vis_dat gives you an at-a-glance ggplot object of what is inside a dataframe. Cells are coloured according to what class they are and whether the values are missing. As vis_dat returns a ggplot object, it is very easy to customize and change labels, and customize the plot

vis_dat(x, sort_type = TRUE, palette = "default", warn_large_data = TRUE,
  large_data_size = 9e+05)



a data.frame object


logical TRUE/FALSE. When TRUE (default), it sorts by the type in the column to make it easier to see what is in the data


character "default", "qual" or "cb_safe". "default" (the default) provides the stock ggplot scale for separating the colours. "qual" uses an experimental qualitative colour scheme for providing distinct colours for each Type. "cb_safe" is a set of colours that are appropriate for those with colourblindness. "qual" and "cb_safe" are drawn from


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


integer default is 900000, this can be changed. See note for more details


ggplot2 object displaying the type of values in the data frame and the position of any missing values.


Some datasets might be too large to plot, sometimes creating a blank plot - if this happens, I would recommend downsampling the data, either looking at the first 1,000 rows or by taking a random sample. This means that you won't get the same "look" at the data, but it is better than a blank plot! See example code for suggestions on doing this.

See also


# NOT RUN { # experimental colourblind safe palette vis_dat(airquality, palette = "cb_safe") vis_dat(airquality, palette = "qual") # if you have a large dataset, you might want to try downsampling: library(nycflight13) library(dplyr) flights %>% sample_n(1000) %>% vis_dat() flights %>% slice(1:1000) %>% vis_dat() # }