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The vtable package is designed to help you quickly and efficiently look at and document your data.
There are three main functions in vtable:
vtable, or vt for short, shows you
information about the variables in your data set, including variable
labels, in a way that is easy to use “find in page” to search through.
It was designed to be similar to Stata’s “Variables” panel.sumtable or st for short, provides a table
of summary statistics. It is very similar in spirit to the summary
statistics function of stargazer::stargazer() except that
it accepts tibbles, handles factor variables, and makes
by-group statistics and group tests easy.labeltable provides a table of value labels, either for
variables labelled with sjlabelled or
haven or similar, or for when you want to see how the
values of one column line up with the values of another.All three of these functions are built with the intent of being fast. Not so much fast to run, but fast to use. The defaults are intended to be good defaults, and the output by default prints to the Viewer tab (in RStudio) or the browser (outside RStudio) so you can see it immediately, and continue to look at it as you work on your data.
You could almost certainly build your own highly-customized version
of vtable, But why do that when you can just do
vt(df) and see the information you need to see? And there
are eight million packages that make summary statistics tables to your
exact specifications if you tweak them. But there’s a good chance that
st(df) does what you want. If you want something real out
there, that’s when you can break out the big guns.
All three main vtable functions can produce HTML,
LaTeX, data.frame, CSV, or knitr::kable()
output.
You can install vtable from CRAN. Note that the documentation on this site refers to the development version, and so may not work perfectly for the CRAN version. But the two will usually be the same.:
install.packages("vtable")The development version can be installed from GitHub:
# install.packages("remotes")
remotes::install_github("NickCH-K/vtable")I’ll just do a brief example here, using the iris we all
know and love. Output will be to kable since this is an
RMarkdown document.
data(iris)
# Basic vtable
vt(iris)| Name | Class | Values |
|---|---|---|
| Sepal.Length | numeric | Num: 4.3 to 7.9 |
| Sepal.Width | numeric | Num: 2 to 4.4 |
| Petal.Length | numeric | Num: 1 to 6.9 |
| Petal.Width | numeric | Num: 0.1 to 2.5 |
| Species | factor | ‘setosa’ ‘versicolor’ ‘virginica’ |
There are plenty of options if we want to go nuts, but let’s keep it
simple and just ask for a little more with lush
vt(iris, lush = TRUE)| Name | Class | Values | Missing | Summary |
|---|---|---|---|---|
| Sepal.Length | numeric | Num: 4.3 to 7.9 | 0 | mean: 5.843, sd: 0.828, nuniq: 35 |
| Sepal.Width | numeric | Num: 2 to 4.4 | 0 | mean: 3.057, sd: 0.436, nuniq: 23 |
| Petal.Length | numeric | Num: 1 to 6.9 | 0 | mean: 3.758, sd: 1.765, nuniq: 43 |
| Petal.Width | numeric | Num: 0.1 to 2.5 | 0 | mean: 1.199, sd: 0.762, nuniq: 22 |
| Species | factor | ‘setosa’ ‘versicolor’ ‘virginica’ | 0 | nuniq: 3 |
Let’s stick with iris!
# Basic summary stats
st(iris)| Variable | N | Mean | Std. Dev. | Min | Pctl. 25 | Pctl. 75 | Max |
|---|---|---|---|---|---|---|---|
| Sepal.Length | 150 | 5.843 | 0.828 | 4.3 | 5.1 | 6.4 | 7.9 |
| Sepal.Width | 150 | 3.057 | 0.436 | 2 | 2.8 | 3.3 | 4.4 |
| Petal.Length | 150 | 3.758 | 1.765 | 1 | 1.6 | 5.1 | 6.9 |
| Petal.Width | 150 | 1.199 | 0.762 | 0.1 | 0.3 | 1.8 | 2.5 |
| Species | 150 | ||||||
| … setosa | 50 | 33.3% | |||||
| … versicolor | 50 | 33.3% | |||||
| … virginica | 50 | 33.3% |
Note that sumtable allows for much more customization
than vtable since there’s a heightened chance you want it
for a paper or something. But I’ll leave that to the more detailed
documentation. For now just note it does by-group stats, either in
“group.long” format (multiple sumtables
stacked on top of each other), or by default, in columns, with an option
to add a group test.
Grouped sumtables look a little nicer in formats that
suport multi-column cells like HTML and LaTeX.
These tables include multi-column cells, which are not
supported in the kable output, but are supported by
vtable’s dftoHTML and dftoLaTeX
functions. They look nicer in the HTML or LaTeX output.
st(iris,
group = 'Species',
group.test = TRUE)| Variable | N | Mean | SD | N | Mean | SD | N | Mean | SD | Test |
|---|---|---|---|---|---|---|---|---|---|---|
| Sepal.Length | 50 | 5.006 | 0.352 | 50 | 5.936 | 0.516 | 50 | 6.588 | 0.636 | F=119.265*** |
| Sepal.Width | 50 | 3.428 | 0.379 | 50 | 2.77 | 0.314 | 50 | 2.974 | 0.322 | F=49.16*** |
| Petal.Length | 50 | 1.462 | 0.174 | 50 | 4.26 | 0.47 | 50 | 5.552 | 0.552 | F=1180.161*** |
| Petal.Width | 50 | 0.246 | 0.105 | 50 | 1.326 | 0.198 | 50 | 2.026 | 0.275 | F=960.007*** |
| Statistical significance markers: * p<0.1; ** p<0.05; *** p<0.01 |
For this we’ll need labeled values.
data(efc, package = 'sjlabelled')
# Now shoot - how was gender coded?
labeltable(efc$e16sex)| e16sex | Label |
|---|---|
| 1 | male |
| 2 | female |
labeltable can also be used to see, for values of one
variable, what values are present of other variables. This is intended
for use if one variable is a recode, simplification, or lost-labels
version of another, but hey, go nuts.
labeltable(efc$e15relat,efc$e16sex,efc$e42dep)| e15relat | e16sex | e42dep |
|---|---|---|
| 1 | 2, 1 | 3, 4, 1, 2, NA |
| 2 | 2, 1, NA | 3, 4, 2, 1 |
| 3 | 1, 2 | 3, 2, 1, 4 |
| 4 | 2, 1 | 4, 3, 2, 1 |
| 5 | 2, 1 | 3, 2, 1, 4 |
| 6 | 2, 1 | 4, 3, 1, 2 |
| 7 | 2, 1 | 4, 3, 2, 1 |
| 8 | 2, 1 | 3, 4, 2, 1 |
| NA | 2, NA | 3, NA |
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.