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What is the difference between na.omit and na.rm in R?
The na.omit performs any calculation by considering the NA values but do not include them in the calculation, on the other hand, na.rm remove the NA values and then perform any calculation. For example, if a vector has one NA and 5 values in total then their sum using na.omit will be calculated by excluding NA and by using na.rm it will be calculated by removing NA.
Consider the below data frame −
Example
x1<-sample(c(NA,5,2),20,replace=TRUE) x2<-sample(c(NA,rpois(5,1)),20,replace=TRUE) df1<-data.frame(x1,x2) df1
Output
x1 x2 1 5 2 2 2 0 3 5 0 4 5 0 5 5 0 6 2 0 7 2 0 8 NA 0 9 2 NA 10 2 0 11 2 0 12 NA 0 13 2 2 14 NA 0 15 2 2 16 5 0 17 NA 0 18 2 0 19 NA NA 20 NA NA
Finding row means using na.rm and na.omit −
rowMeans(df1,na.rm=TRUE)
[1] 3.5 1.0 2.5 2.5 2.5 1.0 1.0 0.0 2.0 1.0 1.0 0.0 2.0 0.0 2.0 2.5 0.0 1.0 NaN [20] NaN
rowMeans(na.omit(df1))
1 2 3 4 5 6 7 10 11 13 15 16 18 3.5 1.0 2.5 2.5 2.5 1.0 1.0 1.0 1.0 2.0 2.0 2.5 1.0
Example
y1<-sample(c(NA,rnorm(5)),20,replace=TRUE) y2<-sample(c(NA,rnorm(5)),20,replace=TRUE) df2<-data.frame(y1,y2) df2
Output
y1 y2 1 -1.8606647 NA 2 -0.2447069 NA 3 -1.8606647 -0.03428118 4 0.4729139 NA 5 0.4729139 1.37315226 6 NA -0.03428118 7 -1.8606647 1.37315226 8 -0.2447069 -1.47198479 9 -0.7419227 NA 10 -0.2447069 -1.47198479 11 -0.2447069 -0.22281980 12 -0.7419227 -0.11284788 13 NA -0.03428118 14 NA -0.11284788 15 NA 1.37315226 16 NA -0.03428118 17 -0.4378451 -0.22281980 18 -0.2447069 1.37315226 19 -0.2447069 -0.03428118 20 -0.4378451 -1.47198479
Finding row means using na.rm and na.omit −
rowMeans(df2,na.rm=TRUE)
[1] -1.86066467 -0.24470688 -0.94747292 0.47291388 0.92303307 -0.03428118 [7] -0.24375621 -0.85834583 -0.74192272 -0.85834583 -0.23376334 -0.42738530 [13] -0.03428118 -0.11284788 1.37315226 -0.03428118 -0.33033246 0.56422269 [19] -0.13949403 -0.95491495
rowMeans(na.omit(df2))
3 5 7 8 10 11 12 -0.9474729 0.9230331 -0.2437562 -0.8583458 -0.8583458 -0.2337633 -0.4273853 17 18 19 20 -0.3303325 0.5642227 -0.1394940 -0.9549149
Updated on: 06-Feb-2021
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