Find columns with all missing values

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(.))

To find the columns with all values missing

 allmisscols <- apply(dataset,2, function(x)all(is.na(x)));  
 colswithallmiss <-names(allmisscols[allmisscols>0]);    
 print("the columns with all values missing");    
 print(colswithallmiss);

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 

---

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