Remove empty rows and/or columns from a data.frame or matrix.

Source: R/remove_empties.R

Removes all rows and/or columns from a data.frame or matrix that are composed entirely of NA values.

remove_empty(dat, which = c("rows", "cols"), cutoff = 1, quiet = TRUE)

Arguments

dat

the input data.frame or matrix.

which

one of "rows", "cols", or c("rows", "cols"). Where no value of which is provided, defaults to removing both empty rows and empty columns, declaring the behavior with a printed message.

cutoff

What fraction (>0 to <=1) of rows or columns must be empty to be removed?

quiet

Should messages be suppressed (TRUE) or printed (FALSE) indicating the summary of empty columns or rows removed?

Value

Returns the object without its missing rows or columns.

See also

remove_constant() for removing constant columns.

Other remove functions: remove_constant()

Examples

# not run:
# dat %>% remove_empty("rows")
# addressing a common untidy-data scenario where we have a mixture of
# blank values in some (character) columns and NAs in others:
library(dplyr)
dd <- tibble(x=c(LETTERS[1:5],NA,rep("",2)),
             y=c(1:5,rep(NA,3)))
# remove_empty() drops row 5 (all NA) but not 6 and 7 (blanks + NAs)
dd %>% remove_empty("rows")
#> # A tibble: 7 × 2
#>   x         y
#>   <chr> <int>
#> 1 "A"       1
#> 2 "B"       2
#> 3 "C"       3
#> 4 "D"       4
#> 5 "E"       5
#> 6 ""       NA
#> 7 ""       NA
# solution: preprocess to convert whitespace/empty strings to NA,
# _then_ remove empty (all-NA) rows
dd %>% mutate(across(is.character,~na_if(trimws(.),""))) %>%
   remove_empty("rows")
#> Warning: There was 1 warning in `mutate()`.
#> ℹ In argument: `across(is.character, ~na_if(trimws(.), ""))`.
#> Caused by warning:
#> ! Use of bare predicate functions was deprecated in tidyselect 1.1.0.
#> ℹ Please use wrap predicates in `where()` instead.
#>   # Was:
#>   data %>% select(is.character)
#> 
#>   # Now:
#>   data %>% select(where(is.character))
#> # A tibble: 5 × 2
#>   x         y
#>   <chr> <int>
#> 1 A         1
#> 2 B         2
#> 3 C         3
#> 4 D         4
#> 5 E         5