/ Data Visualization

Preliminary Exploratory Visualisation of Data

Preliminary Exploratory Visualisation of Data

visdat

Preliminary Exploratory Visualisation of Data.

How to install

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")

What does visdat do?

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 earch value in your
    data

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.

Examples

Using 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.

Using 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 1 decimal
place.

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.

Using 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.

Using 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.

Using vis_cor()

To make it easy to plot correlations of your data, use vis_cor:


vis_cor(airquality)

Using 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()

Using 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.

GitHub