Find columns with all missing values

Question

I am writing a function, which needs a check on whether (and which!) column (variable) has all missing values (NA, <NA>). The following is fragment of the function:

test1 <- data.frame (matrix(c(1,2,3,NA,2,3,NA,NA,2), 3,3)) test2 <- data.frame (matrix(c(1,2,3,NA,NA,NA,NA,NA,2), 3,3))  na.test <-  function (data) {   if (colSums(!is.na(data) == 0)){       stop ("The some variable in the dataset has all missing value,      remove the column to proceed")       }       } na.test (test1)  Warning message: In if (colSums(!is.na(data) == 0)) { :   the condition has length > 1 and only the first element will be used 

Q1: Why is the above error and any fixes ?

Q2: Is there any way to find which of columns have all NA, for example output the list (name of variable or column number)?

Answer 1

This is easy enough to with sapply and a small anonymous function:

sapply(test1, function(x)all(is.na(x)))    X1    X2    X3  FALSE FALSE FALSE   sapply(test2, function(x)all(is.na(x)))    X1    X2    X3  FALSE  TRUE FALSE  

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And inside a function:

na.test <-  function (x) {   w <- sapply(x, function(x)all(is.na(x)))   if (any(w)) {     stop(paste("All NA in columns", paste(which(w), collapse=", ")))   } }  na.test(test1)  na.test(test2) Error in na.test(test2) : All NA in columns 2 

Answer 2

In dplyr

ColNums_NotAllMissing <- function(df){ # helper function   as.vector(which(colSums(is.na(df)) != nrow(df))) }  df %>% select(ColNums_NotAllMissing(.))  example: x <- data.frame(x = c(NA, NA, NA), y = c(1, 2, NA), z = c(5, 6, 7))  x %>% select(ColNums_NotAllMissing(.)) 

or, the other way around

Cols_AllMissing <- function(df){ # helper function   as.vector(which(colSums(is.na(df)) == nrow(df))) }   x %>%   select(-Cols_AllMissing(.))