lares (version 4.8.4)

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

type

Integer. 1 for both plots, 2 for counter plot only, 3 por 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 lares::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?

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(), summer(), tree_var(), trendsRelated()

Other Visualization: freqs_df(), freqs_list(), freqs_plot(), freqs(), gg_bars(), gg_pie(), noPlot(), plot_chord(), plot_survey(), plot_timeline(), summer(), theme_lares2(), theme_lares(), tree_var()

Examples

# NOT RUN {
options("lares.font" = NA) # Temporal
data(dft) # Titanic dataset

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

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

# Distribution of numerical only
dft %>% dplyr::filter(Fare < 200) %>% distr(Fare)

# Distribution of categorical only - uses freqs()
dft %>% distr(Survived, force = "char")

# Distribution of numerical/numerical
dft %>% dplyr::mutate(logFare = log(Fare)) %>% distr(Fare, logFare)

# Select only one of the two default plots of distr()
# }
# NOT RUN {
dft %>% distr(Survived, Age, type = 2)
dft %>% distr(Survived, Age, type = 3)
# }