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
)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.
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.
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) {
library(nycflights13)
library(dplyr)
flights %>%
sample_n(1000) %>%
vis_dat()
flights %>%
slice(1:1000) %>%
vis_dat()
}