visdat is available on CRAN
install.packages("visdat")
If you would like to use the development version, install from github with:
# install.packages("devtools")
devtools::install_github("ropensci/visdat")
Initially inspired by
csv-fingerprint
,
vis_dat
helps you visualise a dataframe and “get a look at the data”
by displaying the variable classes in a dataframe as a plot with
vis_dat
, and getting a brief look into missing data patterns using
vis_miss
.
visdat
has 6 functions:
vis_dat()
visualises a dataframe showing you what the classes of the
columns are, and also displaying the missing data.
vis_miss()
visualises just the missing data, and allows for
missingness to be clustered and columns rearranged. vis_miss()
is
similar to missing.pattern.plot
from the
mi
package. Unfortunately
missing.pattern.plot
is no longer in the mi
package (as of
14/02/2016).
vis_compare()
visualise differences between two dataframes of the
same dimensions
vis_expect()
visualise where certain conditions hold true in your
data
vis_cor()
visualise the correlation of variables in a nice heatmap
vis_guess()
visualise the individual class of each value in your
data
vis_value()
visualise the value class of each cell in your data
vis_binary()
visualise the occurrence of binary values in your data
You can read more about visdat in the vignette, [“using visdat”]https://docs.ropensci.org/visdat/articles/using_visdat.html).
Please note that the visdat project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
vis_dat()
Let’s see what’s inside the airquality
dataset from base R, which
contains information about daily air quality measurements in New York
from May to September 1973. More information about the dataset can be
found with ?airquality
.
library(visdat)
vis_dat(airquality)
The plot above tells us that R reads this dataset as having numeric and
integer values, with some missing data in Ozone
and Solar.R
. The
classes are represented on the legend, and missing data represented by
grey. The column/variable names are listed on the x axis.
The vis_dat()
function also has a facet
argument, so you can create
small multiples of a similar plot for a level of a variable, e.g.,
Month:
vis_dat(airquality, facet = Month)
These currently also exist for vis_miss()
, and the vis_cor()
functions.
vis_miss()
We can explore the missing data further using vis_miss()
:
vis_miss(airquality)
Percentages of missing/complete in vis_miss
are accurate to the
integer (whole number). To get more accurate and thorough exploratory
summaries of missingness, I would recommend the
naniar
R package
You can cluster the missingness by setting cluster = TRUE
:
vis_miss(airquality,
cluster = TRUE)
Columns can also be arranged by columns with most missingness, by
setting sort_miss = TRUE
:
vis_miss(airquality,
sort_miss = TRUE)
vis_miss
indicates when there is a very small amount of missing data
at \<0.1% missingness:
test_miss_df <- data.frame(x1 = 1:10000,
x2 = rep("A", 10000),
x3 = c(rep(1L, 9999), NA))
vis_miss(test_miss_df)
vis_miss
will also indicate when there is no missing data at all:
vis_miss(mtcars)
To further explore the missingness structure in a dataset, I recommend
the naniar
package, which
provides more general tools for graphical and numerical exploration of
missing values.
vis_compare()
Sometimes you want to see what has changed in your data. vis_compare()
displays the differences in two dataframes of the same size. Let’s look
at an example.
Let’s make some changes to the chickwts
, and compare this new dataset:
set.seed(2019-04-03-1105)
chickwts_diff <- chickwts
chickwts_diff[sample(1:nrow(chickwts), 30),sample(1:ncol(chickwts), 2)] <- NA
vis_compare(chickwts_diff, chickwts)
Here the differences are marked in blue.
If you try and compare differences when the dimensions are different, you get an ugly error:
chickwts_diff_2 <- chickwts
chickwts_diff_2$new_col <- chickwts_diff_2$weight*2
vis_compare(chickwts, chickwts_diff_2)
# Error in vis_compare(chickwts, chickwts_diff_2) :
# Dimensions of df1 and df2 are not the same. vis_compare requires dataframes of identical dimensions.
vis_expect()
vis_expect
visualises certain conditions or values in your data. For
example, If you are not sure whether to expect values greater than 25 in
your data (airquality), you could write:
vis_expect(airquality, ~.x>=25)
, and you can see if there are times
where the values in your data are greater than or equal to 25:
vis_expect(airquality, ~.x >= 25)
This shows the proportion of times that there are values greater than 25, as well as the missings.
vis_cor()
To make it easy to plot correlations of your data, use vis_cor
:
vis_cor(airquality)
vis_value
vis_value()
visualises the values of your data on a 0 to 1 scale.
vis_value(airquality)
It only works on numeric data, so you might get strange results if you are using factors:
library(ggplot2)
vis_value(iris)
data input can only contain numeric values, please subset the data to the numeric values you would like. dplyr::select_if(data, is.numeric) can be helpful here!
So you might need to subset the data beforehand like so:
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
iris %>%
select_if(is.numeric) %>%
vis_value()
vis_binary()
vis_binary()
visualises binary values. See below for use with example
data, dat_bin
vis_binary(dat_bin)
If you don’t have only binary values a warning will be shown.
vis_binary(airquality)
Error in test_if_all_binary(data) :
data input can only contain binary values - this means either 0 or 1, or NA. Please subset the data to be binary values, or see ?vis_value.
vis_guess()
vis_guess()
takes a guess at what each cell is. It’s best illustrated
using some messy data, which we’ll make here:
messy_vector <- c(TRUE,
T,
"TRUE",
"T",
"01/01/01",
"01/01/2001",
NA,
NaN,
"NA",
"Na",
"na",
"10",
10,
"10.1",
10.1,
"abc",
"$%TG")
set.seed(2019-04-03-1106)
messy_df <- data.frame(var1 = messy_vector,
var2 = sample(messy_vector),
var3 = sample(messy_vector))
vis_guess(messy_df)
vis_dat(messy_df)
So here we see that there are many different kinds of data in your dataframe. As an analyst this might be a depressing finding. We can see this comparison above.
Thank you to Ivan Hanigan who first
commented
this suggestion after I made a blog post about an initial prototype
ggplot_missing
, and Jenny Bryan, whose
tweet got me
thinking about vis_dat
, and for her code contributions that removed a
lot of errors.
Thank you to Hadley Wickham for suggesting the use of the internals of
readr
to make vis_guess
work. Thank you to Miles McBain for his
suggestions on how to improve vis_guess
. This resulted in making it at
least 2-3 times faster. Thanks to Carson Sievert for writing the code
that combined plotly
with visdat
, and for Noam Ross for suggesting
this in the first place. Thank you also to Earo Wang and Stuart Lee for
their help in getting capturing expressions in vis_expect
.
Finally thank you to rOpenSci and it’s amazing onboarding process, this process has made visdat a much better package, thanks to the editor Noam Ross (@noamross), and the reviewers Sean Hughes (@seaaan) and Mara Averick (@batpigandme).