Question

I have read in a CSV and would like to find “empty” rows and columns, applying something like
isempty = function(x) all(is.na(x) | x == 0 | x == "")
to all columns. The first column is of mode character, all others are numeric.

However, when I do emptycols = apply(mydf, 2, isempty) the logical vector that is returned is all FALSE.

When I try emptycols = apply(mydf[ , -1], 2, isempty) it works perfectly, returning a logical vector which is TRUE for all “empty” columns.

I am aware that I could just use sapply, which works fine anyway, still I wonder: What causes this behaviour? How can the first (character) column affect the application of my function to all the other columns?

Answer 1

@Backlin was right. If you change isemtpy like this:

isempty = function(x) c(typeof(x), all(x == 0 | is.na(x) | x == ""))

The following results show what happens:

> apply(mydata, 2, isempty)
     one         two         three      
[1,] "character" "character" "character"
[2,] "FALSE"     "FALSE"     "FALSE" 

> apply(mydata[,-1], 2, isempty)
     two       three    
[1,] "integer" "integer"
[2,] "TRUE"    "TRUE"   

Quoting @Backlin: “the first column causes apply to turn your data frame into a character matrix, in which “0” would not match 0. However, when you [,-1] it gets turned into a numeric matrix and it works fine.“

sapply behaves itself better:

> sapply(mydata, isempty)
     one         two       three    
[1,] "character" "integer" "integer"
[2,] "FALSE"     "TRUE"    "TRUE"