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Visualises a data.frame to tell you what it contains.

Source: R/vis-dat.R
vis_dat.Rd

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

Usage

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

Arguments

x

a data.frame object

sort_type

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

palette

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 http://colorbrewer2.org/.

warn_large_data

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

large_data_size

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

facet

bare variable name for a variable you would like to facet by. By default there is no facetting. Only one variable can be facetted. You can get the data structure using data_vis_dat and the facetted structure by using group_by and then data_vis_dat.

Value

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

Note

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

vis_miss() vis_guess() vis_expect() vis_cor() vis_compare()

Examples


vis_dat(airquality)


# 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:
if (FALSE) { # \dontrun{
library(nycflights13)
library(dplyr)
flights %>%
  sample_n(1000) %>%
  vis_dat()

flights %>%
  slice(1:1000) %>%
  vis_dat()
} # }