Visualize pareto chart for variables with missing value.

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

# \donttest{ # 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))
#> # A tibble: 14 x 6 #> variables types missing_count missing_percent unique_count unique_rate #> <chr> <chr> <int> <dbl> <int> <dbl> #> 1 company_type factor 291 29.1 7 0.007 #> 2 company_size ordered 278 27.8 9 0.009 #> 3 gender factor 236 23.6 4 0.004 #> 4 major_discipl… factor 137 13.7 7 0.007 #> 5 education_lev… ordered 21 2.1 6 0.006 #> 6 last_new_job ordered 17 1.7 7 0.007 #> 7 enrolled_univ… factor 12 1.2 4 0.004 #> 8 experience ordered 4 0.4 23 0.023 #> 9 enrollee_id charac… 0 0 1000 1 #> 10 city factor 0 0 94 0.094 #> 11 city_dev_index numeric 0 0 73 0.073 #> 12 relevent_expe… factor 0 0 2 0.002 #> 13 training_hours integer 0 0 191 0.191 #> 14 job_chnge factor 0 0 2 0.002
# 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)
#> # A tibble: 8 x 5 #> variable frequencies ratio grade cumulative #> <fct> <int> <dbl> <fct> <dbl> #> 1 company_type 291 0.291 Bad 29.2 #> 2 company_size 278 0.278 Bad 57.1 #> 3 gender 236 0.236 Bad 80.8 #> 4 major_discipline 137 0.137 NotBad 94.6 #> 5 education_level 21 0.021 Good 96.7 #> 6 last_new_job 17 0.017 Good 98.4 #> 7 enrolled_university 12 0.012 Good 99.6 #> 8 experience 4 0.004 Good 100
# Non typographic elements plot_na_pareto(jobchange2, typographic = FALSE)
# }