This method counts tagged NA values (see tagged_na) in a vector and prints a frequency table of counted tagged NAs.

count_na(x, ...)

Arguments

x

A vector or data frame.

...

Optional, unquoted names of variables that should be selected for further processing. Required, if x is a data frame (and no vector) and only selected variables from x should be processed. You may also use functions like : or tidyselect's select_helpers. See 'Examples' or package-vignette.

Value

A data frame with counted tagged NA values.

Examples

library(haven)
#> #> Attaching package: 'haven'
#> The following objects are masked from 'package:sjlabelled': #> #> as_factor, read_sas, read_spss, read_stata, write_sas, zap_labels
x <- labelled( x = c(1:3, tagged_na("a", "c", "z"), 4:1, tagged_na("a", "a", "c"), 1:3, tagged_na("z", "c", "c"), 1:4, tagged_na("a", "c", "z")), labels = c("Agreement" = 1, "Disagreement" = 4, "First" = tagged_na("c"), "Refused" = tagged_na("a"), "Not home" = tagged_na("z")) ) count_na(x)
#> frq label raw.prc valid.prc cum.prc #> 1 5 First 41.67 41.67 41.67 #> 2 3 Not home 25.00 25.00 66.67 #> 3 4 Refused 33.33 33.33 100.00 #>
y <- labelled( x = c(1:3, tagged_na("e", "d", "f"), 4:1, tagged_na("f", "f", "d"), 1:3, tagged_na("f", "d", "d"), 1:4, tagged_na("f", "d", "f")), labels = c("Agreement" = 1, "Disagreement" = 4, "An E" = tagged_na("e"), "A D" = tagged_na("d"), "The eff" = tagged_na("f")) ) # create data frame dat <- data.frame(x, y) # possible count()-function calls count_na(dat)
#> # x #> #> frq label raw.prc valid.prc cum.prc #> 1 5 First 41.67 41.67 41.67 #> 2 3 Not home 25.00 25.00 66.67 #> 3 4 Refused 33.33 33.33 100.00 #> #> #> # y #> #> frq label raw.prc valid.prc cum.prc #> 1 5 A D 41.67 41.67 41.67 #> 2 1 An E 8.33 8.33 50.00 #> 3 6 The eff 50.00 50.00 100.00 #> #>
count_na(dat$x)
#> frq label raw.prc valid.prc cum.prc #> 1 5 First 41.67 41.67 41.67 #> 2 3 Not home 25.00 25.00 66.67 #> 3 4 Refused 33.33 33.33 100.00 #>
count_na(dat, x)
#> # x #> #> frq label raw.prc valid.prc cum.prc #> 1 5 First 41.67 41.67 41.67 #> 2 3 Not home 25.00 25.00 66.67 #> 3 4 Refused 33.33 33.33 100.00 #> #>
count_na(dat, x, y)
#> # x #> #> frq label raw.prc valid.prc cum.prc #> 1 5 First 41.67 41.67 41.67 #> 2 3 Not home 25.00 25.00 66.67 #> 3 4 Refused 33.33 33.33 100.00 #> #> #> # y #> #> frq label raw.prc valid.prc cum.prc #> 1 5 A D 41.67 41.67 41.67 #> 2 1 An E 8.33 8.33 50.00 #> 3 6 The eff 50.00 50.00 100.00 #> #>