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Count specific values row-wise

Source: R/row_count.R
row_count.Rd

row_count() mimics base R's rowSums(), with sums for a specific value indicated by count. Hence, it is similar to rowSums(x == count, na.rm = TRUE), but offers some more options, including strict comparisons. Comparisons using == coerce values to atomic vectors, thus both 2 == 2 and "2" == 2 are TRUE. In row_count(), it is also possible to make "type safe" comparisons using the allow_coercion argument, where "2" == 2 is not true.

Usage

row_count(
  data,
  select = NULL,
  exclude = NULL,
  count = NULL,
  allow_coercion = TRUE,
  ignore_case = FALSE,
  regex = FALSE,
  verbose = TRUE
)

Arguments

data

A data frame with at least two columns, where number of specific values are counted row-wise.

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"), a character vector of variable names (e.g., c("col1", "col2", "col3")), or a character vector of variable names including ranges specified via : (e.g., c("col1:col3", "col5")),

  • 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. 1 or c(1, 3, 5)),

  • a vector of negative integers, giving the positions counting from the right (e.g., -1 or -1:-3),

  • one of the following select-helpers: starts_with(), ends_with(), contains(), a range using : or regex(""). starts_with(), ends_with(), and contains() accept several patterns, e.g starts_with("Sep", "Petal").

  • or a function testing for logical conditions, e.g. is.numeric() (or is.numeric), or any user-defined function that selects the variables for which the function returns TRUE (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.numeric or -(Sepal.Width:Petal.Length). Note: Negation means that matches are excluded, and thus, the exclude argument can be used alternatively. For instance, select=-ends_with("Length") (with -) is equivalent to exclude=ends_with("Length") (no -). In case negation should not work as expected, use the exclude argument instead.

If NULL, selects all columns. Patterns that found no matches are silently ignored, e.g. extract_column_names(iris, select = c("Species", "Test")) will just return "Species".

exclude

See select, however, column names matched by the pattern from exclude will be excluded instead of selected. If NULL (the default), excludes no columns.

count

The value for which the row sum should be computed. May be a numeric value, a character string (for factors or character vectors), NA or Inf.

allow_coercion

Logical. If FALSE, count matches only values of same class (i.e. when count = 2, the value "2" is not counted and vice versa). By default, when allow_coercion = TRUE, count = 2 also matches "2". In order to count factor levels in the data, use count = factor("level"). See 'Examples'.

ignore_case

Logical, if TRUE and when one of the select-helpers or a regular expression is used in select, ignores lower/upper case in the search pattern when matching against variable names.

regex

Logical, if TRUE, the search pattern from select will be treated as regular expression. When regex = 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 = TRUE is comparable to using one of the two select-helpers, select = contains("") or select = regex(""), however, since the select-helpers may not work when called from inside other functions (see 'Details'), this argument may be used as workaround.

verbose

Toggle warnings.

Value

A vector with row-wise counts of values specified in count.

Examples

dat <- data.frame(
  c1 = c(1, 2, NA, 4),
  c2 = c(NA, 2, NA, 5),
  c3 = c(NA, 4, NA, NA),
  c4 = c(2, 3, 7, 8)
)

# count all 4s per row
row_count(dat, count = 4)
#> [1] 0 1 0 1
# count all missing values per row
row_count(dat, count = NA)
#> [1] 2 0 3 1

dat <- data.frame(
  c1 = c("1", "2", NA, "3"),
  c2 = c(NA, "2", NA, "3"),
  c3 = c(NA, 4, NA, NA),
  c4 = c(2, 3, 7, Inf)
)
# count all 2s and "2"s per row
row_count(dat, count = 2)
#> [1] 1 2 0 0
# only count 2s, but not "2"s
row_count(dat, count = 2, allow_coercion = FALSE)
#> [1] 1 0 0 0

dat <- data.frame(
  c1 = factor(c("1", "2", NA, "3")),
  c2 = c("2", "1", NA, "3"),
  c3 = c(NA, 4, NA, NA),
  c4 = c(2, 3, 7, Inf)
)
# find only character "2"s
row_count(dat, count = "2", allow_coercion = FALSE)
#> [1] 1 0 0 0
# find only factor level "2"s
row_count(dat, count = factor("2"), allow_coercion = FALSE)
#> [1] 0 1 0 0