# install.packages("devtools")
devtools::install_github("njtierney/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
.
The name visdat
was chosen as I think in the future it could be integrated with testdat
. The idea being that first you visualise your data (visdat
), then you run tests from testdat
to fix them.
There are two main commands in the visdat
package:
-
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 tomissing.pattern.plot
from themi
package. Unfortunatelymissing.pattern.plot
is no longer in themi
package (well, as of 14/02/2016).
There are two experimental functions:
-
vis_guess()
has a guess at what the value of each cell, usingreadr::parse_guess
. This means that "10.1" will return "double", and10.1
will return "double", and01/01/01
will return "date". Keep in mind that it is a guess at what each cell is, so you can't trust this fully. -
vis_compare()
compares two dataframes, displaying the differences.
Let's see what's inside the airquality
dataset from base R.
library(visdat)
vis_dat(airquality)
#> dmap() is deprecated. Please use the new colwise family in dplyr.
#> E.g., summarise_all(), mutate_all(), etc.
The classes are represented on the legend, and missing data represented by grey. The column/variable names are listed on the x axis.
By default, vis_dat
sorts the columns according to the type of the data in the vectors. You can turn this off by setting sort_type = FALSE
.
vis_dat(airquality,
sort_type = FALSE)
#> dmap() is deprecated. Please use the new colwise family in dplyr.
#> E.g., summarise_all(), mutate_all(), etc.
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
.
To demonstrate what visdat looks like when you have different kinds of data, we can look at the dataset typical_data
, provided within visdat
, and created with the excellent wakefield
package.
vis_dat(typical_data)
#> dmap() is deprecated. Please use the new colwise family in dplyr.
#> E.g., summarise_all(), mutate_all(), etc.
#> Warning: attributes are not identical across measure variables; they will
#> be dropped
We can also look into using even wider data, looking at typical_larger_data
vis_dat(typical_larger_data)
#> dmap() is deprecated. Please use the new colwise family in dplyr.
#> E.g., summarise_all(), mutate_all(), etc.
#> Warning: attributes are not identical across measure variables; they will
#> be dropped
We can explore the missing data further using vis_miss()
.
vis_miss(airquality)
The percentages of missing/complete in vis_miss
are accurate to 1 decimal place.
You can cluster the missingness by setting cluster = TRUE
.
vis_miss(airquality,
cluster = TRUE)
The columns can also just be arranged by columns with most missingness, by setting sort_miss = TRUE
.
vis_miss(airquality,
sort_miss = TRUE)
When there is <0.1% of missingness, vis_miss
indicates that there is >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)
#> Warning: attributes are not identical across measure variables; they will
#> be dropped
vis_miss
will also indicate when there is no missing data at all.
vis_miss(mtcars)
Sometimes you want to see what has changed in your data. vis_compare()
helps with that. It is currently only just barely working, so keep in mind that this is very much in its beta stages.
For the sake of simplicity, lets make some changes to iris
, and compare this new dataset
iris_diff <- iris
iris_diff[sample(1:150, 30),sample(1:4, 2)] <- NA
vis_compare(iris_diff, iris)
#> vis_compare is still in BETA! If you have suggestions or errors,
#> post an issue at https://github.com/njtierney/visdat/issues
#> Warning in if (dim(df1) != dim(df2)) {: the condition has length > 1 and
#> only the first element will be used
#> dmap() is deprecated. Please use the new colwise family in dplyr.
#> E.g., summarise_all(), mutate_all(), etc.
#> Warning: attributes are not identical across measure variables; they will
#> be dropped
#> Warning: attributes are not identical across measure variables; they will
#> be dropped
Here the differences are marked in blue.
If you try and compare differences when the dimensions are different, you get an ugly error.
iris_diff_2 <- iris
iris_diff_2$new_col <- iris$Sepal.Length + iris$Sepal.Width
vis_compare(iris, iris_diff_2)
#> vis_compare is still in BETA! If you have suggestions or errors, post an issue at https://github.com/njtierney/visdat/issuesthe condition has length > 1 and only the first element will be usedError: `.x` (5) and `.y` (6) are different lengths
vis_guess()
takes a guess at what each cell is. It's best illustrated using some messy data, which we'll make here.
messy_vector <- c(TRUE,
T,
"TRUE",
"T",
"01/01/01",
"01/01/2001",
NA,
NaN,
"NA",
"Na",
"na",
"10",
10,
"10.1",
10.1,
"abc",
"$%TG")
set.seed(1114)
messy_df <- data.frame(var1 = messy_vector,
var2 = sample(messy_vector),
var3 = sample(messy_vector))
vis_guess(messy_df)
#> vis_guess is still in BETA! If you have suggestions or errors,
#> post an issue at https://github.com/njtierney/visdat/issues
So here we see that there are many different kinds of data in your dataframe. As an analyst this might be a depressing finding. Compare this to vis_dat
.
vis_dat(messy_df)
#> dmap() is deprecated. Please use the new colwise family in dplyr.
#> E.g., summarise_all(), mutate_all(), etc.
Here, you might just assume your data is weird because it's all factors - or worse, not notice that this is a problem.
At the moment vis_guess
is very slow. Please take this into consideration when you are using it on data with more than 1000 rows. We're looking into ways of making it faster, potentially using methods from the parallel
package, or extending the c++ code from readr:::collectorGuess
.
Thanks to Carson Sievert, you can now add some really nifty interactivity into visdat
by using plotly::ggplotly
, allowing for information to be revealed upon mouseover of a cell. The code to do this can be seen below, but is not shown as the github README doesn't support HTML interactive graphics...yet.
library(plotly)
vis_dat(airquality) %>% ggplotly()
This is still under development, but it is basically a faster version of doing a ggplot and then calling ggplotly.
This is also under development, and still needs some more work on the legend, etc.
vis_miss_ly(airquality)
Visualising expectations
The idea here is to pass expectations into vis_dat
or vis_miss
, along the lines of the expectation
command in assertr
. For example, you could ask vis_dat
to identify those cells with values of -1 with something like this:
data %>%
expect(value == -1) %>%
vis_dat
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 also to Carson Sievert for writing the code that combined plotly
with visdat
, and for Noam Ross for suggesting this in the first place.