plot_na_pareto: Pareto chart for missing value

In dlookr: Tools for Data Diagnosis, Exploration, Transformation

Pareto chart for missing value

Description

Visualize pareto chart for variables with missing value.

Usage

plot_na_pareto(
  x,
  only_na = FALSE,
  relative = FALSE,
  main = NULL,
  col = "black",
  grade = list(Good = 0.05, OK = 0.1, NotBad = 0.2, Bad = 0.5, Remove = 1),
  plot = TRUE,
  typographic = TRUE,
  base_family = NULL
)

Arguments

x	data frames, or objects to be coerced to one.
only_na	logical. The default value is FALSE. If TRUE, only variables containing missing values are selected for visualization. If FALSE, all variables are included.
relative	logical. If this argument is TRUE, it sets the unit of the left y-axis to relative frequency. In case of FALSE, set it to frequency.
main	character. Main title.
col	character. The color of line for display the cumulative percentage.
grade	list. Specifies the cut-off to set the grade of the variable according to the ratio of missing values. The default values are Good: [0, 0.05], OK: (0.05, 0.1], NotBad: (0.1, 0.2], Bad: (0.2, 0.5], Remove: (0.5, 1].
plot	logical. If this value is TRUE then visualize plot. else if FALSE, return aggregate information about missing values.
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.
base_family	character. The name of the base font family to use for the visualization. If not specified, the font defined in dlookr is applied. (See details)

Details

The base_family is selected from "Roboto Condensed", "Liberation Sans Narrow", "NanumSquare", "Noto Sans Korean". If you want to use a different font, use it after loading the Google font with import_google_font().

Examples

# Generate data for the example
set.seed(123L)
jobchange2 <- jobchange[sample(nrow(jobchange), size = 1000), ]

# Diagnose the data with missing_count using diagnose() function
library(dplyr)

jobchange2 %>% 
  diagnose %>% 
  arrange(desc(missing_count))

# Visualize pareto chart for variables with missing value.
plot_na_pareto(jobchange2)

# Visualize pareto chart for variables with missing value.
plot_na_pareto(jobchange2, col = "blue")

# Visualize only variables containing missing values
plot_na_pareto(jobchange2, only_na = TRUE)

# Display the relative frequency 
plot_na_pareto(jobchange2, relative = TRUE)

# Change the grade
plot_na_pareto(jobchange2, grade = list(High = 0.1, Middle = 0.6, Low = 1))

# Change the main title.
plot_na_pareto(jobchange2, relative = TRUE, only_na = TRUE, 
                 main = "Pareto Chart for jobchange")
  
# Return the aggregate information about missing values.
plot_na_pareto(jobchange2, only_na = TRUE, plot = FALSE)

# Non typographic elements
plot_na_pareto(jobchange2, typographic = FALSE)

dlookr documentation built on July 9, 2023, 6:31 p.m.