visdat (version 0.6.0)

vis_miss: Visualise a data.frame to display missingness.

Description

vis_miss provides an at-a-glance ggplot of the missingness inside a dataframe, colouring cells according to missingness, where black indicates a missing cell and grey indicates a present cell. As it returns a ggplot object, it is very easy to customize and change labels.

Usage

vis_miss(
  x,
  cluster = FALSE,
  sort_miss = FALSE,
  show_perc = TRUE,
  show_perc_col = TRUE,
  large_data_size = 9e+05,
  warn_large_data = TRUE,
  facet
)

Value

ggplot2 object displaying the position of missing values in the dataframe, and the percentage of values missing and present.

Arguments

x

a data.frame

cluster

logical. TRUE specifies that you want to use hierarchical clustering (mcquitty method) to arrange rows according to missingness. FALSE specifies that you want to leave it as is. Default value is FALSE.

sort_miss

logical. TRUE arranges the columns in order of missingness. Default value is FALSE.

show_perc

logical. TRUE now adds in the \ in the whole dataset into the legend. Default value is TRUE.

show_perc_col

logical. TRUE adds in the \ column into the x axis. Can be disabled with FALSE. Default value is TRUE. No missingness percentage column information will be presented when facet argument is used. Please see the naniar package to provide missingness summaries over groups.

large_data_size

integer default is 900000 (given by `nrow(data.frame) * ncol(data.frame)``). This can be changed. See note for more details.

warn_large_data

logical - warn if there is large data? Default is TRUE see note for more details

facet

(optional) bare variable name, if you want to create a faceted plot, with one plot per level of the variable. No missingness percentage column information will be presented when facet argument is used. Please see the naniar package to provide missingness summaries over groups.

Details

The missingness summaries in the columns are rounded to the nearest integer. For more detailed summaries, please see the summaries in the naniar R package, specifically, naniar::miss_var_summary().

See Also

vis_dat() vis_guess() vis_expect() vis_cor() vis_compare()

Examples

vis_miss(airquality)

vis_miss(airquality, cluster = TRUE)

vis_miss(airquality, sort_miss = TRUE)

vis_miss(airquality, facet = Month)

if (FALSE) {
# if you have a large dataset, you might want to try downsampling:
library(nycflights13)
library(dplyr)
flights %>%
  sample_n(1000) %>%
  vis_miss()

flights %>%
  slice(1:1000) %>%
  vis_miss()
}