Analytics, Data Mining & Machine Learning Sidekick

Description

R library for better/faster analytics, visualization, data mining, and machine learning tasks.

Author(s)

Bernardo Lares (laresbernardo@gmail.com)

See Also

Useful links:

• https://github.com/laresbernardo/lares

• https://laresbernardo.github.io/lares/

• Report bugs at https://github.com/laresbernardo/lares/issues

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

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)

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

Value

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

See Also

Other Tools: 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()

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

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

Value

data.frame with data joined from all files passed.

See Also

Other Tools: autoline(), 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()

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

Value

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

See Also

Other Tools: autoline(), bind_files(), 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

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()

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

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

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

See Also

Other Tools: autoline(), bind_files(), bring_api(), 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

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

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

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

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

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

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

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

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: 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

Details

DataScience+ Post: 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 Correlations: corr(), corr_var()

Other Exploratory: corr_var(), crosstab(), df_str(), 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

# 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

Value

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

See Also

Other Exploratory: corr_cross(), 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

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

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_cross(), 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

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

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

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

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

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

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(), 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

Value

TRUE when successfully uploads file.

See Also

Other Tools: autoline(), bind_files(), bring_api(), 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

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_cross(), 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

Value

Numeric value result

See Also

Other Calculus: corr(), 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

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_cross(), 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, quiet = FALSE)

dont_sleep_time(quiet = FALSE)

Arguments

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(), 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

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

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

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

Value

No return value, called for side effects.

See Also

Other Tools: autoline(), bind_files(), bring_api(), 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

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(), 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

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

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

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

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

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

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

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

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

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")

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

Value

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

See Also

Other Scrapper: get_mp3(), gtrends_related(), holidays(), ip_data(), readGS(), splot_summary(), stocks_quote()

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

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

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(), 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)

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

Value

Boolean result of the existing fonts check.

See Also

Other Tools: autoline(), bind_files(), bring_api(), 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

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

Value

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

See Also

Other Tools: autoline(), bind_files(), bring_api(), 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

Value

String with format characters included.

Character. String vector with reformatted continuous numbers

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(), 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

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_cross(), 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

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_cross(), 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

Value

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

See Also

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

Other Exploratory: corr_cross(), 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

Value

Plot. Result of the frequency of combined variables.

See Also

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

Other Exploratory: corr_cross(), 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

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

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

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

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)

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

get_mp3(
  id,
  mp3 = TRUE,
  repo = "yt-dlp",
  params = "--no-check-certificate",
  start_time = 0,
  end_time = NA,
  overwrite = TRUE,
  open = FALSE,
  delete = open,
  info = TRUE,
  cover = FALSE,
  quiet = FALSE
)

Arguments

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: trim_mp3()

Examples

# You must have "youtube-dl" installed in your OS:
## Not run: 
# Download video from YouTube and convert to MP3
get_mp3("https://www.youtube.com/watch?v=lrlKcCdVw9Q")
# OR simply
get_mp3("lrlKcCdVw9Q")
# For dev version, use:
get_mp3("m3RX4LJh0iI", repo = "yt-dlp")

## End(Not run)

Get Tweets

Description

This function downloads tweets with personal credentials

Usage

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

Arguments

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

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

Value

Same as input but transformed (glued).

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(), 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

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 123@test.com", "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

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 123@test.com", "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

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

Value

Boolean of same length as x

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(), 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

Value

plot for Google Trend's results input gtrend.

Plot for Google Trend's results input gtrend.

See Also

Other Scrapper: filesGD(), get_mp3(), holidays(), ip_data(), 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",
  verbosity = NULL,
  ...
)

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

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

Arguments

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

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

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,
  ...
)

Arguments

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

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(), 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

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

Value

Boolean. Result of checking if device has internet connection.

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(), 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 scrap holiday dates from any country and year within +- 5 years. Thanks to timeanddate.com!

Usage

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

Arguments

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(), get_mp3(), gtrends_related(), ip_data(), 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)

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

Value

data.frame with meta-data for each image file.

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(), 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

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(), 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

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

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

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(), 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(), get_mp3(), gtrends_related(), holidays(), readGS(), splot_summary(), stocks_quote()