Apply simple rowwise functions — row

Get means, sums and possibly other functions for a select number of columns using dplyr::select-style column selection.

row_sums(data, ..., na_rm = FALSE, varname = "sum")

row_means(data, ..., na_rm = FALSE, varname = "mean")

row_min(data, ..., na_rm = FALSE, varname = "min")

row_max(data, ..., na_rm = FALSE, varname = "max")

row_apply(data, ..., .fun, varname = "var")

Arguments

data	A data frame.
...	The columns to sum, take the mean of, etc. Required.
na_rm	Whether to remove `NA` values or not. Default is `FALSE`.
varname	The column name of the sums means etc. as a character string.
.fun	The function to apply.

Value

A data frame with a new column representing the sums, means or whatever.

Details

Simple wrappers for applying rowwise operations only for selected columns within the tidyverse approach to data processing. The row_apply function requires a function that already works on rows.

Examples

library(tidyext)
library(dplyr)

d = data.frame(x = 1:3, y = 4:6, z = 7:9)

d  %>%
  row_sums(x:y)
#>   x y z sum
#> 1 1 4 7   5
#> 2 2 5 8   7
#> 3 3 6 9   9

d  %>%
  row_means(matches('x|z'))
#>   x y z mean
#> 1 1 4 7    4
#> 2 2 5 8    5
#> 3 3 6 9    6

d  %>%
  row_max(matches('x|y'))
#> # A tibble: 3 x 3
#>       x     y   max
#>   <int> <int> <int>
#> 1     1     4     4
#> 2     2     5     5
#> 3     3     6     6

row_apply(
  d ,
  everything(),
  .fun = function(x)
    apply(x, 1, paste, collapse = '')
)
#>   x y z var
#> 1 1 4 7 147
#> 2 2 5 8 258
#> 3 3 6 9 369