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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"),
  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 to everything(). See examples or vignette("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})" if by is 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 in table()).

label

Whether to show labels. See import_labels() or set_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 default cross_summary(). Ultimately, these arguments are passed to format_fixed().

cor_method

One of c("pearson", "kendall", "spearman") to indicate which correlation coefficient is to be used.

unique_numeric

The number of non-missing different levels a variable should have to be considered as numeric.

date_format

if x is 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_args to override default testing behaviour.

effect

Whether to compute a effect measure.

effect_args

See crosstable_effect_args to override default behaviour.

margin

Deprecated in favor of percent_pattern. One of ["row", "column", "cell", "none", or "all"]. Default to row.

.vars

Deprecated in favor of cols.

Value

A data.frame/tibble of class crosstable

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_pattern can be a list of characters with names body, total_row, total_col, and total_all to also control the pattern in other parts of the crosstable than the body.

See also

https://danchaltiel.github.io/crosstable/, as_flextable, import_labels

Author

Dan Chaltiel

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)      
#> # … with 28 more rows
#> # ℹ Use `print(n = ...)` to see 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%)
#> # … with 68 more rows
#> # ℹ Use `print(n = ...)` to see 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)))
#> # 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")
#> # A tibble: 8 × 8
#>   .id   label                 variable   am=auto…¹ am=ma…² am=au…³ am=ma…⁴ Total
#>   <chr> <chr>                 <chr>      <chr>     <chr>   <chr>   <chr>   <chr>
#> 1 disp  Displacement (cu.in.) Min / Max  120.1 / … 71.1 /… 275.8 … 120.3 … 71.1…
#> 2 disp  Displacement (cu.in.) Med [IQR]  167.6 [1… 79.0 [… 355.0 … 160.0 … 196.…
#> 3 disp  Displacement (cu.in.) Mean (std) 175.1 (4… 89.8 (… 357.6 … 206.2 … 230.…
#> 4 disp  Displacement (cu.in.) N (NA)     7 (0)     7 (0)   12 (0)  6 (0)   32 (…
#> 5 cyl   Number of cylinders   4          3 (42.86… 7 (100… 0 (0% … 1 (16.… 11 (…
#> 6 cyl   Number of cylinders   6          4 (57.14… 0 (0% … 0 (0% … 3 (50.… 7 (2…
#> 7 cyl   Number of cylinders   8          0 (0% / … 0 (0% … 12 (10… 2 (33.… 14 (…
#> 8 cyl   Number of cylinders   Total      7 (21.88… 7 (21.… 12 (37… 6 (18.… 32 (…
#> # … with abbreviated variable names ¹​`am=auto & vs=straight`,
#> #   ²​`am=manual & vs=straight`, ³​`am=auto & vs=vshaped`,
#> #   ⁴​`am=manual & vs=vshaped`

#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)
#> # 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                 Maint…¹ Nonma…²
#>   <chr>              <chr>              <chr>                    <chr>   <chr>  
#> 1 Surv(time, status) Surv(time, status) t=0                      1.00 (… 1.00 (…
#> 2 Surv(time, status) Surv(time, status) t=15                     0.82 (… 0.58 (…
#> 3 Surv(time, status) Surv(time, status) t=30                     0.61 (… 0.29 (…
#> 4 Surv(time, status) Surv(time, status) t=150                    0.18 (… 0 (3/0)
#> 5 Surv(time, status) Surv(time, status) Median follow up [min ;… 103 [1… NA [16…
#> 6 Surv(time, status) Surv(time, status) Median survival          31      23     
#> # … with abbreviated variable names ¹​Maintained, ²​Nonmaintained

#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 crosstable(mtcars2, vs, by = am, percent_digits = 0, percent_pattern = "col={p_col}, row={p_row} ({ifelse(n<5, str_lo, str_high)})"): Could not resolve a variable used in `percent_pattern`.
#>  Authorized variables are `n`, `n_tot`, `n_row`, `n_col`, `p_cell`, `p_row`,
#>   and `p_col`, along with `p_xxx_inf` and `p_xxx_sup` for proportions.
#>  Provided `percent_pattern`: `col={p_col}, row={p_row} ({ifelse(n<5, str_lo,
#>   str_high)})`
#>  Error: "object 'str_lo' not found"