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rif-statementcountnacounting

How to count number of NAs per row, with conditions

df <- data.frame(PatientID = c("0002" ,"0004", "0005", "0006" ,"0009" ,"0010" ,"0018", "0019" ,"0020" ,"0027", "0039" ,"0041" ,"0042", "0043" ,"0044" ,"0045", "0046", "0047" ,"0048" ,"0049", "0055"),
                 A = c(NA , 977.146 , NA , 964.315 ,NA , 952.311 , NA , 950.797 , 958.975  ,960.712  ,NA , 947.465 , 902.852 , NA,  985.124  ,NA , 930.141 ,1007.790 , 948.848, 1027.110 , 999.414),
                 B = c(998.988 , NA , 998.680 , NA , NA ,1020.560 , 947.751 ,1029.560 , 955.540 , 911.606 , 964.039   ,    NA,  988.087 , 902.367 , 959.338 ,1029.050 , 925.162 , 987.374 ,1066.400  ,957.512 , 917.597),
                 C = c( NA , 987.140 , 961.810 , 929.466 , 978.166, 1005.820  ,925.752 , 969.469 , 943.398  ,936.034,  965.292 , 996.404 , 920.610 , 967.047  ,986.565 , 913.517 , 893.428 , 921.606 , NA , 929.590  ,950.493), 
D = c(975.634 , 987.140 , 961.810 , 929.466 , 978.166, 1005.820 , 925.752 , 969.469  ,943.398 , NA , 965.292 , 996.404 , NA , 967.047 , 986.565 , NA , 893.428 , 921.606 , 976.192 , 929.590 , 950.493),
E = c(1006.330, 1028.070 , NA , 954.274 ,1005.910  ,949.969 , 992.820 , 977.048  ,934.407 , 948.913 , NA , NA , NA,  961.375  ,955.296 , 961.128  ,998.119 ,1009.110 , 994.891 ,1000.170  ,982.763),
G= c(NA , 958.990 , NA , NA , 924.680 , 955.927 , NA , 949.384  ,973.348 , 984.392 , 943.894 , 961.468 , 995.368 , 994.997 , NA , 979.454 , 952.605 , NA  ,   NA, NA , 956.507), stringsAsFactors = F)

Dear all,

I would need to do 2 different excercises:

  1. count the number of NAs per patient this would be 3 for patient 0002 or 1 for patients 0004. Counting columns A:G

this is answered here: R count number of NA values for each row of a CSV

  1. I am unsure how to do this though: count the number if NAs, counting only columns A:D.

Thanks!

Lili

enter image description here

Solution

  • Both can be done with rowSums in the second case subset df to the desired columns.

    rowSums(is.na(df))
# [1] 3 1 3 2 2 0 2 0 0 1 2 2 2 1 1 2 0 1 2 1 0

rowSums(is.na(df[2:5]))
# [1] 2 1 1 1 2 0 1 0 0 1 1 1 1 1 0 2 0 0 1 0 0