This function creates frequency or crosstables of variables, including the number of levels/values as well as the distribution of raw, valid and cumulative percentages. For crosstables, row, column and cell percentages can be calculated.
Usage
data_tabulate(x, ...)
# S3 method for default
data_tabulate(
x,
by = NULL,
drop_levels = FALSE,
weights = NULL,
include_na = TRUE,
proportions = NULL,
name = NULL,
verbose = TRUE,
...
)
# S3 method for data.frame
data_tabulate(
x,
select = NULL,
exclude = NULL,
ignore_case = FALSE,
regex = FALSE,
by = NULL,
drop_levels = FALSE,
weights = NULL,
include_na = TRUE,
proportions = NULL,
collapse = FALSE,
verbose = TRUE,
...
)Arguments
- x
A (grouped) data frame, a vector or factor.
- ...
not used.
- by
Optional vector or factor. If supplied, a crosstable is created. If
xis a data frame,bycan also be a character string indicating the name of a variable inx.- drop_levels
Logical, if
FALSE, factor levels that do not occur in the data are included in the table (with frequency of zero), else unused factor levels are dropped from the frequency table.- weights
Optional numeric vector of weights. Must be of the same length as
x. Ifweightsis supplied, weighted frequencies are calculated.- include_na
Logical, if
TRUE, missing values are included in the frequency or crosstable, else missing values are omitted.- proportions
Optional character string, indicating the type of percentages to be calculated. Only applies to crosstables, i.e. when
byis notNULL. Can be"row"(row percentages),"column"(column percentages) or"full"(to calculate relative frequencies for the full table).- name
Optional character string, which includes the name that is used for printing.
- verbose
Toggle warnings.
- select
Variables that will be included when performing the required tasks. Can be either
a variable specified as a literal variable name (e.g.,
column_name),a string with the variable name (e.g.,
"column_name"), or a character vector of variable names (e.g.,c("col1", "col2", "col3")),a formula with variable names (e.g.,
~column_1 + column_2),a vector of positive integers, giving the positions counting from the left (e.g.
1orc(1, 3, 5)),a vector of negative integers, giving the positions counting from the right (e.g.,
-1or-1:-3),one of the following select-helpers:
starts_with(),ends_with(),contains(), a range using:orregex("").starts_with(),ends_with(), andcontains()accept several patterns, e.gstarts_with("Sep", "Petal").or a function testing for logical conditions, e.g.
is.numeric()(oris.numeric), or any user-defined function that selects the variables for which the function returnsTRUE(like:foo <- function(x) mean(x) > 3),ranges specified via literal variable names, select-helpers (except
regex()) and (user-defined) functions can be negated, i.e. return non-matching elements, when prefixed with a-, e.g.-ends_with(""),-is.numericor-(Sepal.Width:Petal.Length). Note: Negation means that matches are excluded, and thus, theexcludeargument can be used alternatively. For instance,select=-ends_with("Length")(with-) is equivalent toexclude=ends_with("Length")(no-). In case negation should not work as expected, use theexcludeargument instead.
If
NULL, selects all columns. Patterns that found no matches are silently ignored, e.g.find_columns(iris, select = c("Species", "Test"))will just return"Species".- exclude
See
select, however, column names matched by the pattern fromexcludewill be excluded instead of selected. IfNULL(the default), excludes no columns.- ignore_case
Logical, if
TRUEand when one of the select-helpers or a regular expression is used inselect, ignores lower/upper case in the search pattern when matching against variable names.- regex
Logical, if
TRUE, the search pattern fromselectwill be treated as regular expression. Whenregex = TRUE, select must be a character string (or a variable containing a character string) and is not allowed to be one of the supported select-helpers or a character vector of length > 1.regex = TRUEis comparable to using one of the two select-helpers,select = contains("")orselect = regex(""), however, since the select-helpers may not work when called from inside other functions (see 'Details'), this argument may be used as workaround.- collapse
Logical, if
TRUEcollapses multiple tables into one larger table for printing. This affects only printing, not the returned object.
Note
There are print_html() and print_md() methods available for printing
frequency or crosstables in HTML and markdown format, e.g.
print_html(data_tabulate(x)).
Crosstables
If by is supplied, a crosstable is created. The crosstable includes <NA>
(missing) values by default. The first column indicates values of x, the
first row indicates values of by (including missing values). The last row
and column contain the total frequencies for each row and column, respectively.
Setting include_na = FALSE will omit missing values from the crosstable.
Setting proportions to "row" or "column" will add row or column
percentages. Setting proportions to "full" will add relative frequencies
for the full table.
Examples
# frequency tables -------
# ------------------------
data(efc)
# vector/factor
data_tabulate(efc$c172code)
#> carer's level of education (efc$c172code) <numeric>
#> # total N=100 valid N=90
#>
#> Value | N | Raw % | Valid % | Cumulative %
#> ------+----+-------+---------+-------------
#> 1 | 8 | 8.00 | 8.89 | 8.89
#> 2 | 66 | 66.00 | 73.33 | 82.22
#> 3 | 16 | 16.00 | 17.78 | 100.00
#> <NA> | 10 | 10.00 | <NA> | <NA>
# drop missing values
data_tabulate(efc$c172code, include_na = FALSE)
#> carer's level of education (efc$c172code) <numeric>
#> # total N=90 valid N=90
#>
#> Value | N | Raw % | Valid % | Cumulative %
#> ------+----+-------+---------+-------------
#> 1 | 8 | 8.89 | 8.89 | 8.89
#> 2 | 66 | 73.33 | 73.33 | 82.22
#> 3 | 16 | 17.78 | 17.78 | 100.00
# data frame
data_tabulate(efc, c("e42dep", "c172code"))
#> elder's dependency (e42dep) <categorical>
#> # total N=100 valid N=97
#>
#> Value | N | Raw % | Valid % | Cumulative %
#> ------+----+-------+---------+-------------
#> 1 | 2 | 2.00 | 2.06 | 2.06
#> 2 | 4 | 4.00 | 4.12 | 6.19
#> 3 | 28 | 28.00 | 28.87 | 35.05
#> 4 | 63 | 63.00 | 64.95 | 100.00
#> <NA> | 3 | 3.00 | <NA> | <NA>
#>
#> carer's level of education (c172code) <numeric>
#> # total N=100 valid N=90
#>
#> Value | N | Raw % | Valid % | Cumulative %
#> ------+----+-------+---------+-------------
#> 1 | 8 | 8.00 | 8.89 | 8.89
#> 2 | 66 | 66.00 | 73.33 | 82.22
#> 3 | 16 | 16.00 | 17.78 | 100.00
#> <NA> | 10 | 10.00 | <NA> | <NA>
# grouped data frame
suppressPackageStartupMessages(library(poorman, quietly = TRUE))
efc %>%
group_by(c172code) %>%
data_tabulate("e16sex")
#> elder's gender (e16sex) <numeric>
#> Grouped by c172code (1)
#> # total N=8 valid N=8
#>
#> Value | N | Raw % | Valid % | Cumulative %
#> ------+---+-------+---------+-------------
#> 1 | 5 | 62.50 | 62.50 | 62.50
#> 2 | 3 | 37.50 | 37.50 | 100.00
#> <NA> | 0 | 0.00 | <NA> | <NA>
#>
#> elder's gender (e16sex) <numeric>
#> Grouped by c172code (2)
#> # total N=66 valid N=66
#>
#> Value | N | Raw % | Valid % | Cumulative %
#> ------+----+-------+---------+-------------
#> 1 | 32 | 48.48 | 48.48 | 48.48
#> 2 | 34 | 51.52 | 51.52 | 100.00
#> <NA> | 0 | 0.00 | <NA> | <NA>
#>
#> elder's gender (e16sex) <numeric>
#> Grouped by c172code (3)
#> # total N=16 valid N=16
#>
#> Value | N | Raw % | Valid % | Cumulative %
#> ------+----+-------+---------+-------------
#> 1 | 4 | 25.00 | 25.00 | 25.00
#> 2 | 12 | 75.00 | 75.00 | 100.00
#> <NA> | 0 | 0.00 | <NA> | <NA>
#>
#> elder's gender (e16sex) <numeric>
#> Grouped by c172code (NA)
#> # total N=10 valid N=10
#>
#> Value | N | Raw % | Valid % | Cumulative %
#> ------+---+-------+---------+-------------
#> 1 | 5 | 50.00 | 50.00 | 50.00
#> 2 | 5 | 50.00 | 50.00 | 100.00
#> <NA> | 0 | 0.00 | <NA> | <NA>
# collapse tables
efc %>%
group_by(c172code) %>%
data_tabulate("e16sex", collapse = TRUE)
#> # Frequency Table
#>
#> Variable | Group | Value | N | Raw % | Valid % | Cumulative %
#> ---------+---------------+-------+----+-------+---------+-------------
#> e16sex | c172code (1) | 1 | 5 | 62.50 | 62.50 | 62.50
#> | | 2 | 3 | 37.50 | 37.50 | 100.00
#> | | <NA> | 0 | 0.00 | <NA> | <NA>
#> ---------+---------------+-------+----+-------+---------+-------------
#> e16sex | c172code (2) | 1 | 32 | 48.48 | 48.48 | 48.48
#> | | 2 | 34 | 51.52 | 51.52 | 100.00
#> | | <NA> | 0 | 0.00 | <NA> | <NA>
#> ---------+---------------+-------+----+-------+---------+-------------
#> e16sex | c172code (3) | 1 | 4 | 25.00 | 25.00 | 25.00
#> | | 2 | 12 | 75.00 | 75.00 | 100.00
#> | | <NA> | 0 | 0.00 | <NA> | <NA>
#> ---------+---------------+-------+----+-------+---------+-------------
#> e16sex | c172code (NA) | 1 | 5 | 50.00 | 50.00 | 50.00
#> | | 2 | 5 | 50.00 | 50.00 | 100.00
#> | | <NA> | 0 | 0.00 | <NA> | <NA>
#> ----------------------------------------------------------------------
# for larger N's (> 100000), a big mark is automatically added
set.seed(123)
x <- sample(1:3, 1e6, TRUE)
data_tabulate(x, name = "Large Number")
#> Large Number (x) <integer>
#> # total N=1,000,000 valid N=1,000,000
#>
#> Value | N | Raw % | Valid % | Cumulative %
#> ------+---------+-------+---------+-------------
#> 1 | 333,852 | 33.39 | 33.39 | 33.39
#> 2 | 332,910 | 33.29 | 33.29 | 66.68
#> 3 | 333,238 | 33.32 | 33.32 | 100.00
#> <NA> | 0 | 0.00 | <NA> | <NA>
# to remove the big mark, use "print(..., big_mark = "")"
print(data_tabulate(x), big_mark = "")
#> x <integer>
#> # total N=1000000 valid N=1000000
#>
#> Value | N | Raw % | Valid % | Cumulative %
#> ------+--------+-------+---------+-------------
#> 1 | 333852 | 33.39 | 33.39 | 33.39
#> 2 | 332910 | 33.29 | 33.29 | 66.68
#> 3 | 333238 | 33.32 | 33.32 | 100.00
#> <NA> | 0 | 0.00 | <NA> | <NA>
# weighted frequencies
set.seed(123)
efc$weights <- abs(rnorm(n = nrow(efc), mean = 1, sd = 0.5))
data_tabulate(efc$e42dep, weights = efc$weights)
#> elder's dependency (efc$e42dep) <categorical>
#> # total N=105 valid N=100 (weighted)
#>
#> Value | N | Raw % | Valid % | Cumulative %
#> ------+----+-------+---------+-------------
#> 1 | 3 | 2.86 | 3.00 | 3.00
#> 2 | 4 | 3.81 | 4.00 | 7.00
#> 3 | 26 | 24.76 | 26.00 | 33.00
#> 4 | 67 | 63.81 | 67.00 | 100.00
#> <NA> | 5 | 4.76 | <NA> | <NA>
# crosstables ------
# ------------------
# add some missing values
set.seed(123)
efc$e16sex[sample.int(nrow(efc), 5)] <- NA
data_tabulate(efc, "c172code", by = "e16sex")
#> c172code | male | female | <NA> | Total
#> ---------+------+--------+------+------
#> 1 | 5 | 2 | 1 | 8
#> 2 | 30 | 34 | 2 | 66
#> 3 | 4 | 10 | 2 | 16
#> <NA> | 5 | 5 | 0 | 10
#> ---------+------+--------+------+------
#> Total | 44 | 51 | 5 | 100
#>
# add row and column percentages
data_tabulate(efc, "c172code", by = "e16sex", proportions = "row")
#> c172code | male | female | <NA> | Total
#> ---------+------------+------------+-----------+------
#> 1 | 5 (62.5%) | 2 (25.0%) | 1 (12.5%) | 8
#> 2 | 30 (45.5%) | 34 (51.5%) | 2 (3.0%) | 66
#> 3 | 4 (25.0%) | 10 (62.5%) | 2 (12.5%) | 16
#> <NA> | 5 (50.0%) | 5 (50.0%) | 0 (0.0%) | 10
#> ---------+------------+------------+-----------+------
#> Total | 44 | 51 | 5 | 100
#>
data_tabulate(efc, "c172code", by = "e16sex", proportions = "column")
#> c172code | male | female | <NA> | Total
#> ---------+------------+------------+-----------+------
#> 1 | 5 (11.4%) | 2 (3.9%) | 1 (20.0%) | 8
#> 2 | 30 (68.2%) | 34 (66.7%) | 2 (40.0%) | 66
#> 3 | 4 (9.1%) | 10 (19.6%) | 2 (40.0%) | 16
#> <NA> | 5 (11.4%) | 5 (9.8%) | 0 (0.0%) | 10
#> ---------+------------+------------+-----------+------
#> Total | 44 | 51 | 5 | 100
#>
# omit missing values
data_tabulate(
efc$c172code,
by = efc$e16sex,
proportions = "column",
include_na = FALSE
)
#> efc$c172code | male | female | Total
#> -------------+------------+------------+------
#> 1 | 5 (12.8%) | 2 (4.3%) | 7
#> 2 | 30 (76.9%) | 34 (73.9%) | 64
#> 3 | 4 (10.3%) | 10 (21.7%) | 14
#> -------------+------------+------------+------
#> Total | 39 | 46 | 85
# round percentages
out <- data_tabulate(efc, "c172code", by = "e16sex", proportions = "column")
print(out, digits = 0)
#> c172code | male | female | <NA> | Total
#> ---------+----------+----------+---------+------
#> 1 | 5 (11%) | 2 (4%) | 1 (20%) | 8
#> 2 | 30 (68%) | 34 (67%) | 2 (40%) | 66
#> 3 | 4 (9%) | 10 (20%) | 2 (40%) | 16
#> <NA> | 5 (11%) | 5 (10%) | 0 (0%) | 10
#> ---------+----------+----------+---------+------
#> Total | 44 | 51 | 5 | 100
#>