R: Replace multiple values in multiple columns of dataframes with NA -

i trying achieve similar this question multiple values must replaced na, , in large dataset.

df <- data.frame(name = rep(letters[1:3], each = 3), foo=rep(1:9),var1 = rep(1:9), var2 = rep(3:5, each = 3)) 

which generates dataframe:

df   name foo var1 var2 1      1    1    3 2      2    2    3 3      3    3    3 4    b   4    4    4 5    b   5    5    4 6    b   6    6    4 7    c   7    7    5 8    c   8    8    5 9    c   9    9    5 

i replace occurrences of, say, 3 , 4 na, in columns start "var".

i know can use combination of [] operators achieve result want:

df[,grep("^var[:alnum:]?",colnames(df))][          df[,grep("^var[:alnum:]?",colnames(df))] == 3 |         df[,grep("^var[:alnum:]?",colnames(df))] == 4    ] <- na  df   name foo var1 var2 1      1    1    na 2      2    2    na 3      3    na   na 4    b   4    na   na 5    b   5    5    na 6    b   6    6    na 7    c   7    7    5 8    c   8    8    5 9    c   9    9    5 

now questions following:

1. is there way in efficient way, given actual dataset has 100.000 lines, , 400 out of 500 variables start "var". seems (subjectively) slow on computer when use double brackets technique.
2. how approach problem if instead of 2 values (3 , 4) replaced na, had long list of, say, 100 various values? there way specify multiple values having clumsy series of conditions separated | operator?

you can using replace:

sel <- grepl("var",names(df)) df[sel] <- lapply(df[sel], function(x) replace(x,x %in% 3:4, na) ) df  #  name foo var1 var2 #1      1    1   na #2      2    2   na #3      3   na   na #4    b   4   na   na #5    b   5    5   na #6    b   6    6   na #7    c   7    7    5 #8    c   8    8    5 #9    c   9    9    5 

some quick benchmarking using million row sample of data suggests quicker other answers.