Remove rows with all or some NAs (missing values) in data.frame

Question

I\'d like to remove the lines in this data frame that:

a) contain NAs across all columns. Below is my example data frame.

Answer 1

I am a synthesizer:). Here I combined the answers into one function:

#' keep rows that have a certain number (range) of NAs anywhere/somewhere and delete others
#' @param df a data frame
#' @param col restrict to the columns where you would like to search for NA; eg, 3, c(3), 2:5, "place", c("place","age")
#' \cr default is NULL, search for all columns
#' @param n integer or vector, 0, c(3,5), number/range of NAs allowed.
#' \cr If a number, the exact number of NAs kept
#' \cr Range includes both ends 3<=n<=5
#' \cr Range could be -Inf, Inf
#' @return returns a new df with rows that have NA(s) removed
#' @export
ez.na.keep = function(df, col=NULL, n=0){
    if (!is.null(col)) {
        # R converts a single row/col to a vector if the parameter col has only one col
        # see https://radfordneal.wordpress.com/2008/08/20/design-flaws-in-r-2-%E2%80%94-dropped-dimensions/#comments
        df.temp = df[,col,drop=FALSE]
    } else {
        df.temp = df
    }

    if (length(n)==1){
        if (n==0) {
            # simply call complete.cases which might be faster
            result = df[complete.cases(df.temp),]
        } else {
            # credit: http://stackoverflow.com/a/30461945/2292993
            log <- apply(df.temp, 2, is.na)
            logindex <- apply(log, 1, function(x) sum(x) == n)
            result = df[logindex, ]
        }
    }

    if (length(n)==2){
        min = n[1]; max = n[2]
        log <- apply(df.temp, 2, is.na)
        logindex <- apply(log, 1, function(x) {sum(x) >= min && sum(x) <= max})
        result = df[logindex, ]
    }

    return(result)
}

Answer 2

Another option if you want greater control over how rows are deemed to be invalid is

final <- final[!(is.na(final$rnor)) | !(is.na(rawdata$cfam)),]

Using the above, this:

             gene hsap mmul mmus rnor cfam
1 ENSG00000208234    0   NA   NA   NA   2
2 ENSG00000199674    0   2    2    2    2
3 ENSG00000221622    0   NA   NA   2   NA
4 ENSG00000207604    0   NA   NA   1    2
5 ENSG00000207431    0   NA   NA   NA   NA
6 ENSG00000221312    0   1    2    3    2

Becomes:

             gene hsap mmul mmus rnor cfam
1 ENSG00000208234    0   NA   NA   NA   2
2 ENSG00000199674    0   2    2    2    2
3 ENSG00000221622    0   NA   NA   2   NA
4 ENSG00000207604    0   NA   NA   1    2
6 ENSG00000221312    0   1    2    3    2

...where only row 5 is removed since it is the only row containing NAs for both rnor AND cfam. The boolean logic can then be changed to fit specific requirements.

Answer 3

This will return the rows that have at least ONE non-NA value.

final[rowSums(is.na(final))<length(final),]

This will return the rows that have at least TWO non-NA value.

final[rowSums(is.na(final))<(length(final)-1),]

Answer 4

If you want control over how many NAs are valid for each row, try this function. For many survey data sets, too many blank question responses can ruin the results. So they are deleted after a certain threshold. This function will allow you to choose how many NAs the row can have before it's deleted:

delete.na <- function(DF, n=0) {
  DF[rowSums(is.na(DF)) <= n,]
}

By default, it will eliminate all NAs:

delete.na(final)
             gene hsap mmul mmus rnor cfam
2 ENSG00000199674    0    2    2    2    2
6 ENSG00000221312    0    1    2    3    2

Or specify the maximum number of NAs allowed:

delete.na(final, 2)
             gene hsap mmul mmus rnor cfam
2 ENSG00000199674    0    2    2    2    2
4 ENSG00000207604    0   NA   NA    1    2
6 ENSG00000221312    0    1    2    3    2