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Generate cross tabulations with optional percentages and totals

Source: R/dta_crosstab.R
dta_crosstab.Rd

dta_crosstab() creates a cross-tabulation (contingency table) for a given dataset, with options to include counts, row percentages, or column percentages. Totals can also be added to rows, columns, or both. The output is styled using the janitor package.

Usage

dta_crosstab(
  dat,
  .row,
  .column,
  cells = c("counts", "row", "col"),
  add_totals = c("both", "row", "col"),
  name = "Variable",
  add_percent_symbol = TRUE,
  digits = 2
)

Arguments

dat

A data frame (not a tibble) containing the variables to tabulate.

.row

The variable to be used as rows in the crosstab.

.column

The variable to be used as columns in the crosstab.

cells

A character string indicating the type of values to display in the crosstab:

"counts"

Display counts (default).

"row"

Display row percentages.

"col"

Display column percentages.

add_totals

A character string specifying where to add totals:

"both"

Add totals to both rows and columns (default).

"row"

Add totals to rows only.

"col"

Add totals to columns only.

name

A character string to rename the first column in the output. Default is "Variable".

add_percent_symbol

Logical indicating whether or not to add a % sign to percentages. Default is TRUE.

digits

An integer specifying the number of decimal places to use for percentages. Default is 1.

Value

A data frame representing the cross-tabulation, styled using janitor functions. The format of the output depends on the cells argument:

Counts

Displays raw counts.

Percentages

Displays percentages with counts in parentheses if cells is "row" or "col".

Examples

data("data_sample")
df <- data_sample

# Crosstabulation of frequencies (counts)

result <- dta_crosstab(
  dat = df, .row = region, .column = age_group
)
dta_gtable(result)



  
Variable
      20-29
      30-39
      40-49
      50-59
      60-69
      70+
      Total
    
Central
33
68
100
80
45
30
356
North East
65
133
199
174
127
66
764
South
94
151
177
206
154
69
851
West
50
93
117
128
96
45
529
Total
242
445
593
588
422
210
2500

# Calculate column percentages

result2 <- dta_crosstab(
  dat = df, 
  .row = region,
  .column = age_group,
  cells = "col",
  add_totals = "col"
)
dta_gtable(result2)



  
region/age_group
      20-29
      30-39
      40-49
      50-59
      60-69
      70+
      Total
    
Central
33 (13.64%)
68 (15.28%)
100 (16.86%)
80 (13.61%)
45 (10.66%)
30 (14.29%)
356 (14.24%)
North East
65 (26.86%)
133 (29.89%)
199 (33.56%)
174 (29.59%)
127 (30.09%)
66 (31.43%)
764 (30.56%)
South
94 (38.84%)
151 (33.93%)
177 (29.85%)
206 (35.03%)
154 (36.49%)
69 (32.86%)
851 (34.04%)
West
50 (20.66%)
93 (20.90%)
117 (19.73%)
128 (21.77%)
96 (22.75%)
45 (21.43%)
529 (21.16%)

# Calculate row percentages

result3 <- dta_crosstab(
  dat = df,
  .row = region,
  .column = age_group,
  cells = "row",
  add_totals = "row"
)
dta_gtable(result3)



  
region/age_group
      20-29
      30-39
      40-49
      50-59
      60-69
      70+
    
Central
33  (9.27%)
68 (19.10%)
100 (28.09%)
80 (22.47%)
45 (12.64%)
30 (8.43%)
North East
65  (8.51%)
133 (17.41%)
199 (26.05%)
174 (22.77%)
127 (16.62%)
66 (8.64%)
South
94 (11.05%)
151 (17.74%)
177 (20.80%)
206 (24.21%)
154 (18.10%)
69 (8.11%)
West
50  (9.45%)
93 (17.58%)
117 (22.12%)
128 (24.20%)
96 (18.15%)
45 (8.51%)
Total
242  (9.68%)
445 (17.80%)
593 (23.72%)
588 (23.52%)
422 (16.88%)
210 (8.40%)


# Remove the percentages symbol

result4 <- dta_crosstab(
  dat = df,
  .row = region,
  .column = age_group,
  cells = "row",
  add_totals = "row",
  add_percent_symbol = FALSE
)
dta_gtable(result4)



  
region/age_group
      20-29
      30-39
      40-49
      50-59
      60-69
      70+
    
Central
33  (9.27)
68 (19.10)
100 (28.09)
80 (22.47)
45 (12.64)
30 (8.43)
North East
65  (8.51)
133 (17.41)
199 (26.05)
174 (22.77)
127 (16.62)
66 (8.64)
South
94 (11.05)
151 (17.74)
177 (20.80)
206 (24.21)
154 (18.10)
69 (8.11)
West
50  (9.45)
93 (17.58)
117 (22.12)
128 (24.20)
96 (18.15)
45 (8.51)
Total
242  (9.68)
445 (17.80)
593 (23.72)
588 (23.52)
422 (16.88)
210 (8.40)