dlookr (version 0.5.0)

plot_bar_category: Plot bar chart of categorical variables

Description

The plot_bar_category() to visualizes the distribution of categorical data by level or relationship to specific numerical data by level.

Usage

plot_bar_category(.data, ...)
# S3 method for data.frame
plot_bar_category(
  .data,
  ...,
  top = 10,
  add_character = TRUE,
  title = "Frequency by levels of category",
  each = FALSE,
  typographic = TRUE
)
# S3 method for grouped_df
plot_bar_category(
  .data,
  ...,
  top = 10,
  add_character = TRUE,
  title = "Frequency by levels of category",
  each = FALSE,
  typographic = TRUE
)

Arguments

.data

a data.frame or a tbl_df or a grouped_df.

…

one or more unquoted expressions separated by commas. You can treat variable names like they are positions. Positive values select variables; negative values to drop variables. If the first expression is negative, plot_bar_category() will automatically start with all variables. These arguments are automatically quoted and evaluated in a context where column names represent column positions. They support unquoting and splicing.

top

an integer. Specifies the upper top rank to extract. Default is 10.

add_character

logical. Decide whether to include text variables in the diagnosis of categorical data. The default value is TRUE, which also includes character variables.

title

character. a main title for the plot.

each

logical. Specifies whether to draw multiple plots on one screen. The default is FALSE, which draws multiple plots on one screen.

typographic

logical. Whether to apply focuses on typographic elements to ggplot2 visualization. The default is TRUE. if TRUE provides a base theme that focuses on typographic elements using hrbrthemes package.

Details

The distribution of categorical variables can be understood by comparing the frequency of each level. The frequency table helps with this. As a visualization method, a bar graph can help you understand the distribution of categorical data more easily than a frequency table.

Examples

# NOT RUN {
# Generate data for the example
heartfailure2 <- heartfailure
heartfailure2[sample(seq(NROW(heartfailure2)), 20), "platelets"] <- NA
heartfailure2[sample(seq(NROW(heartfailure2)), 5), "smoking"] <- NA

set.seed(123)
heartfailure2$test <- sample(LETTERS[1:15], 299, replace = TRUE)
heartfailure2$test[1:30] <- NA

# Visualization of all numerical variables
plot_bar_category(heartfailure2)

# Select the variable to diagnose
# plot_bar_category(heartfailure2, "test", "smoking")
# plot_bar_category(heartfailure2, -test, -smoking)

# Visualize the each plots
# plot_bar_category(heartfailure2, each = TRUE)

# Not allow typographic argument
# plot_bar_category(heartfailure2, typographic = FALSE)

# Using pipes ---------------------------------
library(dplyr)

# Plot of all categorical variables
# heartfailure2 %>%
#   plot_bar_category()

# Visualize just 7 levels of top frequency
# heartfailure2 %>%
#   plot_bar_category(top = 7)
  
# Visualize only factor, not character
# heartfailure2 %>%
#   plot_bar_category(add_character = FALSE) 

# Using groupd_df  ------------------------------
# heartfailure2 %>% 
#   group_by(death_event) %>% 
#   plot_bar_category(top = 5)
  
# heartfailure2 %>% 
#   group_by(death_event) %>% 
#   plot_bar_category(each = TRUE, top = 5)  
  
# }