Select variables

This vignette will review the different ways of selecting variables to describe with crosstable. For more general informations about crosstable, see vignette("crosstable") (link). For more information and tips on tidyselect, see tidyselect syntax

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)

.id	label	variable	value
Sepal.Length	Length of Sepal	Min / Max	4.3 / 7.9
		Med [IQR]	5.8 [5.1;6.4]
		Mean (std)	5.8 (0.8)
		N (NA)	150 (0)
Sepal.Width	Width of Sepal	Min / Max	2.0 / 4.4
		Med [IQR]	3.0 [2.8;3.3]
		Mean (std)	3.1 (0.4)
		N (NA)	150 (0)
Petal.Length	Length of Petal	Min / Max	1.0 / 6.9
		Med [IQR]	4.3 [1.6;5.1]
		Mean (std)	3.8 (1.8)
		N (NA)	150 (0)
Petal.Width	Width of Petal	Min / Max	0.1 / 2.5
		Med [IQR]	1.3 [0.3;1.8]
		Mean (std)	1.2 (0.8)
		N (NA)	150 (0)
Species	Specie	setosa	50 (33.33%)
		versicolor	50 (33.33%)
		virginica	50 (33.33%)

Select by column name

Name

Just like with dplyr::select(), you can use names with or without quotes to select variables you want to describe. Use c() to select several columns:

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

.id	label	variable	Engine
.id	label	variable	straight	vshaped
mpg	Miles/(US) gallon	Min / Max	17.8 / 33.9	10.4 / 26.0
		Med [IQR]	22.8 [21.4;29.6]	15.7 [14.8;19.1]
		Mean (std)	24.6 (5.4)	16.6 (3.9)
		N (NA)	14 (0)	18 (0)
qsec	1/4 mile time	Min / Max	16.9 / 22.9	14.5 / 18.0
		Med [IQR]	19.2 [18.6;20.0]	17.0 [16.0;17.4]
		Mean (std)	19.3 (1.4)	16.7 (1.1)
		N (NA)	14 (0)	18 (0)

External vector

However, it is better to use all_of or any_of when you take your column names from an external vector. Otherwise, there would be an ambiguity as you might have wanted to select a column named like this vector.

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)

.id	label	variable	Engine
.id	label	variable	straight	vshaped
mpg	Miles/(US) gallon	Min / Max	17.8 / 33.9	10.4 / 26.0
		Med [IQR]	22.8 [21.4;29.6]	15.7 [14.8;19.1]
		Mean (std)	24.6 (5.4)	16.6 (3.9)
		N (NA)	14 (0)	18 (0)
cyl	Number of cylinders	4	10 (90.91%)	1 (9.09%)
		6	4 (57.14%)	3 (42.86%)
		8	0 (0%)	14 (100.00%)

Negation

You can use negation to keep all but some columns:

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

.id	label	variable	Engine
.id	label	variable	straight	vshaped
disp	Displacement (cu.in.)	Min / Max	71.1 / 258.0	120.3 / 472.0
		Med [IQR]	120.5 [83.0;162.4]	311.0 [275.8;360.0]
		Mean (std)	132.5 (56.9)	307.1 (106.8)
		N (NA)	14 (0)	18 (0)
hp	Gross horsepower	Min / Max	52.0 / 123.0	91.0 / 335.0
		Med [IQR]	96.0 [66.0;109.8]	180.0 [156.2;226.2]
		Mean (std)	91.4 (24.4)	189.7 (60.3)
		N (NA)	14 (0)	18 (0)

Indice

This can be useful sometimes, for instance when you want to quickly describe the 3 first columns.

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

.id	label	variable	Engine
.id	label	variable	straight	vshaped
mpg	Miles/(US) gallon	Min / Max	17.8 / 33.9	10.4 / 26.0
		Med [IQR]	22.8 [21.4;29.6]	15.7 [14.8;19.1]
		Mean (std)	24.6 (5.4)	16.6 (3.9)
		N (NA)	14 (0)	18 (0)
cyl	Number of cylinders	4	10 (90.91%)	1 (9.09%)
		6	4 (57.14%)	3 (42.86%)
		8	0 (0%)	14 (100.00%)
disp	Displacement (cu.in.)	Min / Max	71.1 / 258.0	120.3 / 472.0
		Med [IQR]	120.5 [83.0;162.4]	311.0 [275.8;360.0]
		Mean (std)	132.5 (56.9)	307.1 (106.8)
		N (NA)	14 (0)	18 (0)

You can also use negation (-(1:3)), concatenation (c(1,2,3)), or both (crosstable(mtcars2, 1:4, -2, by=vs)).

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)

.id	label	variable	value
disp	Displacement (cu.in.)	Min / Max	71.1 / 472.0
		Med [IQR]	196.3 [120.8;326.0]
		Mean (std)	230.7 (123.9)
		N (NA)	32 (0)
drat	Rear axle ratio	Min / Max	2.8 / 4.9
		Med [IQR]	3.7 [3.1;3.9]
		Mean (std)	3.6 (0.5)
		N (NA)	32 (0)

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

.id	label	variable	value
mpg	Miles/(US) gallon	Min / Max	10.4 / 33.9
		Med [IQR]	19.2 [15.4;22.8]
		Mean (std)	20.1 (6.0)
		N (NA)	32 (0)
cyl	Number of cylinders	4	11 (34.38%)
		6	7 (21.88%)
		8	14 (43.75%)

#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)

.id	label	variable	value
disp	Displacement (cu.in.)	Min / Max	71.1 / 472.0
		Med [IQR]	196.3 [120.8;326.0]
		Mean (std)	230.7 (123.9)
		N (NA)	32 (0)
drat	Rear axle ratio	Min / Max	2.8 / 4.9
		Med [IQR]	3.7 [3.1;3.9]
		Mean (std)	3.6 (0.5)
		N (NA)	32 (0)
gear	Number of forward gears	3	15 (46.88%)
		4	12 (37.50%)
		5	5 (15.62%)

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)

.id	label	variable	value
vs	Engine	straight	14 (43.75%)
vs	Engine	vshaped	18 (56.25%)
am	Transmission	auto	19 (59.38%)
am	Transmission	manual	13 (40.62%)
cyl	Number of cylinders	4	11 (34.38%)
		6	7 (21.88%)
		8	14 (43.75%)
gear	Number of forward gears	3	15 (46.88%)
		4	12 (37.50%)
		5	5 (15.62%)

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)

.id	label	variable	value
disp	Displacement (cu.in.)	Min / Max	71.1 / 472.0
		Med [IQR]	196.3 [120.8;326.0]
		Mean (std)	230.7 (123.9)
		N (NA)	32 (0)
hp	Gross horsepower	Min / Max	52.0 / 335.0
		Med [IQR]	123.0 [96.5;180.0]
		Mean (std)	146.7 (68.6)
		N (NA)	32 (0)

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)

.id	label	variable	Engine
.id	label	variable	straight	vshaped
mpg	Miles/(US) gallon	Min / Max	17.8 / 33.9	10.4 / 26.0
		Med [IQR]	22.8 [21.4;29.6]	15.7 [14.8;19.1]
		Mean (std)	24.6 (5.4)	16.6 (3.9)
		N (NA)	14 (0)	18 (0)
cyl	Number of cylinders	4	10 (90.91%)	1 (9.09%)
		6	4 (57.14%)	3 (42.86%)
		8	0 (0%)	14 (100.00%)

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()

label	variable	ifelse(mpg > 20, "mpg>20", "mpg<20")
label	variable	mpg<20	mpg>20
sqrt(mpg)	Min / Max	3.2 / 4.4	4.6 / 5.8
	Med [IQR]	4.0 [3.8;4.2]	4.9 [4.6;5.4]
	Mean (std)	4.0 (0.4)	5.0 (0.4)
	N (NA)	18 (0)	14 (0)
I(qsec^2)	Min / Max	210.2 / 408.8	270.9 / 524.4
	Med [IQR]	301.0 [259.3;321.8]	351.8 [303.0;391.8]
	Mean (std)	294.3 (50.4)	356.8 (66.7)
	N (NA)	18 (0)	14 (0)

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()

label	variable	value
mpg	Min / Max	10.4 / 33.9
	Med [IQR]	19.2 [15.4;22.8]
	Mean (std)	20.1 (6.0)
	N (NA)	32 (0)
hp	Min / Max	52.0 / 335.0
	Med [IQR]	123.0 [96.5;180.0]
	Mean (std)	146.7 (68.6)
	N (NA)	32 (0)
qsec	Min / Max	14.5 / 22.9
	Med [IQR]	17.7 [16.9;18.9]
	Mean (std)	17.8 (1.8)
	N (NA)	32 (0)
carb	Min / Max	1.0 / 8.0
	Med [IQR]	2.0 [2.0;4.0]
	Mean (std)	2.8 (1.6)
	N (NA)	32 (0)
drat	Min / Max	2.8 / 4.9
	Med [IQR]	3.7 [3.1;3.9]
	Mean (std)	3.6 (0.5)
	N (NA)	32 (0)

Dan Chaltiel

2024-11-01

Whole table

Select by column name

Name

External vector

Negation

Indice

Select with `tidyselect` helpers

Select with predicate functions

Select with a formula

Ultimate example

Select variables

Dan Chaltiel

2024-11-01

Whole table

Select by column name

Name

External vector

Negation

Indice

Select with tidyselect helpers

Select with predicate functions

Select with a formula

Ultimate example

Select with `tidyselect` helpers