r - check if every column is na

Data

set.seed(1)
data <- c(LETTERS, NA)
df <- data.frame(Flag_A = sample(data), Flag_B = sample(data), 
                 C = sample(data), D = sample(data), Flag_E = sample(data))

df <- rbind(NA, df)

Code

Identifying all NAs per row:

> df$All_NA <- apply(df[, grep("Flag", names(df))], 1, function(x) all(is.na(x)))
> head(df)
  Flag_A Flag_B    C    D Flag_E All_NA
1   <NA>   <NA> <NA> <NA>   <NA>   TRUE
2      H      K    B    T      Y  FALSE
3      J      W    C    K      P  FALSE
4      O      I    H    I   <NA>  FALSE
5      V      L    M    S      R  FALSE
6      E      N    P    E      I  FALSE

Identifying at least one NA per row:

> df$Any_NA <- apply(df[, grep("Flag", names(df))], 1, function(x) anyNA(x))
> head(df)
  Flag_A Flag_B    C    D Flag_E Any_NA
1   <NA>   <NA> <NA> <NA>   <NA>   TRUE
2      H      K    B    T      Y  FALSE
3      J      W    C    K      P  FALSE
4      O      I    H    I   <NA>   TRUE
5      V      L    M    S      R  FALSE
6      E      N    P    E      I  FALSE

I'm not sure what the grep part is supposed to do, but here's a simpler way to accomplish what you want:

 apply(ItemStats_2014[, 2:10], MARGIN = 1, FUN = function(x) all(is.na(x)))

Replace 2:10 with whatever columns you want to check.

Amendment: If you want to detect which columns contain the word "Flag" rather than hard coding their indices -- which would be better anyway! -- I like the package stringr for working with text. You could do this to select your columns:

 library(stringr)
 MyCols <- which(str_detect(names(ItemStats_2014), "Flag"))

Now, replace 2:10 with MyCols in the apply(... code above.

I think that you are trying to test if a row (not a column) contains at least one NA.

Here a dataset

x = c(1:10, NA)
df = data.frame(A = sample(x), B = sample(x), C = sample(x))

And here a loop that test that with anyNA

df$Any_na = apply(df[,2:3], 1, anyNA)
df

    A  B  C Any_na
1  NA  8  9  FALSE
2   5  9 NA   TRUE
3   9  3 10  FALSE
4   7  5  1  FALSE
5   4  2  3  FALSE
6  10  4  6  FALSE
7   3  1  2  FALSE
8   6  6  5  FALSE
9   1 10  7  FALSE
10  2 NA  8   TRUE
11  8  7  4  FALSE