (see get_creds())
metrics

Character. Which metrics we wish to bring
dimensions

Character. Which dimensions we wish to bring
met_filters, dim_filters

A filter_clause_ga4 for filtering metrics/dimensions. Check googleAnalyticsR::google_analytics().
start

Date. Start date for the report
end

Date. End date for the report

Value

data.frame with the API GET request tabulated results.

See Also

Other Credentials: db_download(), db_upload(), encrypt_file(), get_credentials(), get_tweets(), mail_send(), queryDB(), slackSend(), stocks_file()

Other Google: filesGD(), gtrends_related(), readGS()

Other API: bring_api(), fb_accounts(), fb_ads(), fb_creatives(), fb_insights(), fb_process(), fb_report_check(), fb_rf(), fb_token(), gemini_ask(), gpt_ask(), slackSend()

Quiet prints and verbose noise

Description

This function silences (verbose) output prints. Thanks to Hadley Wickham for bringing the idea.

Usage

quiet(fx, quiet = TRUE)

Arguments

fx

Function to quiet
quiet

Quiet outputs? If not, skip quietness.

Value

Same as fx but with no messages or prints.

See Also

Other Tools: autoline(), bind_files(), bring_api(), cal_split(), chr2num(), db_download(), db_upload(), dont_sleep(), export_plot(), export_results(), files_functions(), font_exists(), formatColoured(), formatHTML(), glued(), grepm(), h2o_selectmodel(), haveInternet(), image_metadata(), importxlsx(), ip_data(), json2vector(), list_cats(), listfiles(), mail_send(), markdown2df(), move_files(), msplit(), myip(), read.file(), statusbar(), tic(), try_require(), updateLares(), warnifnot(), what_size()

Read Files Quickly (Auto-detected)

Description

This function lets the user import csv, xlsx, xls, sav files.

Usage

read.file(filename, current_wd = TRUE, sheet = 1, quiet = FALSE)

Arguments

filename

Character. File name to import.
current_wd

Boolean. Use current working directory before the file's name? Use this param to NOT get absolute root directory.
sheet

Character. Name or index of the sheet to read data from if file is xlsx or xls.
quiet

Boolean. Quiet summary message?

Value

List or data.frame, depending on filename's data.

See Also

Other Tools: autoline(), bind_files(), bring_api(), cal_split(), chr2num(), db_download(), db_upload(), dont_sleep(), export_plot(), export_results(), files_functions(), font_exists(), formatColoured(), formatHTML(), glued(), grepm(), h2o_selectmodel(), haveInternet(), image_metadata(), importxlsx(), ip_data(), json2vector(), list_cats(), listfiles(), mail_send(), markdown2df(), move_files(), msplit(), myip(), quiet(), statusbar(), tic(), try_require(), updateLares(), warnifnot(), what_size()

Google Sheets Reading and Writing (API v4)

Description

Read and write data from Google Sheets knowing the file's title. You may use a single value from a cell or a data.frame from a cell range.

Usage

readGS(
  title,
  sheet = "Hoja 1",
  range = NULL,
  drop_nas = TRUE,
  json = NULL,
  email = NULL,
  api_key = NULL,
  server = FALSE,
  ...
)

writeGS(
  data,
  title,
  sheet = "Hoja 1",
  range = "A1",
  reformat = FALSE,
  append = FALSE,
  json = NULL,
  email = NULL,
  api_key = NULL,
  server = FALSE,
  ...
)

Arguments

title

Character. Title of Google Drive file. Uses regular expressions so you may fetch with patterns instead of names.
sheet

Character. Working sheet to import
range

Character. A cell range to read from
drop_nas

Boolean. Remove columns and rows that contain only NAs?
json

Character. JSON filename with service auth
email, api_key

Character. If you have multiple pre-authorized accounts in your machine, you may non-interactively select which one you wish to use by email and/or api_key.
server

Boolean. Force interacting auth process?
...

Additional parameters passed to read_sheet().
data

Object (value, vector, data.frame, list).
reformat

Boolean. Reformat the affected cells?
append

Boolean.

Value

For reading, data.frame with the results of your Google Sheets file based on its title, specificially the sheet and range requested. For writing, no return value.

See Also

Other Scrapper: filesGD(), gtrends_related(), holidays(), ip_data(), mp3_get(), splot_summary(), stocks_quote()

Other Google: filesGD(), gtrends_related(), queryGA()

Reduce Dimensionality with PCA

Description

Principal component analysis or (PCA) is a method we can use to reduce high-dimensional data to a low-dimensional space. In other words, we cannot accurately visualize high-dimensional datasets because we cannot visualize anything above 3 features. The main purpose behind PCA is to transform datasets with more than 3 features (high-dimensional) into typically a 2/3 column dataset. Despite the reduction into a lower-dimensional space we still can retain most of the variance or information from our original dataset.

Usage

reduce_pca(
  df,
  n = NULL,
  ignore = NULL,
  comb = c(1, 2),
  quiet = FALSE,
  plot = TRUE,
  ...
)

Arguments

df

Dataframe
n

Integer. Number of dimensions to reduce to.
ignore

Character vector. Names of columns to ignore.
comb

Vector. Which columns do you wish to plot? Select which two variables by name or column position.
quiet

Boolean. Keep quiet? If not, informative messages will be shown.
plot

Boolean. Create plots?
...

Additional parameters passed to stats::prcomp

Value

List with reduced dataframe and possible plots.

See Also

Other Dimensionality: reduce_tsne()

Other Clusters: clusterKmeans(), clusterOptimalK(), clusterVisualK(), reduce_tsne()

Examples

Sys.unsetenv("LARES_FONT") # Temporal
data("iris")
df <- subset(iris, select = c(-Species))
df$id <- seq_len(nrow(df))
reduce_pca(df, n = 3, ignore = "id")

Reduce Dimensionality with t-SNE

Description

t-SNE takes high-dimensional data and reduces it to a low-dimensional graph (1-3 dimensions). Unlike PCA, t-SNE can reduce dimensions with non-linear relationships. PCA attempts to draw the best fitting line through the distribution. T-SNE calculates a similarity measure based on the distance between points instead of trying to maximize variance.

Usage

reduce_tsne(df, n = 2, ignore = NULL, quiet = FALSE, plot = TRUE, ...)

Arguments

df

Dataframe
n

Integer. Number of dimensions to reduce to.
ignore

Character vector. Names of columns to ignore.
quiet

Boolean. Keep quiet? If not, informative messages will be shown.
plot

Boolean. Create plots?
...

Additional parameters passed to Rtsne::Rtsne

Value

List with reduced dataframe and possible plots.

See Also

Other Dimensionality: reduce_pca()

Other Clusters: clusterKmeans(), clusterOptimalK(), clusterVisualK(), reduce_pca()

Examples

## Not run: 
data("iris")
df <- subset(iris, select = c(-Species))
df$id <- seq_len(nrow(df))
reduce_tsne(df, ignore = "id", max_iter = 800, perplexity = 20)

## End(Not run)

Remove stop-words and patterns from character vector

Description

Remove all stop-words and specific patterns from a character vector

Usage

remove_stopwords(text, stop_words, exclude = NULL, sep = " ")

Arguments

text

Character vector
stop_words

Character vector. Words to exclude from text. Example: if you want to exclude "a", whenever that word appears it will be excluded, but when the letter "a" appears in a word, it will remain.
exclude

Character. Pattern to exclude using regex.
sep

Character. String that separate the terms.

Value

Character vector with removed texts.

See Also

Other Text Mining: cleanText(), ngrams(), replaceall(), sentimentBreakdown(), textCloud(), textFeats(), textTokenizer(), topics_rake()

Examples

x <- c("A brown fox jumps over a dog.", "Another brown dog.")
remove_stopwords(x, stop_words = c("dog", "brown", "a"), exclude = "\\.")

Remove/Drop Columns in which ALL or SOME values are NAs

Description

This function lets the user remove all columns that have some or all values as NAs

This function lets the user remove all rows that have some or all values as NAs

Usage

removenacols(df, all = TRUE, ignore = NULL)

removenarows(df, all = TRUE)

numericalonly(df, dropnacols = TRUE, logs = FALSE, natransform = NA)

Arguments

df

Data.frame
all

Boolean. Remove rows which contains ONLY NA values. If set to FALSE, rows which contains at least one NA will be removed
ignore

Character vector. Column names to ignore validation.
dropnacols

Boolean. Drop columns with only NA values?
logs

Boolean. Calculate log(x)+1 for numerical columns?
natransform

String. "mean" or 0 to impute NA values. If set to NA no calculation will run.

Value

data.frame with removed columns.

data.frame with removed rows.

data.frame with all numerical columns selected.

See Also

Other Data Wrangling: balance_data(), categ_reducer(), cleanText(), date_cuts(), date_feats(), file_name(), formatHTML(), holidays(), impute(), left(), normalize(), num_abbr(), ohe_commas(), ohse(), quants(), replaceall(), replacefactor(), textFeats(), textTokenizer(), vector2text(), year_month(), zerovar()

Other Data Wrangling: balance_data(), categ_reducer(), cleanText(), date_cuts(), date_feats(), file_name(), formatHTML(), holidays(), impute(), left(), normalize(), num_abbr(), ohe_commas(), ohse(), quants(), replaceall(), replacefactor(), textFeats(), textTokenizer(), vector2text(), year_month(), zerovar()

Other Data Wrangling: balance_data(), categ_reducer(), cleanText(), date_cuts(), date_feats(), file_name(), formatHTML(), holidays(), impute(), left(), normalize(), num_abbr(), ohe_commas(), ohse(), quants(), replaceall(), replacefactor(), textFeats(), textTokenizer(), vector2text(), year_month(), zerovar()

Examples

data(dft) # Titanic dataset
str(dft)
numericalonly(dft) %>% head()
numericalonly(dft, natransform = "mean") %>% head()

Replace Values With

Description

This function lets the user replace all specific values in a vector or data.frame into another value. If replacing more than one value, order matters so they will be replaced in the same order that you pass them to the function. Factors will be refactored.

Usage

replaceall(df, original, change, which = "all", fixclass = TRUE, quiet = TRUE)

Arguments

df

Data.frame or Vector
original

String or Vector. Original text you wish to replace
change

String or Vector. Values you wish to replace the originals with
which

Character vector. Name of columns to use. Leave "all" for everything
fixclass

Boolean. Try to detect logical classes after transformations (or leave as default classes as character)?
quiet

Boolean. Keep quiet? If not, informative messages will be shown.

Value

data.frame with replaced values based on inputs.

See Also

Other Data Wrangling: balance_data(), categ_reducer(), cleanText(), date_cuts(), date_feats(), file_name(), formatHTML(), holidays(), impute(), left(), normalize(), num_abbr(), ohe_commas(), ohse(), quants(), removenacols(), replacefactor(), textFeats(), textTokenizer(), vector2text(), year_month(), zerovar()

Other Text Mining: cleanText(), ngrams(), remove_stopwords(), sentimentBreakdown(), textCloud(), textFeats(), textTokenizer(), topics_rake()

Examples

df <- data.frame(
  one = c(1:4, NA),
  two = LETTERS[1:5],
  three = rep("A", 5),
  four = c(NA, "Aaa", 123, "B", "C")
)
print(df)

replaceall(df, "A", NA)

replaceall(df, "A", "a")

replaceall(df, 1, "*")

replaceall(df, NA, "NotNA")

replaceall(df, NA, 0)

replaceall(df, c("A", "B"), c("'A'", "'B'"))

replaceall(df, "a", "*", which = "four")

Replace Factor Values

Description

This function lets the user replace levels on a factor vector.

Usage

replacefactor(x, original, change)

Arguments

x

Factor (or Character) Vector
original

String or Vector. Original text you wish to replace
change

String or Vector. Values you wish to replace the originals with

Value

Factor vector with transformed levels.

See Also

Other Data Wrangling: balance_data(), categ_reducer(), cleanText(), date_cuts(), date_feats(), file_name(), formatHTML(), holidays(), impute(), left(), normalize(), num_abbr(), ohe_commas(), ohse(), quants(), removenacols(), replaceall(), textFeats(), textTokenizer(), vector2text(), year_month(), zerovar()

Examples

library(dplyr)
data(dft)
# Replace a single value
dft <- mutate(dft, Pclass = replacefactor(Pclass, original = "1", change = "First"))
levels(dft$Pclass)
# Replace multiple values
dft <- mutate(dft, Pclass = replacefactor(Pclass, c("2", "3"), c("Second", "Third")))
levels(dft$Pclass)

Robyn: Generate default hyperparameters

Description

Generate a list with hyperparameter default values, ready to be passed to Robyn::robyn_inputs().

Usage

robyn_hypsbuilder(
  channels,
  media_type = "default",
  adstock = "geometric",
  date_type = "weekly",
  lagged = FALSE
)

Arguments

channels

Character vector. Paid media and organic variables names.
media_type

Character vector. Must be length 1 or same as channels. Pick, for every channels value, what type of media it is: "online" or "offline".
adstock

Character. Pick one of: "geometric" or "weibull".
date_type

Character. Pick one of: "daily", "weekly", or "monthly". Only valid to transform thetas when using geometric adstock. Set to "skip" in case you wish to leave default weekly values.
lagged

Boolean vector. Must be length 1 or same as channels. Pick, for every channels value, if you wish to have a lagged effect. Only valid for Weibull adstock.

Value

list with default hyperparameters ranges.

See Also

Other Robyn: robyn_marginal(), robyn_modelselector(), robyn_performance()

Examples

robyn_hypsbuilder(
  channels = c(
    "branded_search_spend",
    "nonbranded_search_spend",
    "print_spend",
    "ooh_spend",
    "tv_spend",
    "radio_spend"
  ),
  media_type = c(
    "online", "online", "offline",
    "offline", "offline", "offline"
  ),
  adstock = "geometric",
  date_type = "weekly"
)

Robyn: Marginal Performance (mROAS & mCPA) [Experimental]

Description

Calculate and plot marginal performance of any spend or organic variable.

Usage

robyn_marginal(..., marginal_unit = 1)

Arguments

...

Additional parameters.
marginal_unit

Additional units to calculate the marginal performance.

Value

list with base and marginal response results, marginal performance metric and value, and plot.

See Also

Other Robyn: robyn_hypsbuilder(), robyn_modelselector(), robyn_performance()

Examples

## Not run: 
# You may load an exported model to recreate Robyn objects
mod <- Robyn::robyn_recreate(json_file = "your_model.json")
robyn_marginal(
  InputCollect = mod$InputCollect,
  OutputCollect = mod$OutputCollect,
  metric_name = "emails_O",
  metric_value = 100000,
  date_range = "all",
  marginal_unit = 10000000
)

## End(Not run)

Robyn: Model Selection by Weighted Criteria Scores

Description

Consider N best models to select the right ones to study using several criteria/metrics such as potential improvement on budget allocator, how many non-zero coefficients there are, R squared, historical performance, baseline expectation, etc.

Read more about this functionality in Medium post: here.

Usage

robyn_modelselector(
  InputCollect,
  OutputCollect,
  metrics = c("rsq_train", "performance", "potential_improvement", "non_zeroes",
    "incluster_models", "cluster_sd", "certainty", "baseline_dist"),
  wt = c(2, 0.1, 0, 1, 0.1, 0, 1.5, 0),
  baseline_ref = 0,
  top = 4,
  n_per_cluster = 5,
  allocator_limits = c(0.5, 2),
  quiet = FALSE,
  cache = TRUE,
  ...
)

## S3 method for class 'robyn_modelselector'
plot(x, ...)

Arguments

InputCollect, OutputCollect

Robyn output objects.
metrics

Character vector. Which metrics do you want to consider? Pick any combination from: "rsq_train" for trained R squared, "performance" for ROAS or (inverse) CPA, "potential_improvement" for default budget allocator improvement using allocator_limits, "non_zeroes" for non-zero beta coefficients, "incluster_models" for amount of models per cluster, "baseline_dist" for the difference between the model's baseline and baseline_ref value, "certainty" metric to minimize the channels' distance to their cluster's mean performance, weighted by spends if spend_wt = TRUE, "cluster_sd" metric to score based on the paid channels' performance standard deviations in clusters. Additionally, you can use the standard MOO errors: "nrmse", "decomp.rssd", and "mape" (the lowest the error, the highest the score; same for "baseline_dist" and "cluster_sd").
wt

Vector. Weight for each of the normalized metrics selected, to calculate the score and rank models. Must have the same order and length of metrics parameter input.
baseline_ref

Numeric value. Between 0 and 1. What is the baseline percentage you expect? Baseline in this case are all the sales or conversions from non-media channels (organic & paid). Use with "baseline_dist" metric.
top

Integer. How many ranked models to star? The better the model is, the more stars it will have marked.
n_per_cluster

Integer. How many models per cluster do you want to plot? Default: 5. Keep in mind they will all be considered for the calculations.
allocator_limits

Numeric vector, length 2. How flexible do you want to be with the budget allocator? By default, we'll consider a 0.5X and 2X range to let the budget shift across channels.
quiet

Boolean. Keep quiet? If not, informative messages will be shown.
cache

Use cache functionality for allocator's results?
...

Additional parameters.
x

robyn_modelselector object

Value

list with resulting ranked data.frames, weights and plot.

See Also

Other Robyn: robyn_hypsbuilder(), robyn_marginal(), robyn_performance()

Robyn: Dynamic Performance and Contribution Report

Description

Given a date range, calculate specific and total performance and contribution for each of your marketing and non-marketing channels.

Usage

robyn_performance(
  InputCollect,
  OutputCollect,
  start_date = NULL,
  end_date = NULL,
  solID = NULL,
  totals = TRUE,
  non_promo = FALSE,
  marginals = FALSE,
  carryovers = FALSE,
  new_version = FALSE,
  quiet = FALSE,
  ...
)

Arguments

InputCollect, OutputCollect

Robyn output objects.
start_date, end_date

Date. Start and end date to filter the data to be reported.
solID

Character. Single ID of the model to report. If there's only one available in OutputCollect, no need to define.
totals

Boolean. Add total rows. This includes summary rows (promotional which is paid and organic channels, baseline, grand total).
non_promo

Boolean. Add non-promotional responses as well?
marginals

Boolean. Include mROAS or mCPA marginal performance metric as an additional column called "marginal". Calculations are based on mean spend and mean response with mean carryover results, between start_date and end_date.
carryovers

Boolean. Add mean percentage of carryover response for date range between start_date and end_date on paid channels. Keep in mind organic variables also have carryover but currently not showing.
new_version

Boolean. Use dev version's new function for marginal calculations (if available)?
quiet

Boolean. Keep quiet? If not, informative messages will be shown.
...

Additional parameters.

Value

data.frame with results on ROAS/CPA, spend, response, contribution per channel, with or without total rows.

See Also

Other Robyn: robyn_hypsbuilder(), robyn_marginal(), robyn_modelselector()

Examples

## Not run: 
# You may load an exported model to recreate Robyn objects
mod <- Robyn::robyn_recreate(json_file = "your_model.json")
robyn_performance(mod$InputCollect, mod$OutputCollect)

## End(Not run)

Cross-MMM Budget Optimization across Models

Description

Given a list of recreated Robyn models, this function optimizes budget allocation across MMM with respective constraints by maximizing incremental revenue/conversions. This method assumes each model is independent, that can be compared given its spends were cleanly and properly split, they modeled the same metric (revenue or conversion) and units (currency or type of conversion), and date granularity. For best results, ensure channels have similar granularity across markets to simplify interpretation and application of the outputs.

Given a list of recreated Robyn models, this function optimizes budget allocation across MMM with respective constraints by maximizing response across all channels. This method assumes each model is independent, that can be compared given its spends were cleanly and properly split, they modeled the same metric (revenue or conversion) and units (currency or type of conversion), and date granularity. Recommended to have same channels granularity across markets to simplify results readings and application.

Usage

robyn_xmodels(
  models,
  initial_budgets = NULL,
  start_dates = NULL,
  end_dates = NULL,
  budget_constr_low = 0.5,
  budget_constr_up = 1.5,
  channel_constr_low = budget_constr_low,
  channel_constr_up = budget_constr_up,
  cores = NULL,
  quiet = FALSE,
  ...
)

## S3 method for class 'robyn_crossmmm'
print(x, ...)

robyn_xchannels(
  models,
  initial_budgets = NULL,
  start_dates = NULL,
  end_dates = NULL,
  channel_constr_low = 0.5,
  channel_constr_up = 2,
  quiet = FALSE,
  ...
)

Arguments

models

Lists. Recreated Robyn models with robyn_recreate().
initial_budgets

Numeric vector. Default will use the total spends per model for the specified or total date range. Must be length 1 or same as models.
start_dates, end_dates

Character vector. Start and end dates for each specific model. You can specify a single date and will be used in all models. Default empty value will assume you want all available data and date range. Must be length 1 or same as models.
budget_constr_low, budget_constr_up

Numeric vector. Relative minimum and maximum budgets to consider based on initial_budgets. By default it'll consider 50 Must be length 1 or same as models.
channel_constr_low, channel_constr_up

Numeric vector. Relative lower and upper constraints per channel compared with mean spend during the time period defined. If mean was zero for date range, historical mean spend value will be used. Must have length 1 to replicate for all channels or same length (and order )as paid_media_spends.
cores

Integer. How many cores to use for parallel computations? Set to 1 to not use this option. Default will the minimum between 10 cores and all available cores - 1.
quiet

Boolean. Keep quiet? If not, informative messages will be shown.
...

Additional parameters to be passed to internal functions.
x

Object to print: robyn_crossmmm() output.

Details

This approach is faster and cleaner compared with previous proposal using robyn_xmodels().

Value

Invisible vector with results by letter.

List. Contains optimized allocation results and plots.

Examples

# You must have Robyn installed and some models stored as JSON files
## Not run: 
# Import and recreate the models
files <- c("BrandA.json", "BrandB.json", "BrandC.json", "BrandN.json")
models <- lapply(files, function(x) Robyn::robyn_recreate(x))
names(models) <- gsub("\\.json", "", files)

# Calculate cross-brand optimal allocation
res <- robyn_xmodels(
  models,
  cores = 10,
  start_dates = "2023-01-01",
  end_dates = "2023-12-01"
)
print(res)
res$summary

## End(Not run)
## Not run: 
# Calculate cross-brand optimal allocation
res <- robyn_xchannels(
  models,
  start_dates = "2023-01-01",
  end_dates = "2023-12-01"
)

## End(Not run)

AUC and ROC Curves Data

Description

This function calculates ROC Curves and AUC values with 95% confidence range. It also works for multi-categorical models.

Usage

ROC(tag, score, multis = NA)

Arguments

tag

Vector. Real known label
score

Vector. Predicted value or model's result
multis

Data.frame. Containing columns with each category score (only used when more than 2 categories coexist)

Value

List with ROC's results, area under the curve (AUC) and their CI.

Plot Results

To plot results, use the mplot_roc() function.

See Also

Other Machine Learning: conf_mat(), export_results(), gain_lift(), h2o_automl(), h2o_predict_MOJO(), h2o_selectmodel(), impute(), iter_seeds(), lasso_vars(), model_metrics(), model_preprocess(), msplit()

Other Model metrics: conf_mat(), errors(), gain_lift(), loglossBinary(), model_metrics()

Examples

data(dfr) # Results for AutoML Predictions
lapply(dfr[c(1, 2)], head)

# ROC Data for Binomial Model
roc1 <- ROC(dfr$class2$tag, dfr$class2$scores)
lapply(roc1, head)

# ROC Data for Multi-Categorical Model
roc2 <- ROC(dfr$class3$tag, dfr$class3$score,
  multis = subset(dfr$class3, select = -c(tag, score))
)
lapply(roc2, head)

Generative Art: Sphere XmodY

Description

Generative Art: Sphere XmodY

Usage

rtistry_sphere(eye = c(100, 0, 0), pal = "auto", var = 3)

Arguments

eye, pal, var

Parameters to change aesthetics and calculations

Value

ggplot object

Axis scales format

Description

The _comma ones set comma format for axis text, the _percent ones set percent format for axis text, _dollar for collar currency, and _abbr for abbreviated format. Lastly, use _formatNum to further customize your numerical scales with formatNum.

Usage

scale_x_comma(...)

scale_y_comma(...)

scale_x_percent(...)

scale_y_percent(...)

scale_x_dollar(...)

scale_y_dollar(...)

scale_x_abbr(...)

scale_y_abbr(...)

scale_x_formatNum(
  ...,
  decimals = 2,
  signif = NULL,
  type = Sys.getenv("LARES_NUMFORMAT"),
  pre = "",
  pos = "",
  sign = FALSE,
  abbr = FALSE
)

scale_y_formatNum(
  ...,
  decimals = 2,
  signif = NULL,
  type = Sys.getenv("LARES_NUMFORMAT"),
  pre = "",
  pos = "",
  sign = FALSE,
  abbr = FALSE
)

Arguments

...

Arguments passed to ggplot2::continuous_scale or formatNum depending on the function.
decimals

Integer. Amount of decimals to display. If set to NULL, then getOption("digits") will be used.
signif

Integer. Rounds the values in its first argument to the specified number of significant digits.
type

Integer. 1 for International standards. 2 for American Standards. Use Sys.setenv("LARES_NUMFORMAT" = 2) to set this parameter globally.
pre, pos

Character. Add string before or after number.
sign

Boolean. Add + sign to positive values.
abbr

Boolean. Abbreviate using num_abbr()? You can use the ‘decimals' parameter to set abbr’s n(-1) parameter.

Value

Reformatted scales on ggplot2 object

Examples

library(ggplot2)
df <- ggplot2::txhousing %>% removenarows(all = FALSE)

ggplot(df, aes(x = sales, y = volume)) +
  geom_point() +
  scale_x_dollar() +
  scale_y_abbr()

# Use any argument from scale_x/y_continuous
ggplot(df, aes(x = listings, y = log(inventory))) +
  geom_point() +
  scale_x_comma() +
  scale_y_percent(limits = c(0, 3))

# Use any argument from scale_x/y_continuous AND formatNum
ggplot(df, aes(x = median, y = inventory)) +
  geom_point() +
  scale_x_formatNum(n.breaks = 3, pre = "@", abbr = TRUE) +
  scale_y_formatNum(position = "right", decimals = 0, pos = " X")

Scrabble: Dictionaries

Description

Download words from 4 different languages: English, Spanish, German, and French. Words will be save into the temp directory. This is an auxiliary function. You may want to use scrabble_words directly if you are searching for the highest score words!

Get score for any word or list of words. You may set manually depending on the rules and languages you are playing with. Check the examples for Spanish and English values when I played Words With Friends.

Dataframe for every letter and points given a language.

Find highest score words given a set of letters, rules, and language to win at Scrabble! You just have to find the best place to post your tiles.

Usage

scrabble_dictionary(lang_dic, quiet = FALSE)

scrabble_score(words, scores.df)

scrabble_points(lang)

scrabble_words(
  tiles = "",
  free = 0,
  force_start = "",
  force_end = "",
  force_str = "",
  force_exclude = "",
  exclude_here = "",
  force_n = 0,
  force_max = 0,
  pattern = "",
  repeated = FALSE,
  language = Sys.getenv("LARES_LANG"),
  scores = language,
  words = NULL,
  quiet = FALSE,
  print = TRUE
)

Arguments

lang_dic

Character. Any of "en","es","de","fr". Set to NULL if you wish to skip this step (and use words parameter in scrabble_words instead).
quiet

Boolean. Do not print words as they are being searched.
words

Character vector. Use if you wish to manually add words.
scores.df

Dataframe. Must contain two columns: "tiles" with every letter of the alphabet and "scores" for each letter's score.
lang

Character. Any of "en","es" or "chars". Set to NULL if you wish to skip this step (and use words parameter in scrabble_words() instead). The "chars" parameter will score the number of characters a word has.
tiles

Character. The letters you wish to consider.
free

Integer. How many free blank tiles you have?
force_start, force_end

Character. Force words to start or end with a pattern of letters and position. Examples: "S" or "SO" or "__S_O"... If the string contains tiles that were not specified in tiles, they will automatically be included.
force_str

Character vector. Force words to contain strings. If the string contains tiles that were not specified in tiles, they will automatically be included.
force_exclude, exclude_here

Character vector. Exclude words containing these tiles (and positions). Not very relevant on Scrabble but for Wordle.
force_n, force_max

Integer. Force words to be n or max n characters long. Leave 0 to ignore parameter.
pattern

Character string. Custom regex patterns you'd like to match.
repeated

Boolean. By default, no replacement allowed. When activated, a single tile can be repeated and won't be "used and discarded".
scores, language

Character. Any of "en","es","de","fr". If scores is not any of those languages, must be a data.frame that contains two columns: "tiles" with every letter of the alphabet and "scores" for each letter's score. If you wish to overwrite or complement this dictionaries other words you can set to "none" and/or use the words parameter. You might also want to set this parameter globally with Sys.setenv("LARES_LANG" = "en") and forget about it!
print

Boolean. Print how many words are left by step.

Value

data.frame with words and language columns.

data.frame with word, scores, and length values for each word.

data.frame with tiles and scores for each alphabet letter.

data.frame with matching words found, sorted by higher points.

See Also

Other Games: maze_solve(), sudoku_solver(), wordle_check()

Examples

if (haveInternet()) {
  # For Spanish words (default)
  es_scores <- scrabble_points("es")
  # Custom scores for each letter
  cu_scores <- data.frame(
    tiles = tolower(LETTERS),
    scores = c(
      1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 5, 2, 4, 2, 1,
      4, 10, 1, 1, 1, 2, 5, 4, 8, 3, 10
    )
  )

  # Score values for each set of rules
  words <- c("Bernardo", "Whiskey", "R is great")
  scrabble_score(words, es_scores)
  scrabble_score(words, cu_scores)
}

scrabble_points("es")
scrabble_points("en")
# Not yet available
scrabble_points("fr")

if (haveInternet()) {
  # Automatic use of languages and scores
  Sys.setenv("LARES_LANG" = "es")
  scrabble_words(
    tiles = "hola",
    free = 2,
    force_start = "h",
    force_n = 4,
    force_str = "_o_a",
    exclude_here = "__z|j"
  )

  wordle <- c("board", "tempo", "shoes", "hoard")
  scrabble_words(
    language = NULL,
    words = wordle,
    force_n = 5,
    force_str = "O_R"
  )

  # Words considered for a language (you can custom it too!)
  es_words <- scrabble_dictionary("es")
}

Sentiment Breakdown on Text

Description

This function searches for relevant words in a given text and adds sentiments labels (joy, anticipation, surprise, positive, trust, anger, sadness, fear, negative, disgust) for each of them, using NRC. Then, makes a summary for all words and plot results.

Usage

sentimentBreakdown(
  text,
  lang = "spanish",
  exclude = c("maduro", "que"),
  append_file = NA,
  append_words = NA,
  plot = TRUE,
  subtitle = NA
)

Arguments

text

Character vector
lang

Character. Language in text (used for stop words)
exclude

Character vector. Which word do you wish to exclude?
append_file

Character. Add a dictionary to append. This file must contain at least two columns, first with words and second with the sentiment (consider sentiments on description).
append_words

Dataframe. Same as append_file but appending data frame with word and sentiment directly
plot

Boolean. Plot results summary?
subtitle

Character. Add subtitle to the plot

Value

List. Contains data.frame with words and sentiments, summary and plot.

See Also

Other Text Mining: cleanText(), ngrams(), remove_stopwords(), replaceall(), textCloud(), textFeats(), textTokenizer(), topics_rake()

Surnames Order Sequence

Description

Generate a sequence of numbers that determines the order in which surnames should be listed based on the number of generations of ancestors you wish to include. This sequence follows the traditional Latin custom of assigning the father's surname first, followed by the mother's surname. The same logic extends systematically to higher generations, ensuring that the order of surnames remains consistent as you move upward through the family tree.

Usage

seq_surnames(n = 1)

Arguments

n

Integer. Number of generations to include in the sequence. Notice it will generate a vector with 2^(n-1) values.

Value

Integer vector.

Examples

seq_surnames(1)
seq_surnames(2)
seq_surnames(3)
seq_surnames(4)

SHAP-based dependence plots for categorical/numerical features (PDP)

Description

Having a h2o_shap object, plot a dependence plot for any categorical or numerical feature.

Usage

shap_var(x, var, keep_outliers = FALSE)

Arguments

x

h2o_shap object
var

Variable name
keep_outliers

Boolean. Outliers detected with z-score and 3sd may be suppress or kept in your plot. Keep them?

Value

ggplot2 objct with shap values plotted

See Also

Other SHAP: h2o_shap()

Examples

## Not run: 
# Train a h2o_automl model
model <- h2o_automl(dft, Survived,
  max_models = 1, target = TRUE,
  ignore = c("Ticket", "Cabin", "PassengerId"),
  quiet = TRUE
)

# Calculate SHAP values
SHAP_values <- h2o_shap(model)
# Equivalent to:
# SHAP_values <- h2o_shap(
#  model = model$model,
#  test = model$datasets$test,
#  scores = model$scores_test$scores)

# Check SHAP results
head(SHAP_values)

# You must have "ggbeeswarm" library to use this auxiliary function:
# Plot SHAP values (feature importance)
plot(SHAP_values)

# Plot some of the variables (categorical)
shap_var(SHAP_values, Pclass)

# Plot some of the variables (numerical)
shap_var(SHAP_values, Fare)

## End(Not run)

Send Slack Message (Webhook)

Description

This function send a Slack message using its Webhooks.

Usage

slackSend(text, title = "", pretext = "", hook = NA, creds = NA)

Arguments

text, title, pretext

Character. Content on you Slack message.
hook

Character. Web hook URL. Ths value will be overwritten by creds if correctly used.
creds

Character. Credential's dir (see get_creds()). Set hook URL into the "slack" list in your YML file. Will use first value.

Details

For more help, you can follow the Sending messages using Incoming Webhooks original documentarion.

Value

Invisible POST response

See Also

Other API: bring_api(), fb_accounts(), fb_ads(), fb_creatives(), fb_insights(), fb_process(), fb_report_check(), fb_rf(), fb_token(), gemini_ask(), gpt_ask(), queryGA()

Other Credentials: db_download(), db_upload(), encrypt_file(), get_credentials(), get_tweets(), mail_send(), queryDB(), queryGA(), stocks_file()

Examples

## Not run: 
slackSend(text = "This is a message", title = "TEST", pretext = Sys.info()["user"])

## End(Not run)

Investing Portfolio Reporting Plots

Description

splot_summary() plots a summary for the whole portfolio, showing how much have you invested, how much has each ticker changed, etc.

splot_change() plots each stock's change through history, since inception, with weighted attributions or absolute values.

splot_growth() plots your portfolio's growth, in cash and investment, since inception.

stocks_plots() plots a portfolio's historical dividends incomes grouped by quarter an year.

splot_roi() plots a portfolio's historical ROI since inception or since last n days, with 2 moving average lines.

splot_types() lets the user plot types or categories of tickers.

splot_etf() lets the user plot his portfolio's distribution, specifically ETF's sectors.

Usage

splot_summary(p, s, save = FALSE)

splot_change(
  p,
  s,
  rel = TRUE,
  group = FALSE,
  n_days = 365,
  keep_old = FALSE,
  save = FALSE
)

splot_growth(p, save = FALSE)

splot_divs(p, type = 1)

splot_roi(p, n_days = 365, historical = TRUE, ma = c(12, 50), save = FALSE)

splot_types(s, save = FALSE)

splot_etf(s, keep_all = FALSE, cache = TRUE, save = FALSE)

Arguments

p

Dataframe. Result from daily_portfolio()
s

Dataframe. Result from daily_stocks()
save

Boolean. Save plot into a local file?
rel

Boolean. Relative delta values (weighted with portfolio)? If not, absolute monetary delta values.
group

Boolean. Group stocks by stocks type?
n_days

Integer. How many days back you want to see?
keep_old

Boolean. Include sold tickers even though not currently in portfolio?
type

Integer. Typo of plot. 1 for incomes.
historical

Boolean. Historical ROI metric? If not, ROI will be calculated locally for n_days parameter
ma

Numeric Vector. Select 2 values for moving averages. Set to NA to turn this metric off
keep_all

Boolean. Keep "Not Known / Not ETF"?
cache

Boolean. Use daily cache if available?

Value

ggplot object

See Also

Other Investment: etf_sector(), stocks_file(), stocks_quote()

Other Scrapper: filesGD(), gtrends_related(), holidays(), ip_data(), mp3_get(), readGS(), stocks_quote()

Spread list column into new columns

Description

Spread an existing list column into new columns on a data.frame. Note that every element on every observation must have a name for the function to do its work. Original column will be automatically suppressed but you can set the replace argument to avoid it.

Usage

spread_list(df, col, str = NULL, replace = TRUE)

Arguments

df

Dataframe
col

Variable name.
str

Character. Start column names with. If set to NULL, original name of column will be used.
replace

Boolean. Replace original values (delete column)

Value

data.frame. Result of un-nesting named or un-named list columns.

Examples

df <- dplyr::starwars
# Un-named list columns
spread_list(df, films, replace = FALSE) %>%
  dplyr::select(name, dplyr::starts_with("films")) %>%
  head(8)
# Named (and un-nammed) list columns
df <- dplyr::tibble(id = 1:3, platform = list(
  list("fb" = 1, "ig" = 2),
  list("fb" = 3),
  list()
))
spread_list(df, platform, str = "ptf_")

Progressive Status Bar (Loading)

Description

This function lets the user view a progressbar for a 'for' loop.

Usage

statusbar(
  run = 1,
  max.run = 100,
  label = run,
  msg = "",
  type = Sys.getenv("LARES_STATUSBAR"),
  start_time = NA,
  multiples = 1,
  alarm = FALSE
)

Arguments

run

Iterator. for loop or an integer with the current loop number. Start with 1 preferibly
max.run

Number. Maximum number of loops
label

String. With additionaly information to be printed at the end of the line. The default is run.
msg

Character. Finish message.
type

Character. Loading type style: equal, domino, sword, filled.
start_time

POSIXct. Start time to consider. If NA, then when first iteration starts will be set as start time. Useful for when first iteration is showed as done but started a few seconds/minutes ago.
multiples

Integer. Only print when multiples of N (to avoid) wasting resources on fast and lots of iterations.
alarm

Boolean. Ping (sound) when done. Requires beepr.

Value

No return value, called for side effects.

See Also

Other Tools: autoline(), bind_files(), bring_api(), cal_split(), chr2num(), db_download(), db_upload(), dont_sleep(), export_plot(), export_results(), files_functions(), font_exists(), formatColoured(), formatHTML(), glued(), grepm(), h2o_selectmodel(), haveInternet(), image_metadata(), importxlsx(), ip_data(), json2vector(), list_cats(), listfiles(), mail_send(), markdown2df(), move_files(), msplit(), myip(), quiet(), read.file(), tic(), try_require(), updateLares(), warnifnot(), what_size()

Examples

for (i in 1:9) {
  statusbar(i, 9, multiples = 2)
  Sys.sleep(0.3)
}

Build a Personal Investing Portfolio Report

Description

stocks_file() lets the user download his personal Excel with his Portfolio's data, locally or from Dropbox.

daily_stocks() creates a dataframe with all relevant metrics and values, for each ticker or symbol, for every day since inception.

daily_portfolio() creates a data.frame with all relevant metrics and values, for the overall portfolio, for every day since inception.

stocks_obj() lets the user create his portfolio's calculations and plots for further study.

stocks_report() lets the user create his portfolio's full report with plots and send it to an email with the HTML report attached

Usage

stocks_file(
  file = NA,
  creds = NA,
  auto = TRUE,
  sheets = c("Portafolio", "Fondos", "Transacciones"),
  keep_old = TRUE,
  cache = TRUE,
  quiet = FALSE,
  ...
)

daily_stocks(hist, trans, tickers = NA, window = "MAX", ...)

daily_portfolio(hist, trans, cash, cash_fix = 0, window = "MAX")

stocks_obj(
  data = stocks_file(),
  cash_fix = 0,
  tax = 30,
  sectors = FALSE,
  parg = FALSE,
  window = c("1M", "YTD", "1Y", "MAX"),
  cache = TRUE,
  quiet = FALSE
)

stocks_report(
  data = NA,
  keep_old = TRUE,
  dir = NA,
  mail = FALSE,
  attachment = TRUE,
  to = "[email protected]",
  sectors = FALSE,
  keep = FALSE,
  creds = NA,
  cache = TRUE
)

Arguments

file

Character. Import an Excel file, local or from URL.
creds

Character. Credential's user (see get_creds()) for sending mail and Dropbox interaction.
auto

Boolean. Automatically use my local personal file? You might want to set in into your .Renviron LARES_PORTFOLIO=~/dir/to/your/file.xlsx so you can leave all other parameters as NA and use it every time.
sheets

Character Vector. Names of each sheet containing Portfolio summary, Cash, and Transactions information. Please, keep the order of these tabs.
keep_old

Boolean. Include sold tickers even though not currently in portfolio?
cache

Boolean. Use daily cache if available?
quiet

Boolean. Keep quiet? If not, informative messages will be shown.
...

Additional parameters.
hist

Dataframe. Result from stocks_hist()
trans

Dataframe. Result from stocks_file()$transactions
tickers

Dataframe. Result from stocks_file()$portfolio
window

Character. Choose any of: "1W", "1M", "6M", "1Y", "YTD", "5Y", "MAX"
cash

Dataframe. Result from stocks_file()$cash
cash_fix

Numeric. If, for some reason, you need to fix your cash amount for all reports, set the amount here
data

Character. stocks_obj() output. If NA, automatic parameters and stocks_file() defaults will be used.
tax

Numeric. How much [0-99] of your dividends are gone with taxes?
sectors

Boolean. Return sectors segmentation for ETFs?
parg

Boolean. Personal argument. Used to personalize stuff, in this case, taxes changed from A to B in given date (hard-coded)
dir

Character. Directory for HTML report output. If set to NA, current working directory will be used. If mail sent, file will be erased
mail

Boolean. Do you want to send an email with the report attached? If not, an HTML file will be created in dir
attachment

Boolean. Create and add report as attachment if mail=TRUE? If not, no report will be rendered and only tabulated summaries will be included on email's body.
to

Character. Email to send the report to
keep

Boolean. Keep HTML file when sent by email?

Value

List with portfolio, transactions, and cash data.frames.

data.frame. Processed at date and symbol level.

data.frame. Processed at date and portfolio level.

List. Aggregated results and plots.

Invisible list. Aggregated results and plots.

See Also

Other Investment: etf_sector(), splot_summary(), stocks_quote()

Other Credentials: db_download(), db_upload(), encrypt_file(), get_credentials(), get_tweets(), mail_send(), queryDB(), queryGA(), slackSend()

Examples

## Not run: 
# Load lares dummy portfolio XLSX
file <- system.file("inst/docs", "dummyPortfolio.xlsx", package = "lares")
df <- stocks_file(
  file = file,
  sheets = c("Portafolio", "Fondos", "Transacciones"),
  keep_old = FALSE
)

## End(Not run)
## Not run: 
list <- stocks_obj()
stocks_report(list, dir = "~/Desktop")

## End(Not run)

Download Stocks Historical and Current Values

Description

stocks_quote() lets the user download stocks live data.

stocks_hist() lets the user download stocks historical data.

Usage

stocks_quote(symbols, ...)

stocks_hist(
  symbols = c("VTI", "META"),
  from = Sys.Date() - 365,
  to = Sys.Date(),
  today = TRUE,
  tax = 15,
  parg = FALSE,
  cache = TRUE,
  quiet = FALSE,
  ...
)

## S3 method for class 'stocks_hist'
plot(x, type = 1, ...)

Arguments

symbols

Character Vector. List of symbols to download historical data.
...

Additional parameters.
from, to

Date. Dates for range. If not set, 1 year will be downloaded. Do use more than 4 days or will be over-written.
today

Boolean. Do you wish to add today's live quote? This will happen only if to value is the same as today's date
tax

Numeric. How much [0-99] of your dividends are gone with taxes?
parg

Boolean. Personal argument. Used to personalize stuff, in this case, taxes changed from A to B in given date (hard-coded)
cache

Boolean. Use daily cache if available?
quiet

Boolean. Keep quiet? If not, informative messages will be shown.
x

stocks_hist object
type

Integer. Select type of plot.

Value

data.frame with Symbol, Type of stock, Quote time, current value, Daily Change, Market, and Symbol Name.

See Also

Other Investment: etf_sector(), splot_summary(), stocks_file()

Other Scrapper: filesGD(), gtrends_related(), holidays(), ip_data(), mp3_get(), readGS(), splot_summary()

Examples

## Not run: 
# Multiple quotes at the same time
stocks_quote(c("VTI", "VOO", "TSLA"))

## End(Not run)
## Not run: 
df <- stocks_hist(symbols = c("VTI", "META", "FIW"), from = Sys.Date() - 180)
print(head(df))
plot(df)

## End(Not run)

Solve Sudoku Puzzles

Description

Solve a Sudoku puzzle, where empty values are represented by 0s into a matrix object.

Usage

sudoku_solver(board, needed_cells = NULL, index = 1, quiet = FALSE)

Arguments

board

Matrix. 9x9 matrix or vector length 81, with only digits from 0 to 9.
needed_cells, index

Auxiliary parameters to auto-iterate using this same fx.
quiet

Boolean. Keep quiet? If not, plot results.

Value

Logical output answering of the input board can be solved. The actual solved solution will be created as an object named solved in your .GlobalEnv.

See Also

Other Games: maze_solve(), scrabble_dictionary(), wordle_check()

Examples

# board <- c(0,0,0,0,0,6,0,0,0,
#            0,9,5,7,0,0,3,0,0,
#            4,0,0,0,9,2,0,0,5,
#            7,6,4,0,0,0,0,0,3,
#            0,0,0,0,0,0,0,0,0,
#            2,0,0,0,0,0,9,7,1,
#            5,0,0,2,1,0,0,0,9,
#            0,0,7,0,0,5,4,8,0,
#            0,0,0,8,0,0,0,0,0)
# sudoku_solver(board)

# Trivial input (everything)
trivial <- matrix(rep(0, 81), byrow = TRUE, ncol = 9)
trivial
sudoku_solver(trivial)

# Wrong / Impossible to solve input
imp <- matrix(c(rep(1, 72), rep(0, 9)), byrow = TRUE, ncol = 9)
imp
sudoku_solver(imp)

Set Target Value in Target Variable

Description

This function detects or forces the target value when predicting a categorical binary model. This is an auxiliary function.

Usage

target_set(tag, score, target = "auto", quiet = FALSE)

Arguments

tag

Vector. Real known label
score

Vector. Predicted value or model's result
target

Value. Which is your target positive value? If set to 'auto', the target with largest mean(score) will be selected. Change the value to overwrite. Only used when binary categorical model.
quiet

Boolean. Keep quiet? If not, informative messages will be shown.

Value

List. Contains original data.frame df and which with the target variable.

Wordcloud Plot

Description

Study the distribution of a target variable vs another variable. This function is quite similar to the funModeling's corrplot function.

Usage

textCloud(
  text,
  lang = "english",
  exclude = NULL,
  seed = 0,
  keep_spaces = FALSE,
  min = 2,
  pal = NA,
  print = TRUE
)

Arguments

text

Character vector
lang

Character. Language in text (used for stop words)
exclude

Character vector. Which word do you wish to exclude?
seed

Numeric. Seed for re-producible plots
keep_spaces

Boolean. If you wish to keep spaces in each line to keep unique compount words, separated with spaces, set to TRUE. For example, 'LA ALAMEDA' will be set as 'LA_ALAMEDA' and treated as a single word.
min

Integer. Words with less frequency will not be plotted
pal

Character vector. Which colours do you wish to use
print

Boolean. Plot results as textcloud?

Value

wordcloud plot object

See Also

Other Text Mining: cleanText(), ngrams(), remove_stopwords(), replaceall(), sentimentBreakdown(), textFeats(), textTokenizer(), topics_rake()

Create features out of text

Description

This function creates a data.frame with features based on a text vector

Usage

textFeats(text, auto = TRUE, contains = NA, prc = FALSE)

Arguments

text

Character vector
auto

Boolean. Auto create some useful parameters?
contains

Character vector. Which columns do you wish to add with a contains (counter) string validator?
prc

Boolean. Also add percentage of each column compared with length?

Value

data.frame with additional features based on text.

See Also

Other Data Wrangling: balance_data(), categ_reducer(), cleanText(), date_cuts(), date_feats(), file_name(), formatHTML(), holidays(), impute(), left(), normalize(), num_abbr(), ohe_commas(), ohse(), quants(), removenacols(), replaceall(), replacefactor(), textTokenizer(), vector2text(), year_month(), zerovar()

Other Text Mining: cleanText(), ngrams(), remove_stopwords(), replaceall(), sentimentBreakdown(), textCloud(), textTokenizer(), topics_rake()

Examples

textFeats("Bernardo Lares")
textFeats("Bernardo Lares 123!", prc = TRUE)
textFeats("I'm 100% Lares...", contains = c("Lares", "lares"))
textFeats(c("GREAT library!!", "Have you tried this 2?", "Happy faces :D :-)"))

Tokenize Vectors into Words

Description

This function transforms texts into words, calculate frequencies, supress stop words in a given language.

Usage

textTokenizer(
  text,
  exclude = NULL,
  lang = NULL,
  min_word_freq = 5,
  min_word_len = 2,
  keep_spaces = FALSE,
  lowercase = TRUE,
  remove_numbers = TRUE,
  remove_punct = TRUE,
  remove_lettt = TRUE,
  laughs = TRUE,
  utf = TRUE,
  df = FALSE,
  h2o = FALSE,
  quiet = FALSE
)

Arguments

text

Character vector. Sentences or texts you wish to tokenize.
exclude

Character vector. Which words do you wish to exclude?
lang

Character. Language in text (used for stop words). Example: "spanish" or "english". Set to NA to ignore.
min_word_freq

Integer. This will discard words that appear less than <int> times. Defaults to 2. Set to NA to ignore.
min_word_len

Integer. This will discard words that have less than <int> characters. Defaults to 5. Set to NA to ignore.
keep_spaces

Boolean. If you wish to keep spaces in each line to keep unique compound words, separated with spaces, set to TRUE. For example, 'one two' will be set as 'one_two' and treated as a single word.
lowercase, remove_numbers, remove_punct

Boolean.
remove_lettt

Boolean. Repeated letters (more than 3 consecutive).
laughs

Boolean. Try to unify all laughs texts.
utf

Boolean. Transform all characters to UTF (no accents and crazy symbols)
df

Boolean. Return a dataframe with a one-hot-encoding kind of results? Each word is a column and returns if word is contained.
h2o

Boolean. Return H2OFrame?
quiet

Boolean. Keep quiet? If not, informative messages will be shown.

Value

data.frame. Tokenized words with counters.

See Also

Other Data Wrangling: balance_data(), categ_reducer(), cleanText(), date_cuts(), date_feats(), file_name(), formatHTML(), holidays(), impute(), left(), normalize(), num_abbr(), ohe_commas(), ohse(), quants(), removenacols(), replaceall(), replacefactor(), textFeats(), vector2text(), year_month(), zerovar()

Other Text Mining: cleanText(), ngrams(), remove_stopwords(), replaceall(), sentimentBreakdown(), textCloud(), textFeats(), topics_rake()

Theme for ggplot2 (lares)

Description

Based on hrbrthemes' theme_ipsum and customized for lares usage. With this team you can custom the colour and fill palettes, global colour parameters, major and minor grids, legend, font and font size.

Usage

theme_lares(
  font = Sys.getenv("LARES_FONT"),
  size = 12,
  main_colour = "darkorange3",
  hard_colour = "black",
  soft_colour = "grey30",
  plot_colour = "transparent",
  panel_colour = "transparent",
  background = "transparent",
  no_facets = FALSE,
  legend = NULL,
  grid = TRUE,
  axis = TRUE,
  clean = FALSE,
  mg = 9,
  pal = 0,
  palette = NULL,
  which = "fc",
  quiet = TRUE,
  ...
)

Arguments

font, size

Character and numeric. Base font family and base size for texts. Arial Narrow is set by default when the library is loaded; you may change it with Sys.setenv("LARES_FONT" = "X") or by using this parameter manually. If font is "ignore" or NA, it'll be ignored.
main_colour, hard_colour, soft_colour, plot_colour, panel_colour

Character. Main colours for your theme.
background

Character. Main colour for your background. Overwrites plot_colour and panel_colour.
no_facets

Boolean. Suppress facet labels?
legend

Character. Legend position: "top", "right", "bottom", or "left" You can also set to FALSE or "none" to suppress legend.
grid

Character or Boolean. Use TRUE/FALSE or a combination of X, x, Y, y to enable/disable minor and major grids.
axis

Character or Boolean. Use TRUE/FALSE, x or Y to enable X and/or Y axis lines.
clean

Boolean. Suppress grids and axis? Overwrites both parameters.
mg

Numeric. External margins reference.
pal

Integer. 1 for fill and colour palette, 2 for only colour palette, 3 for only fill palette, 4 for personal labels-colour palette. 0 for nothing.
palette

Character vector. Pass a vector with HEX colour codes to use a custom palette. If you pass a named vector, the name values will be used as fill and the values will be used as colour.
which

Character. When pal = 3, select which colours should be added with the custom colours palette: fill, colour, text (fct) - first letters.
quiet

Boolean. Keep quiet? If not, informative messages will be shown.
...

Additional parameters.

Value

Themed ggplot2 object

Why Arial Narrow?

First and foremost, Arial Narrow is generally installed by default or readily available on any modern system, so it's "free"-ish; plus, it is a condensed font with solid default kerning pairs and geometric numbers.

See Also

Other Themes: gg_fill_customs(), lares_pal(), plot_palette()

Examples

data(dft)
library(ggplot2)
p <- ggplot(dft, aes(x = Pclass, y = sum(Fare), fill = Pclass)) +
  geom_col()
p + theme_lares()
p + theme_lares(pal = 1)
p + theme_lares(background = "#999999", mg = 25)
p + theme_lares(legend = "top", grid = "Yy")
p + theme_lares(clean = TRUE)

Stopwatch to measure timings in R

Description

Start a stopwatch.

Stop a stopwatch.

Usage

tic(id = 1, start = proc.time()["elapsed"], quiet = TRUE)

toc(
  id = 1,
  msg = "Elapsed time:",
  type = "units",
  signif = 3,
  quiet = FALSE,
  ...
)

Arguments

id

Define ID if multiple tic & toc are being used.
start

Start time. Now is default.
quiet

Boolean. Keep quiet? If not, informative messages will be shown.
msg

Character. Custom message shown
type

Character. Output format for time list element. Choose any of: units, clock, seconds.
signif

Integer. Significant digits.
...

Additional parameters.

Value

Invisible list. Contains tic (start time), toc (stop time), elapsed time and message printed.

toc returns an (invisible) list containing the time-stamps tic and toc, time in seconds and the message msg.

See Also

Other Tools: autoline(), bind_files(), bring_api(), cal_split(), chr2num(), db_download(), db_upload(), dont_sleep(), export_plot(), export_results(), files_functions(), font_exists(), formatColoured(), formatHTML(), glued(), grepm(), h2o_selectmodel(), haveInternet(), image_metadata(), importxlsx(), ip_data(), json2vector(), list_cats(), listfiles(), mail_send(), markdown2df(), move_files(), msplit(), myip(), quiet(), read.file(), statusbar(), try_require(), updateLares(), warnifnot(), what_size()

Examples

# Basic use (global stopwatch)
tic()
Sys.sleep(0.1)
toc()

# Multiple tic tocs
tic(id = "two", quiet = FALSE)
Sys.sleep(0.2)
toc(id = "two")

# Global is still working (id = 1)
toc(msg = "The function finished its work in")

Calculate Character Reduction from R Object to TOON Format

Description

Calculates the percentage reduction in the number of characters when converting an R object to its TOON string representation compared to its JSON representation (or the string itself if the input is a single string).

Usage

toon_reduction(obj, ...)

Arguments

obj

Any R object (list, vector, data frame, or string).
...

Additional arguments passed to jsonlite::toJSON()

Value

A numeric value representing the character reduction ratio (0 to 1).

Keyword/Topic identification using RAKE

Description

RAKE is a basic algorithm which tries to identify keywords in text. Based on udpipe library, model models, and keywords_rake function.

Usage

topics_rake(text, file = "english-ewt-ud-2.4-190531.udpipe", lang = "english")

Arguments

text

Character vector
file

Character. Name of udpipe model previously downloaded for a specific language
lang

Character. If file does not exist, this language will be downloaded from udpipe's models.

Value

data.frame with topics for each text input.

See Also

Other Text Mining: cleanText(), ngrams(), remove_stopwords(), replaceall(), sentimentBreakdown(), textCloud(), textFeats(), textTokenizer()

Recursive Partitioning and Regression Trees

Description

Fit and plot a rpart model for exploratory purposes using rpart and rpart.plot libraries.

Usage

tree_var(
  df,
  y,
  type = 2,
  max = 3,
  min = 20,
  cp = 0,
  ohse = TRUE,
  plot = TRUE,
  explain = TRUE,
  title = NA,
  subtitle = NULL,
  ...
)

Arguments

df

Data frame
y

Variable or Character. Name of the dependent variable or response.
type

Type of plot. Possible values:

0 Draw a split label at each split and a node label at each leaf.

1 Label all nodes, not just leaves. Similar to text.rpart's all=TRUE.

2 Default. Like 1 but draw the split labels below the node labels. Similar to the plots in the CART book.

3 Draw separate split labels for the left and right directions.

4 Like 3 but label all nodes, not just leaves. Similar to text.rpart's fancy=TRUE. See also clip.right.labs.

5 Show the split variable name in the interior nodes.
max

Integer. Maximal depth of the tree.
min

Integer. The minimum number of observations that must exist in a node in order for a split to be attempted.
cp

complexity parameter. Any split that does not decrease the overall lack of fit by a factor of cp is not attempted. For instance, with anova splitting, this means that the overall R-squared must increase by cp at each step. The main role of this parameter is to save computing time by pruning off splits that are obviously not worthwhile. Essentially,the user informs the program that any split which does not improve the fit by cp will likely be pruned off by cross-validation, and that hence the program need not pursue it.

ohse

Boolean. Auto generate One Hot Smart Encoding?
plot

Boolean. Return a plot? If not, rpart object.
explain

Boolean. Include a brief explanation on the bottom part of the plot.
title, subtitle

Character. Title and subtitle to include in plot. Set to NULL to ignore.
...

Additional parameters passed to rpart.plot().

Value

(Invisible) list type 'tree_var' with plot (function), model, predictions, performance metrics, and interpret auxiliary text.

See Also

Other Exploratory: corr_var(), crosstab(), df_str(), distr(), freqs(), freqs_df(), freqs_list(), freqs_plot(), lasso_vars(), missingness(), plot_cats(), plot_df(), plot_nums()

Other Visualization: distr(), freqs(), freqs_df(), freqs_list(), freqs_plot(), noPlot(), plot_chord(), plot_survey(), plot_timeline()

Examples

data(dft)
# Regression Tree
tree <- tree_var(dft, Fare, subtitle = "Titanic dataset")
tree$plot() # tree plot
tree$model # rpart model object
tree$performance # metrics
# Binary Tree
tree_var(dft, Survived_TRUE, explain = FALSE, cex = 0.8)$plot()
# Multiclass tree
tree_var(dft[, c("Pclass", "Fare", "Age")], Pclass, ohse = FALSE)$plot()

Check if Specific Package is Installed

Description

This function checks library dependencies

Usage

try_require(package, stop = TRUE, load = TRUE, lib.loc = NULL, ...)

Arguments

package

Character. Name of the library
stop

Boolean. Stop if not installed. If FALSE and library is not available, warning will be shown.
load

Boolean. Load library?
lib.loc

Character vector. Location of R library trees to search through, or NULL. The default value of NULL corresponds to all libraries currently known to .libPaths(). Non-existent library trees are silently ignored.
...

Additional parameters.

Value

No return value, called for side effects.

See Also

Other Tools: autoline(), bind_files(), bring_api(), cal_split(), chr2num(), db_download(), db_upload(), dont_sleep(), export_plot(), export_results(), files_functions(), font_exists(), formatColoured(), formatHTML(), glued(), grepm(), h2o_selectmodel(), haveInternet(), image_metadata(), importxlsx(), ip_data(), json2vector(), list_cats(), listfiles(), mail_send(), markdown2df(), move_files(), msplit(), myip(), quiet(), read.file(), statusbar(), tic(), updateLares(), warnifnot(), what_size()

Examples

# Check if library base is installed. If not, stop and show error
try_require("base", stop = TRUE)
# Check if library xxx is installed. If not, show warning
try_require("xxx", stop = FALSE)

Update the library (dev or CRAN version)

Description

This auxiliary function lets the user update lares to latest CRAN or developer version.

Usage

updateLares(force = FALSE, dev = TRUE, all = FALSE, local = FALSE, fb = FALSE)

Arguments

force

Boolean. Force install.
dev

Boolean. Developer version (Github)? If not, CRAN version.
all

Boolean. Install other recommended libraries? Kinda Docker install!
local

Boolean. Install package with local files? (or Github repo).
fb

Boolean. From FB instance? Personal internal use.

Value

No return value, called for side effects.

See Also

Other Tools: autoline(), bind_files(), bring_api(), cal_split(), chr2num(), db_download(), db_upload(), dont_sleep(), export_plot(), export_results(), files_functions(), font_exists(), formatColoured(), formatHTML(), glued(), grepm(), h2o_selectmodel(), haveInternet(), image_metadata(), importxlsx(), ip_data(), json2vector(), list_cats(), listfiles(), mail_send(), markdown2df(), move_files(), msplit(), myip(), quiet(), read.file(), statusbar(), tic(), try_require(), warnifnot(), what_size()

Examples

## Not run: 
# Update to CRAN version
updateLares(dev = FALSE)

# Update to development version from GitHub
updateLares(dev = TRUE)

# Install with all recommended packages
updateLares(all = TRUE)

## End(Not run)

Convert a vector into a comma separated text

Description

Convert a vector into a comma separated text

Usage

vector2text(vector, sep = ", ", quotes = TRUE, force_single = FALSE, and = "")

v2t(vector, sep = ", ", quotes = TRUE, force_single = FALSE, and = "")

Arguments

vector

Vector. Vector with more than 1 observation.
sep

Character. String text wished to insert between values.
quotes

Boolean. Bring simple quotes for each observation.
force_single

Boolean. Force single quotes by replacing \".
and

Character. Add 'and' or something before last observation. Not boolean variable so it can be used on other languages. Note that the last comma will be suppressed if Sys.getenv("LARES_NUMFORMAT") is set to 1 and you have less than 3 values.

Value

Vector pasting vector values into a single string

See Also

Other Data Wrangling: balance_data(), categ_reducer(), cleanText(), date_cuts(), date_feats(), file_name(), formatHTML(), holidays(), impute(), left(), normalize(), num_abbr(), ohe_commas(), ohse(), quants(), removenacols(), replaceall(), replacefactor(), textFeats(), textTokenizer(), year_month(), zerovar()

Examples

vector2text(LETTERS[1:5])
vector2text(c(1:5), quotes = FALSE)
vector2text(c(1:5), quotes = FALSE, sep = "-")
vector2text(c(1:5), and = "and also")
vector2text(c("Text", "R's"), force_single = TRUE)
# Shorter function with same purpose
v2t(LETTERS[1:5])

Test the Truth of R Expressions and Warn

Description

If the expression in ... is not TRUE, warning is called, producing a warning message indicating the expression which was not true.

Usage

warnifnot(...)

Arguments

...

any R expression, which should evaluate to TRUE

See Also

Other Tools: autoline(), bind_files(), bring_api(), cal_split(), chr2num(), db_download(), db_upload(), dont_sleep(), export_plot(), export_results(), files_functions(), font_exists(), formatColoured(), formatHTML(), glued(), grepm(), h2o_selectmodel(), haveInternet(), image_metadata(), importxlsx(), ip_data(), json2vector(), list_cats(), listfiles(), mail_send(), markdown2df(), move_files(), msplit(), myip(), quiet(), read.file(), statusbar(), tic(), try_require(), updateLares(), what_size()

Examples

warnifnot(TRUE)
warnifnot(FALSE)
warnifnot(1 + 1 == 3)

Calculate weighted stock values using FIFO/LIFO

Description

Calculate weighted stock values using FIFO/LIFO

Usage

weighted_value(
  value,
  n = rep(1, length(value)),
  technique = NULL,
  n_stocks = NULL,
  buy_only = TRUE,
  type = 1,
  ...
)

Arguments

value

Numeric vector. Representing the values of the stock.
n

Numeric vector. Representing the volume of the operation. Positive for 'Buy' and negative for 'Sale'.
technique

Character. Pick any of FIFO or LIFO, or NULL to skip.
n_stocks

Integer. Specify the number of stocks to consider. By default will sum positive values of n.
buy_only

Boolean. Consider only buy (positive) values?
type

Integer. 1 for returning the value, 2 for returning the data.frame with the details ("df" attribute)
...

Additional parameters.

Value

The calculated weighted mean value.

Examples

values <- c(10, 20, 30, 40, 50)
weights <- c(2, 3, -4, 5, 6)
mean(values)
weighted_value(values)
weighted.mean(values, weights)
weighted_value(values, weights, buy_only = FALSE)
# Using FIFO and LIFO
weighted_value(values, weights, "FIFO")
weighted_value(values, weights, "LIFO", n_stocks = 8)

Calculate the size of any R object or directory

Description

Calculate the size of any R object or directory

Usage

what_size(x = NULL, units = "Mb", path = NULL, recursive = TRUE, ...)

Arguments

x

Object
units

Character. Specify which unit to use, i.e. "Gb", "Mb", "Kb".
path

a character vector of full path names; the default corresponds to the working directory, getwd(). Tilde expansion (see path.expand) is performed. Missing values will be ignored. Elements with a marked encoding will be converted to the native encoding (and if that fails, considered non-existent).
recursive

logical. Should the listing recurse into directories?
...

further arguments passed to or from other methods.

See Also

Other Tools: autoline(), bind_files(), bring_api(), cal_split(), chr2num(), db_download(), db_upload(), dont_sleep(), export_plot(), export_results(), files_functions(), font_exists(), formatColoured(), formatHTML(), glued(), grepm(), h2o_selectmodel(), haveInternet(), image_metadata(), importxlsx(), ip_data(), json2vector(), list_cats(), listfiles(), mail_send(), markdown2df(), move_files(), msplit(), myip(), quiet(), read.file(), statusbar(), tic(), try_require(), updateLares(), warnifnot()

Examples

what_size(seq(1:1e3), "Kb")
what_size(seq(1:1e6))
what_size(as.character(seq(1:1e6)))
# what_size(path = ".")

Outliers: Winsorize

Description

Winsorizing a vector means that a predefined quantum of the smallest and/or the largest values are replaced by less extreme values. Thereby the substitute values are the most extreme retained values.

Usage

winsorize(x, thresh = c(0.05, 0.95), na.rm = FALSE)

Arguments

x

Numeric vector. Distribution to be winsorized.
thresh

Numeric vector. Lower and upper quantiles thresholds. Set values within [0,1].
na.rm

Boolean. Should NA be omitted to calculate the quantiles? Note that NA in x are preserved and left unchanged anyway.

Value

Numeric vector transformed.

See Also

Other Outliers: outlier_tukey(), outlier_zscore(), outlier_zscore_plot()

Wordle Game Validation

Description

Given and input and a word, validate each letter based on Wordle's rules: correct letter in correct placement (green), correct letter in wrong placement (yellow), letter is not present (red).

Usage

wordle_check(
  input,
  word,
  dictionary = NULL,
  lang_dic = "en",
  method = 3,
  print = TRUE
)

## S3 method for class 'wordle_check'
print(x, print = TRUE, ...)

wordle_dictionary(lang_dic = "en", method = 3, quiet = TRUE)

wordle_simulation(input, word, seed = NULL, quiet = FALSE, ...)

## S3 method for class 'wordle_simulation'
print(x, type = 1, ...)

Arguments

input

Character. Word to validate (5-letters)
word

Character. Word actually answer (5-letters).
dictionary

Character vector. List of valid words. If set to NULL then will use modified scrabble_dictionary() to fetch 5 letter words. Use lang_dic param to set language.
lang_dic

Character. Any of: "en", "es". Only used when dictionary parameter is NULL. Requires internet connection the first time. Uses cache.
method

Integer. 1 for scrabble_dictionary(), 3 for scrapping the words taken straight from the game's source code.
print

Boolean. Print validation results?
x

Object to print
...

Additional parameters.
quiet

Boolean. Do not print words as they are being searched.
seed

Numeric. For reproducibility. Accepts more than one: will run as many seeds there are.
type

Integer. 1 for summary and 2 for coloured results.

Value

Invisible vector with results by letter.

See Also

Other Games: maze_solve(), scrabble_dictionary(), sudoku_solver()

Examples

word <- "ABBEY"
# Or pick a random one:
# word <- sample(wordle_dictionary("en"), 1)
wordle_check("OPENS", word)
wordle_check("BABES", word)
wordle_check("KEBAB", word, print = FALSE)
wordle_check("ABYSS", word)
wordle_check("ABBEY", word)
# Feel free to use scrabble_words() for hints

x <- wordle_simulation(input = "SAINT", word = "ABBEY", seed = 1:3)
print(x)

Ranked Predictive Power of Cross-Features (x2y)

Description

The relative reduction in error when we go from a baseline model (average for continuous and most frequent for categorical features) to a predictive model, can measure the strength of the relationship between two features. In other words, x2y measures the ability of x to predict y. We use CART (Classification And Regression Trees) models to be able to 1) compare numerical and non-numerical features, 2) detect non-linear relationships, and 3) because they are easy/quick to train.

Usage

x2y(
  df,
  target = NULL,
  symmetric = FALSE,
  target_x = FALSE,
  target_y = FALSE,
  plot = FALSE,
  top = 20,
  quiet = "auto",
  ohse = FALSE,
  corr = FALSE,
  ...
)

x2y_metric(x, y, confidence = FALSE, bootstraps = 20, max_cat = 20)

## S3 method for class 'x2y_preds'
plot(x, corr = FALSE, ...)

## S3 method for class 'x2y'
plot(x, type = 1, ...)

x2y_preds(x, y, max_cat = 10)

Arguments

df

data.frame. Note that variables with no variance will be ignored.
target

Character vector. If you are only interested in the x2y values between particular variable(s) in df, set name(s) of the variable(s) you are interested in. Keep NULL to calculate for every variable (column). Check target_x and target_y parameters as well.
symmetric

Boolean. x2y metric is not symmetric with respect to x and y. The extent to which x can predict y can be different from the extent to which y can predict x. Set symmetric=TRUE if you wish to average both numbers.
target_x, target_y

Boolean. Force target features to be part of x OR y?
plot

Boolean. Return a plot? If not, only a data.frame with calculated results will be returned.
top

Integer. Show/plot only top N predictive cross-features. Set to NULL to return all.
quiet

Boolean. Keep quiet? If not, show progress bar.
ohse

Boolean. Use lares::ohse() to pre-process the data?
corr

Boolean. Add correlation and pvalue data to compare with? For more custom studies, use lares::corr_cross() directly.
...

Additional parameters passed to x2y_metric()
x, y

Vectors. Categorical or numerical vectors of same length.
confidence

Boolean. Calculate 95% confidence intervals estimated with N bootstraps.
bootstraps

Integer. If confidence=TRUE, how many bootstraps? The more iterations we run the more precise the confidence internal will be.
max_cat

Integer. Maximum number of unique x or y values when categorical. Will select then most frequent values and the rest will be passed as "".
type

Integer. Plot type: 1 for tile plot, 2 for ranked bar plot.

Details

This x2y metric is based on Rama Ramakrishnan's post: An Alternative to the Correlation Coefficient That Works For Numeric and Categorical Variables. This analysis complements our lares::corr_cross() output.

Value

Depending on plot input, a plot or a data.frame with x2y results.

Examples

data(dft) # Titanic dataset
x2y_results <- x2y(dft, quiet = TRUE, max_cat = 10, top = NULL)
head(x2y_results, 10)
plot(x2y_results, type = 2)

# Confidence intervals with 10 bootstrap iterations
x2y(dft,
  target = c("Survived", "Age"),
  confidence = TRUE, bootstraps = 10, top = 8
)

# Compare with mean absolute correlations
x2y(dft, "Fare", corr = TRUE, top = 6, target_x = TRUE)

# Plot (symmetric) results
symm <- x2y(dft, target = "Survived", symmetric = TRUE)
plot(symm, type = 1)

# Symmetry: x2y vs y2x
on.exit(set.seed(42))
x <- seq(-1, 1, 0.01)
y <- sqrt(1 - x^2) + rnorm(length(x), mean = 0, sd = 0.05)

# Knowing x reduces the uncertainty about the value of y a lot more than
# knowing y reduces the uncertainty about the value of x. Note correlation.
plot(x2y_preds(x, y), corr = TRUE)
plot(x2y_preds(y, x), corr = TRUE)

Convert Date into Year-Month, Year-Quarter or Year-Week Format

Description

This function lets the user convert a date into YYYY-MM, YYYY-QX, or YYYY-WW format easily.

Usage

year_month(date)

year_quarter(date)

year_week(date)

Arguments

date

Date vector. Date to transform format.

Value

Vector with dates reformatted

See Also

Other Data Wrangling: balance_data(), categ_reducer(), cleanText(), date_cuts(), date_feats(), file_name(), formatHTML(), holidays(), impute(), left(), normalize(), num_abbr(), ohe_commas(), ohse(), quants(), removenacols(), replaceall(), replacefactor(), textFeats(), textTokenizer(), vector2text(), zerovar()

Examples

year_month(Sys.Date())
year_quarter(Sys.Date())
year_week(Sys.Date())

Zero Variance Columns

Description

This function detects which columns have the same value (whichever) for each column.

Usage

zerovar(df)

Arguments

df

Dataframe

Value

Character vector with column names on which its values have no variance.

See Also

Other Data Wrangling: balance_data(), categ_reducer(), cleanText(), date_cuts(), date_feats(), file_name(), formatHTML(), holidays(), impute(), left(), normalize(), num_abbr(), ohe_commas(), ohse(), quants(), removenacols(), replaceall(), replacefactor(), textFeats(), textTokenizer(), vector2text(), year_month()

Examples

df <- data.frame(a = c(1, NA, 3), b = rep(NA, 3), c = rep(5, 3))
print(df)
zerovar(df)