lares (version 5.0.6)

distr: Compare Variables with their Distributions

Description

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

Usage

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

Arguments

data

Dataframe

...

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

type

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

ref

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

note

Character. Caption for the plot.

top

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

breaks

Integer. Number of splits for numerical values.

na.rm

Boolean. Ignore NAs if needed.

force

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

trim

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

clean

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

abc

Boolean. Do you wish to sort by alphabetical order?

custom_colours

Boolean. Use custom colours function?

plot

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

chords

Boolean. Use a chords plot?

save

Boolean. Save the output plot in our working directory

subdir

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

Value

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

See Also

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

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

Examples

# NOT RUN {
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)
# }