Create preliminary exploratory data visualisations of an entire dataset to identify problems or unexpected features using 'ggplot2'.
visdat is available on CRAN
If you would like to use the development version, install from github with:
Initially inspired by
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
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.
missing.pattern.plot from the
missing.pattern.plot is no longer in the
mi package (as of 14/02/2016).
vis_compare() visualise differences between two dataframes of the
vis_expect() visualise where certain conditions hold true in your
vis_cor() visualise the correlation of variables in a nice heatmap
vis_guess() visualise the individual class of earch value in your
You can read more about visdat in the vignette, “using visdat”.
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
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
The plot above tells us that R reads this dataset as having numeric and
integer values, with some missing data in
classes are represented on the legend, and missing data represented by
grey. The column/variable names are listed on the x axis.
We can explore the missing data further using
Percentages of missing/complete in
vis_miss are accurate to 1 decimal
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
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:
To further explore the missingness structure in a dataset, I recommend
naniar package, which
provides more general tools for graphical and numerical exploration of
Sometimes you want to see what has changed in your data.
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:
chickwts_diff <- chickwtschickwts_diff[sample(1:nrow(chickwts), 30),sample(1:ncol(chickwts), 2)] <- NAvis_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 <- chickwtschickwts_diff_2$new_col <- chickwts_diff_2$weight*2vis_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 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
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.
To make it easy to plot correlations of your data, use
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(1114)messy_df <- data.frame(var1 = messy_vector,var2 = sample(messy_vector),var3 = sample(messy_vector))
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
this suggestion after I made a blog post about an initial prototype
ggplot_missing, and Jenny Bryan, whose
tweet got me
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
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
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).
vis_cor()to use perceptually uniform colours from
scico::scico(3, palette = "vik").
vis_cor()to have fixed legend values from -1 to +1 (#110) using options
limits. Special thanks to this SO thread for the answer
guess_parser, to not guess integer types by default. To opt-into the current behavior you need to pass
guess_integer = TRUE.
vis_compare()for comparing two dataframes of the same dimensions
vis_expect()for visualising where certain values of expectations occur in the data
vis_expectto show the percentage of expectations that are TRUE. #73
vis_corto visualise correlations in a dataframe
vis_guess()for displaying the likely type for each cell in a dataframe
vis_expectto make it easy to look at certain appearances of numbers in your data.
vis_corto use argument
vis_miss_ly- thanks to Stuart Lee
Fix bug reported in #75
seq_len(nrow(x)) inside internal
vis_gather_, used to calculate the row numbers. Using
mutate(rows = dplyr::row_number()) solved the issue.
Fix bug reported in #72
vis_miss errored when one column was given to it. This was an issue
scale_x_discrete - which is used to order the
columns of the data. It is not necessary to order one column of data, so I
created an if-else to avoid this step and return the plot early.
Fix visdat x axis alignment when show_perc_col = FALSE - #82
fix visdat x axis alignment - issue 57
fix bug where the column percentage missing would print to be NA when it was exactly equal to 0.1% missing. - issue 62
vis_cor didn't gather variables for plotting appropriately - now fixed
add_vis_dat_pal()(internal) to add a palette for
vis_guessnow gets a palette argument like
plotlyvis_*_ly interactive graphs:
vis_compare_ly()These simply wrap
plotly::ggplotly(vis_*(data)). In the future they will be written in
plotlyso that they can be generated much faster
vis_family are now flipped by default
vis_missnow shows the % missingness in a column, can be disabled by setting
show_perc_colargument to FALSE
flipargument, as this should be the default
vdiffr. Code coverage is now at 99%
paper.mdwritten and submitted to JOSS
flip = TRUE, to
vis_miss. This flips the x axis and the ordering of the rows. This more closely resembles a dataframe.
vis_miss_lyis a new function that uses plotly to plot missing data, like
vis_miss, but interactive, without the need to call
plotly::ggplotlyon it. It's fast, but at the moment it needs a bit of love on the legend front to maintain the style and features (clustering, etc) of current
vis_missnow gains a
show_percargument, which displays the % of missing and complete data. This is switched on by default and addresses issue #19.
vis_compareis a new function that allows you to compare two dataframes of the same dimension. It gives a fairly ugly warning if they are not of the same dimension.
vis_datgains a "palette" argument in line with issue 26, drawn from http://colorbrewer2.org/, there are currently three arguments, "default", "qual", and "cb_safe". "default" provides the ggplot defaults, "qual" uses some colour blind unfriendly colours, and "cb_safe" provides some colours friendly for colour blindness.
1:rnow(x)and replaced with
vis_dat_ly, as it currently does not work.
vis_compareare very beta
vis_compareto be different to the ggplot2 standards.
vis_misslegend labels are created using the internal function
miss_guide_labelwill check if data is 100% missing or 100% present and display this in the figure. Additionally, if there is less than 0.1% missing data, "<0.1% missingness" will also be displayed. This sort of gets around issue #18 for the moment.
miss_guide_labellegend labels function.
mutate_each_(). This solves issue #3 where
vis_datcouldn't take variables with spaces in their name.
vis_misswere updated so that you can make them all interactive using the latest dev version of
plotlyfrom Carson Sievert.
vis_guess(), a function that uses the unexported function