Remove Rows in R Matrix Where All Data Is Na

Remove rows in R matrix where all data is NA

Solutions using rowSums() generally outperform apply() ones:

m <- structure(c( 1,  NA,  3,  4,  5, 
                  6,  NA,  8, NA, 10, 
                 11,  NA, 13, NA, NA), 
               .Dim = c(5L, 3L))

m[rowSums(is.na(m)) != ncol(m), ]

     [,1] [,2] [,3]
[1,]    1    6   11
[2,]    3    8   13
[3,]    4   NA   NA
[4,]    5   10   NA

remove Rows with complete set of NA

We can use dplyr. With the example by @lovalery:

library(dplyr)

df %>% filter(!if_all(V2:V3, is.na))

#>   V1 V2 V3
#> 1  3  3 NA
#> 2 NA  1 NA
#> 3  3  5 NA

We can use many different selection statements inside if_all. Check the documentation for more examples.

Remove rows where all variables are NA using dplyr

Since dplyr 0.7.0 new, scoped filtering verbs exists. Using filter_any you can easily filter rows with at least one non-missing column:

# dplyr 0.7.0
dat %>% filter_all(any_vars(!is.na(.)))

Using @hejseb benchmarking algorithm it appears that this solution is as efficient as f4.

UPDATE:

Since dplyr 1.0.0 the above scoped verbs are superseded. Instead the across function family was introduced, which allows to perform a function on multiple (or all) columns. Filtering rows with at least one column being not NA looks now like this:

# dplyr 1.0.0
dat %>% filter(if_any(everything(), ~ !is.na(.)))

Removing rows of a matrix with at least one NA in R

Try using the na.omit function:

x <- matrix(c(32, 54, 34, NA, 10, NA, 17, 93, NA), nrow = 3, ncol = 3, byrow = TRUE)
na.omit(x)

Output:

     [,1] [,2] [,3]
[1,]   32   54   34
attr(,"na.action")
[1] 2 3
attr(,"class")
[1] "omit"

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

Also check complete.cases :

> final[complete.cases(final), ]
             gene hsap mmul mmus rnor cfam
2 ENSG00000199674    0    2    2    2    2
6 ENSG00000221312    0    1    2    3    2

na.omit is nicer for just removing all NA's. complete.cases allows partial selection by including only certain columns of the dataframe:

> final[complete.cases(final[ , 5:6]),]
             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

Your solution can't work. If you insist on using is.na, then you have to do something like:

> final[rowSums(is.na(final[ , 5:6])) == 0, ]
             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

but using complete.cases is quite a lot more clear, and faster.

How to remove only rows that have all NA in R?

Here is one option; first you need to define the NA pattern.

df[df == ""] <- NA # define NA pattern
df[rowSums(is.na(df)) != ncol(df), ] # result
# Try with x1 <- c("Bob", "Mary","","","") 

Remove rows with NAs for matrices in a list

If it is only for the first column, we loop through the list with lapply, then use an. anonymous function call, get the first column (x[,1]), check the NA with (is.na), negate (!) i.e. for non_NA elemengts and subset the rows of the dataset based. on that

list2 <- lapply(list, function(x) x[!is.na(x[,1]),, drop = FALSE])

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For the entire dataset in the list, we can make use of rowSums on the logical matrix created with is.na on the entire dataset.

lapply(list, function(x) x[rowSums(is.na(x)) == 0,, drop = FALSE])

How to remove row if it has a NA value in one certain column

The easiest solution is to use is.na():

df[!is.na(df$B), ]

which gives you:

   A B  C
1 NA 2 NA
2  1 2  3
4  1 2  3

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