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Crosstable is a package centered on a single function, crosstable(), which easily computes descriptive statistics on datasets.

Before starting this vignette, if you are not familiar with dplyr and pipes (%>%, Ctrl+Shift+M in RStudio), I warmly recommend you to read the vignette, or, if you can read French, Julien Barnier’s awesome tidyverse tutorial. Nevertheless, this vignette should still be easy to understand otherwise, as crosstable is perfectly usable with base R.

Example dataset: modified mtcars

First, since crosstable() uses the power of the label attribute, let’s start by building a labelled dataset.

In this vignette, we will use a modified version of the mtcars famous dataset, which comprises 11 aspects of design and performance for 32 automobiles. Let’s modify it to add textual categories, keep row names as a column, make some numeric variables factors, and add labels from a table using import_labels().

For convenience, this dataset is already packed into {crosstable} as ?mtcars2 so don’t bother re-creating it for your own tests.

mtcars_labels = read.table(header=TRUE, text="
  name  label
  model 'Model'
  mpg   'Miles/(US) gallon'
  cyl   'Number of cylinders'
  disp  'Displacement ('
  hp    'Gross horsepower'
  drat  'Rear axle ratio'
  wt    'Weight (1000 lbs)'
  qsec  '1/4 mile time'
  vs    'Engine'
  am    'Transmission'
  gear  'Number of forward gears'
  carb  'Number of carburetors'
mtcars2 = mtcars %>% 
         vs=ifelse(vs==0, "vshaped", "straight"),
         am=ifelse(am==0, "auto", "manual"), 
         across(c("cyl", "gear"), factor),
         .before=1) %>% 
  import_labels(mtcars_labels, name_from="name", label_from="label") %>% 
#I also could have used `labelled::set_variable_labels()` to add labels

First overview

As a first example, let’s describe the columns mpg and cyl, grouping by the column am (the transmission).

As tables are not very readable in the console, let’s also use as_flextable() to turn the resulting crosstable into a beautiful, ready-to-print HTML table. This table will be automatically displayed in the Viewer pane if your are using RStudio.

crosstable(mtcars2, c(mpg, cyl), by=am) %>%

By default, numeric variables (like mpg and disp) are described with min/max, median/IQR, mean/sd and number of observations/missing, while categorical (factor/character) variables (like cyl) are described with levels counts and fractions. All of this is fully customizable, as you will see hereafter.

There are many ways to select variables: with names, character vector, tidyselect helpers, formula… This is described in details in vignette("crosstable-selection").

The by column is usually a factor, character or logical vector. If it is a numeric vector, then only numeric vectors can be described and correlation coefficients will be displayed. While it is possible to apply several variables (by=c(am, vs)), I will use only one variable here for clarity.

In this vignette, I will often set keep_id=TRUE so you can see the variable name, but in practice you usually omit it. See vignette("crosstable-report") for more about as_flextable() and on how to integrate crosstables in MS Word document (using officer) and Rmarkdown. On the other hand, you can set label=FALSE if you don’t want them to appear.


To display totals, use the total argument as one of c("none", "row", "column", "both").

#of course, the total of a "column" in only meaningful for categorical variables.
crosstable(mtcars2, c(am, mpg), by=vs, total="both") %>% 

Note that totals always take missing values into account. Therefore, be aware that if showNA="no", totals may be higher than the sum of the values inside the table.

Controlling the description

The crosstable() function comes with a lot of arguments to add control on how it will describe your dataset. Some arguments are tied to a type of variable and will only apply for the descriptions of those.

Note that most of the parameters can be controlled using global options. This comes handy when you want to use the same parameterization on all your crosstables. See ?crosstable_options for more details about this.

Categorical variables

Categorical variables are described using counts and percentages. A numeric variable is considered categorical if its number of levels is lesser than the unique_numeric argument (default=3).

If by is set, you can use the margin argument to control whether percentages should be calculated by row (default), column, or cell. You can also use percent_digits (default=2) to control how many decimals should be displayed.

Use showNA (one of c("ifany", "always", "no")) to control when missing values should be displayed.

mtcars3 = mtcars2
mtcars3$cyl[1:5] = NA
crosstable(mtcars3, c(am, cyl), by=vs, showNA="always", 
           percent_digits=0, percent_pattern="{n} ({p_col}/{p_row})") %>% 

You can see that missing values are never taken into account when calculating percentage calculation in R. You can change this behaviour by using tidyr::replace_na() or forcats::fct_explicit_na() on your dataset before applying crosstable().

Numeric variables

Numeric variables are described with summary functions. By default, it outputs min/max, median/IQR, mean/sd and number of observations/missing.

However, this might not be the information you need, so you can use the funs argument to apply the set of functions of your choice:

crosstable(mtcars2, c(mpg, wt), funs=c(median, mean, "std dev"=sd)) %>% 

To this end, you might want to use crosstable’s convenience functions such as meansd(), meanCI(), mediqr(), minmax(), or nna().

For more advanced use cases, you can also use anonymous and lambda functions, for instance crosstable(mtcars2, c(mpg, wt), funs=c("mean square"=function(xx) mean(xx^2), "mean cube"= ~mean(.x^3))).

If some functions need additional arguments, you can use the funs_arg argument. For instance, you could write crosstable(mtcars2, c(disp, hp), funs=c(quantile), funs_arg=list(probs=c(0.25,0.75))).

Numbers are formatted to have the same number of decimal places. You can use funs_arg=list(dig=3) to customize the number of decimals. You might want to take a look at ?summaryFunctions and ?format_fixed.

On the other hand, if by refers to a numeric variable, correlation coefficients will be calculated.

crosstable(mtcars2, where(is.numeric), cor_method="pearson", by=mpg) %>% 

You can use the cor_method argument to choose which coefficient to calculate ("pearson", "kendall", or "spearman").

Other variables

Survival data

Crosstable is also able to describe survival data.

Use times to set the specific times of interest and followup to compute the median followup. For each time, you will get the survival, the number of events before this time, and the number of patients at risk, as per survival:::summary.survfit().

aml$surv = Surv(aml$time, aml$status)
crosstable(aml, surv, by=x, times=c(0,50,150), followup=TRUE) %>% 

Note that, using the formula interface, you could declare the Surv object directly inside the crosstable function: crosstable(aml, Surv(time, status) ~ x).


Although less usual, you can describe variables of call Date or POSIXt with crosstable(). Use date_format to apply a specific format (see ?strptime for formats). Beside this, they are considered as numeric variables.

mtcars2$x_date = as.Date(mtcars2$hp , origin="2010-01-01") %>% set_label("Date")
mtcars2$x_posix = as.POSIXct(mtcars2$qsec*3600*24 , origin="2010-01-01") %>% set_label("Date+time")
crosstable(mtcars2, c(x_date, x_posix), date_format="%d/%m/%Y") %>% 

For the standard deviation to be readable, date unit is chosen automatically among ["seconds", "minutes", "hours", "days", "months", "years"]. If you don’t want two groups to have different date unit, you can set it globally using the date_unit key in funs_arg:

crosstable(mtcars2, c(x_date, x_posix), funs=meansd, funs_arg=list(date_unit="days")) %>% 


If there is only one by variable with only 2 levels, it is possible to automatically compute an effect-size, most often along with its confidence interval.

crosstable(mtcars2, c(vs, qsec), by=am, funs=mean, effect=TRUE) %>% 

Type of effect (method, bootstrap, …) are also chosen depending on the characteristics of the crossed variables (class, size, distribution, …).

See crosstable_effect_args() for more details on the effect choice algorithm and how to customize it.


It is also possible to perform statistical tests automatically.

crosstable(mtcars2, c(vs, qsec), by=am, funs=mean, test=TRUE) %>% 

Of course, this should only be done in an exploratory context, as it would cause extensive alpha inflation otherwise.

Tests are chosen depending on the characteristics of the crossed variables (class, size, distribution, …).

See crosstable_test_args() for more details on the test choice algorithm.