Generate a descriptive table of all chosen columns, as contingency tables for categorical variables and as calculation summaries for numeric variables. If the by argument points to one or several categorical variables, crosstable will output a description of all columns for each level. Otherwise, if it points to a numeric variable, crosstable will calculate correlation coefficients with all other selected numeric columns. Finally, if it points to a Surv object, crosstable will describe the survival at different times.
Can be formatted as an HTML table using as_flextable().
Usage
crosstable(
data,
cols = everything(),
...,
by = NULL,
total = c("none", "row", "column", "both"),
percent_pattern = "{n} ({p_row})",
percent_digits = 2,
num_digits = 1,
showNA = c("ifany", "always", "no"),
label = TRUE,
funs = c(` ` = cross_summary),
funs_arg = list(),
cor_method = c("pearson", "kendall", "spearman"),
drop_levels = FALSE,
remove_zero_percent = NULL,
unique_numeric = 3,
date_format = NULL,
times = NULL,
followup = FALSE,
test = FALSE,
test_args = crosstable_test_args(),
effect = FALSE,
effect_args = crosstable_effect_args(),
margin = deprecated(),
.vars = deprecated()
)Arguments
- data
A data.frame
- cols
<
tidy-select> Columns to describe, default toeverything(). See examples orvignette("crosstable-selection")for more details.- ...
Unused. All parameters after this one must be named.
- by
The variable to group on. Character or name.
- total
one of ["none", "row", "column" or "both"] to indicate whether to add total rows and/or columns. Default to
none.- percent_pattern
Pattern used to describe proportions in categorical data. Syntax uses a
glue::glue()specification, see the section below for more details. Default to"{n} ({p_col})"ifbyis null and"{n} ({p_row})"if it is not.- percent_digits
Number of digits for percentages.
- num_digits
Number of digits for numeric summaries.
- showNA
Whether to show NA in categorical variables (one of
c("ifany", "always", "no"), like intable()).- label
Whether to show labels. See
import_labels()orset_label()for how to add labels to the dataset columns.- funs
Functions to apply to numeric variables. Default to
cross_summary().- funs_arg
Additional parameters for
funs, e.g.digits(the number of decimal places) for the defaultcross_summary(). Ultimately, these arguments are passed toformat_fixed().- cor_method
One of
c("pearson", "kendall", "spearman")to indicate which correlation coefficient is to be used.- drop_levels
Whether to drop unused levels of factor variables. Default to
TRUE.- remove_zero_percent
Whether to remove proportions when
n==0. Default toFALSE.- unique_numeric
The number of non-missing different levels a variable should have to be considered as numeric.
- date_format
if
xis a vector of Date or POSIXt, the format to apply (see strptime for formats)- times
When using formula with
survival::Surv()objects, which times to summarize.- followup
When using formula with
survival::Surv()objects, whether to display follow-up time.- test
Whether to perform tests.
- test_args
See
crosstable_test_argsto override default testing behaviour.- effect
Whether to compute a effect measure.
- effect_args
See
crosstable_effect_argsto override default behaviour.- margin
Deprecated in favor of
percent_pattern. One of ["row", "column", "cell", "none", or "all"]. Default torow.- .vars
Deprecated in favor of
cols.
About percent_pattern
The percent_pattern argument is very powerful but can be difficult to understand at first :
It is usually a single string that uses the glue syntax, where variables are put in curly braces (
{x}).Counts are expressed as
{n},{n_row},{n_col}, and{n_tot}, and proportions as{p_row},{p_col}, and{p_cell}, depending on the margin on which they are calculated.For each variable, a version including missing values in the total is proposed as
{n_xxx_na}or{p_xxx_na}.For each proportion, a confidence interval is also calculated using Wilson score and can be expressed as
{p_xxx_inf}and{p_xxx_sup}. See examples for practical applications.Alternatively,
percent_patterncan be a list of characters with namesbody,total_row,total_col, andtotal_allto also control the pattern in other parts of the crosstable than the body.
Examples
#whole table
crosstable(iris)
#> # A tibble: 19 × 4
#> .id label variable value
#> <chr> <chr> <chr> <chr>
#> 1 Sepal.Length Sepal.Length Min / Max 4.3 / 7.9
#> 2 Sepal.Length Sepal.Length Med [IQR] 5.8 [5.1;6.4]
#> 3 Sepal.Length Sepal.Length Mean (std) 5.8 (0.8)
#> 4 Sepal.Length Sepal.Length N (NA) 150 (0)
#> 5 Sepal.Width Sepal.Width Min / Max 2.0 / 4.4
#> 6 Sepal.Width Sepal.Width Med [IQR] 3.0 [2.8;3.3]
#> 7 Sepal.Width Sepal.Width Mean (std) 3.1 (0.4)
#> 8 Sepal.Width Sepal.Width N (NA) 150 (0)
#> 9 Petal.Length Petal.Length Min / Max 1.0 / 6.9
#> 10 Petal.Length Petal.Length Med [IQR] 4.3 [1.6;5.1]
#> 11 Petal.Length Petal.Length Mean (std) 3.8 (1.8)
#> 12 Petal.Length Petal.Length N (NA) 150 (0)
#> 13 Petal.Width Petal.Width Min / Max 0.1 / 2.5
#> 14 Petal.Width Petal.Width Med [IQR] 1.3 [0.3;1.8]
#> 15 Petal.Width Petal.Width Mean (std) 1.2 (0.8)
#> 16 Petal.Width Petal.Width N (NA) 150 (0)
#> 17 Species Species setosa 50 (33.33%)
#> 18 Species Species versicolor 50 (33.33%)
#> 19 Species Species virginica 50 (33.33%)
crosstable(mtcars)
#> # A tibble: 38 × 4
#> .id label variable value
#> <chr> <chr> <chr> <chr>
#> 1 mpg mpg Min / Max 10.4 / 33.9
#> 2 mpg mpg Med [IQR] 19.2 [15.4;22.8]
#> 3 mpg mpg Mean (std) 20.1 (6.0)
#> 4 mpg mpg N (NA) 32 (0)
#> 5 cyl cyl 4 11 (34.38%)
#> 6 cyl cyl 6 7 (21.88%)
#> 7 cyl cyl 8 14 (43.75%)
#> 8 disp disp Min / Max 71.1 / 472.0
#> 9 disp disp Med [IQR] 196.3 [120.8;326.0]
#> 10 disp disp Mean (std) 230.7 (123.9)
#> # ℹ 28 more rows
crosstable(mtcars2)
#> # A tibble: 78 × 4
#> .id label variable value
#> <chr> <chr> <chr> <chr>
#> 1 model Model AMC Javelin 1 (3.12%)
#> 2 model Model Cadillac Fleetwood 1 (3.12%)
#> 3 model Model Camaro Z28 1 (3.12%)
#> 4 model Model Chrysler Imperial 1 (3.12%)
#> 5 model Model Datsun 710 1 (3.12%)
#> 6 model Model Dodge Challenger 1 (3.12%)
#> 7 model Model Duster 360 1 (3.12%)
#> 8 model Model Ferrari Dino 1 (3.12%)
#> 9 model Model Fiat 128 1 (3.12%)
#> 10 model Model Fiat X1-9 1 (3.12%)
#> # ℹ 68 more rows
#tidyselection, custom functions
library(dplyr)
crosstable(mtcars2, c(ends_with("t"), starts_with("c")), by=vs,
funs=c(mean, quantile), funs_arg=list(probs=c(.25,.75)))
#> Warning: Direct call of 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or 'as.data.frame()' instead
#> Warning: Direct call of 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or 'as.data.frame()' instead
#> Warning: Direct call of 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or 'as.data.frame()' instead
#> Warning: Direct call of 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or 'as.data.frame()' instead
#> Warning: Direct call of 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or 'as.data.frame()' instead
#> Warning: Direct call of 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or 'as.data.frame()' instead
#> Warning: Direct call of 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or 'as.data.frame()' instead
#> Warning: Direct call of 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or 'as.data.frame()' instead
#> Warning: Direct call of 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or 'as.data.frame()' instead
#> # A tibble: 12 × 5
#> .id label variable straight vshaped
#> <chr> <chr> <chr> <chr> <chr>
#> 1 drat Rear axle ratio mean 3.9 3.4
#> 2 drat Rear axle ratio quantile 25% 3.7 3.1
#> 3 drat Rear axle ratio quantile 75% 4.1 3.7
#> 4 wt Weight (1000 lbs) mean 2.6 3.7
#> 5 wt Weight (1000 lbs) quantile 25% 2.0 3.2
#> 6 wt Weight (1000 lbs) quantile 75% 3.2 3.8
#> 7 cyl Number of cylinders 4 10 (90.91%) 1 (9.09%)
#> 8 cyl Number of cylinders 6 4 (57.14%) 3 (42.86%)
#> 9 cyl Number of cylinders 8 0 (0%) 14 (100.00%)
#> 10 carb Number of carburetors mean 1.8 3.6
#> 11 carb Number of carburetors quantile 25% 1.0 2.2
#> 12 carb Number of carburetors quantile 75% 2.0 4.0
#margin and totals, multiple by
crosstable(mtcars2, c(disp, cyl), by=c(am, vs),
margin=c("row", "col"), total = "both")
#> Warning: Direct call of 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or 'as.data.frame()' instead
#> # A tibble: 8 × 8
#> .id label variable am=auto & vs=straigh…¹ am=manual & vs=strai…²
#> <chr> <chr> <chr> <chr> <chr>
#> 1 disp Displacement (cu… Min / M… 120.1 / 258.0 71.1 / 121.0
#> 2 disp Displacement (cu… Med [IQ… 167.6 [143.8;196.3] 79.0 [77.2;101.5]
#> 3 disp Displacement (cu… Mean (s… 175.1 (49.1) 89.8 (18.8)
#> 4 disp Displacement (cu… N (NA) 7 (0) 7 (0)
#> 5 cyl Number of cylind… 4 3 (42.86% / 27.27%) 7 (100.00% / 63.64%)
#> 6 cyl Number of cylind… 6 4 (57.14% / 57.14%) 0 (0% / 0%)
#> 7 cyl Number of cylind… 8 0 (0% / 0%) 0 (0% / 0%)
#> 8 cyl Number of cylind… Total 7 (21.88%) 7 (21.88%)
#> # ℹ abbreviated names: ¹`am=auto & vs=straight`, ²`am=manual & vs=straight`
#> # ℹ 3 more variables: `am=auto & vs=vshaped` <chr>,
#> # `am=manual & vs=vshaped` <chr>, Total <chr>
#predicate selection, correlation, effect calculation
crosstable(mtcars2, where(is.numeric), by=hp, effect=TRUE)
#> # A tibble: 6 × 4
#> .id label variable `Gross horsepower`
#> <chr> <chr> <chr> <chr>
#> 1 mpg Miles/(US) gallon pearson "-0.78 \n95%CI [-0.89;-0.59]"
#> 2 disp Displacement (cu.in.) pearson "0.79 \n95%CI [0.61;0.89]"
#> 3 drat Rear axle ratio pearson "-0.45 \n95%CI [-0.69;-0.12]"
#> 4 wt Weight (1000 lbs) pearson "0.66 \n95%CI [0.4;0.82]"
#> 5 qsec 1/4 mile time pearson "-0.71 \n95%CI [-0.85;-0.48]"
#> 6 carb Number of carburetors pearson "0.75 \n95%CI [0.54;0.87]"
#lambda selection & statistical tests
crosstable(mtcars2, ~is.numeric(.x) && mean(.x)>50, by=vs, test=TRUE)
#> Warning: Direct call of 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or 'as.data.frame()' instead
#> Warning: Direct call of 'as.data.frame.numeric()' is deprecated. Use 'as.data.frame.vector()' or 'as.data.frame()' instead
#> # A tibble: 8 × 6
#> .id label variable straight vshaped test
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 disp Displacement (cu.in.) Min / Max 71.1 / 258.0 120.3 / 472.0 "p v…
#> 2 disp Displacement (cu.in.) Med [IQR] 120.5 [83.0;162.4] 311.0 [275.8;… "p v…
#> 3 disp Displacement (cu.in.) Mean (std) 132.5 (56.9) 307.1 (106.8) "p v…
#> 4 disp Displacement (cu.in.) N (NA) 14 (0) 18 (0) "p v…
#> 5 hp Gross horsepower Min / Max 52.0 / 123.0 91.0 / 335.0 "p v…
#> 6 hp Gross horsepower Med [IQR] 96.0 [66.0;109.8] 180.0 [156.2;… "p v…
#> 7 hp Gross horsepower Mean (std) 91.4 (24.4) 189.7 (60.3) "p v…
#> 8 hp Gross horsepower N (NA) 14 (0) 18 (0) "p v…
#Dates
mtcars2$my_date = as.Date(mtcars2$hp , origin="2010-01-01") %>% set_label("Some nonsense date")
crosstable(mtcars2, my_date, by=vs, date_format="%d/%m/%Y")
#> # A tibble: 4 × 5
#> .id label variable straight vshaped
#> <chr> <chr> <chr> <chr> <chr>
#> 1 my_date Some nonsense date Min / Max 22/02/2010 - 04/05/2010 02/04/…
#> 2 my_date Some nonsense date Med [IQR] 07/04/2010 [08/03/2010;21/04/20… 30/06/…
#> 3 my_date Some nonsense date Mean (std) 02/04/2010 (24.4 days) 09/07/…
#> 4 my_date Some nonsense date N (NA) 14 (0) 18 (0)
#Survival data (using formula syntax)
library(survival)
crosstable(aml, Surv(time, status) ~ x, times=c(0,15,30,150), followup=TRUE)
#> # A tibble: 6 × 5
#> .id label variable Maintained Nonmaintained
#> <chr> <chr> <chr> <chr> <chr>
#> 1 Surv(time, status) Surv(time, status) t=0 1.00 (0/1… 1.00 (0/12)
#> 2 Surv(time, status) Surv(time, status) t=15 0.82 (2/8) 0.58 (5/7)
#> 3 Surv(time, status) Surv(time, status) t=30 0.61 (2/5) 0.29 (3/4)
#> 4 Surv(time, status) Surv(time, status) t=150 0.18 (3/1) 0 (3/0)
#> 5 Surv(time, status) Surv(time, status) Median follow … 103 [13 ;… NA [16 ; 45]
#> 6 Surv(time, status) Surv(time, status) Median survival 31 23
#Patterns
crosstable(mtcars2, vs, by=am, percent_digits=0,
percent_pattern="{n} ({p_col} / {p_row})")
#> # A tibble: 2 × 5
#> .id label variable auto manual
#> <chr> <chr> <chr> <chr> <chr>
#> 1 vs Engine straight 7 (37% / 50%) 7 (54% / 50%)
#> 2 vs Engine vshaped 12 (63% / 67%) 6 (46% / 33%)
crosstable(mtcars2, vs, by=am, percent_digits=0,
percent_pattern="N={n} \np[95%CI] = {p_col} [{p_col_inf}; {p_col_sup}]")
#> # A tibble: 2 × 5
#> .id label variable auto manual
#> <chr> <chr> <chr> <chr> <chr>
#> 1 vs Engine straight "N=7 \np[95%CI] = 37% [19%; 59%]" "N=7 \np[95%CI] = 54…
#> 2 vs Engine vshaped "N=12 \np[95%CI] = 63% [41%; 81%]" "N=6 \np[95%CI] = 46…
str_high="n>5"; str_lo="n<=5"
crosstable(mtcars2, vs, by=am, percent_digits=0,
percent_pattern="col={p_col}, row={p_row} ({ifelse(n<5, str_lo, str_high)})")
#> Error in map(.x, .f, ..., .progress = .progress): ℹ In index: 1.
#> ℹ With name: body.
#> Caused by error in `crosstable()`:
#> ! Could not resolve a variable used in `percent_pattern`.
#> ℹ Authorized variables are `n`, `n_tot`, `n_row`, `n_col`, `p_tot`, `p_row`,
#> and `p_col`, along with `p_xxx_inf` and `p_xxx_sup` for proportions.
#> ℹ Provided `percent_pattern`: `col={p_col}, row={p_row} ({ifelse(n<5, str_lo,
#> str_high)})`
#> ✖ Error: "Failed to evaluate glue component {ifelse(n<5, str_lo, str_high)}"