Select variables

Whole table

The simplest case is when you want to describe the whole table, as you need no further argument. If you really want to be more explicit, you also can use tidyselect::everything(). All tidyselect helpers are re-exported by dplyr so we only want to load this latter package.

Here are the 10 first lines of the iris2 dataset:

library(crosstable)
ct = crosstable(iris2, everything()) #or simply `crosstable(iris2)`
ct %>% 
  as_flextable(keep_id=TRUE)

Select by column name

crosstable(mtcars2, c(mpg, "qsec"), by=vs) %>% 
  as_flextable(keep_id=TRUE)

qsec = c("mpg", "cyl") #wouldn't that be the most evil variable name ever?
crosstable(mtcars2, any_of(qsec), by=vs) %>% 
  as_flextable(keep_id=TRUE)

crosstable(mtcars2, c(-mpg, -cyl, -1), by=vs) %>% head(8) %>% #-c(mpg, cyl, 1) would also work
  as_flextable(keep_id=TRUE)

crosstable(mtcars2, 2:4, by=vs) %>% 
  as_flextable(keep_id=TRUE)

Select with tidyselect helpers

Along with everything(), tidyselect provides a large choice of helpers. You can browse ?tidyselect::select_helpers for a complete list.

Note that all have the useful ignore.case argument which is often very convenient.

The main ones are re-exported by crosstable: starts_with(), ends_with(), contains() and matches(). Here are some examples:

crosstable(mtcars2, starts_with("d")) %>% 
  as_flextable(keep_id=TRUE)

crosstable(mtcars2, c(ends_with("g"), contains("yl"))) %>% 
  as_flextable(keep_id=TRUE)

#to all regex haters: the following call selects all columns which name 
#starts with "d" or "g", followed by exactly 3 characters
crosstable(mtcars2, matches("^d|g.{3}$")) %>% 
  as_flextable(keep_id=TRUE)

Select with predicate functions

Sometimes, you want to select columns if they meet a set of specifications, for instance of type or of value. You can then use predicate functions: functions that return a single logical value. If the function is named, it is a good practice to wrap it in where().

For instance, you might want to keep only character variables:

crosstable(mtcars2, c(where(is.character), where(is.factor), -model)) %>% 
  as_flextable(keep_id=TRUE)

Using anonymous functions, you can even use more complicated patterns. For instance, you might want only numeric variables which mean is higher than 100:

crosstable(mtcars2, where(function(x) is.numeric(x) && mean(x)>100)) %>% 
  as_flextable(keep_id=TRUE)

Of note, crosstable support lambda-functions, so you could instead write crosstable(mtcars2, where(~is.numeric(.x) && mean(.x)>100)) for the exact same result but a tidier code.

The only logical constraint is that the function in where() should return a single logical value. Use &&, ||, and parenthesis to combine functions in complex patterns.

Select with a formula

If you want to mutate some variables in real-time, you can use the formula interface. The left-hand-side are the variables to describe, while the right-hand-side is the by variable (which can be set to NULL, 0 or 1 for “no variable”).

crosstable(mtcars2, mpg+cyl ~ vs) %>% 
  as_flextable(keep_id=TRUE)

This permits very complex and interesting patterns, using functions in situ and operations using with the I function. Labels are inherited and make little sense though.

crosstable(mtcars2, sqrt(mpg) + I(qsec^2) ~ ifelse(mpg>20,"mpg>20","mpg<20"),
           label=FALSE) %>% 
  as_flextable()

Note that you cannot use tidyselect helpers in formulas and that you cannot use formula declared in an object.

Ultimate example

Lets play a little with all of this :-)

I want all numeric variables that do not start by “d” or “w”, but I still want drat in the end.

crosstable(mtcars2, c(where(is.numeric), -matches("^d|w"), drat), label=FALSE) %>% 
  as_flextable()