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How to find the row mean for columns in an R data frame by ignoring missing values?
To find the row mean for columns by ignoring missing values, we would need to use rowMeans function with na.rm. For example, if we have a data frame called df that contains five columns and some of the values are missing then the row means will be calculated by using the command: rowMeans(df,na.rm=TRUE).
Consider the below data frame −
Example
x1<-sample(c(NA,rpois(4,5)),20,replace=TRUE) x2<-sample(c(NA,rpois(4,5)),20,replace=TRUE) df1<-data.frame(x1,x2) df1
Output
x1 x2 1 NA 2 2 7 9 3 5 8 4 7 NA 5 NA 8 6 7 8 7 5 2 8 5 NA 9 5 6 10 NA 8 11 7 8 12 5 9 13 5 9 14 NA 9 15 5 6 16 5 NA 17 5 9 18 7 9 19 7 9 20 NA NA
Finding the row means for rows in df1 −
Example
df1$RowMeans<-rowMeans(df1,na.rm=TRUE) df1
Output
x1 x2 RowMeans 1 NA 2 2.0 2 7 9 8.0 3 5 8 6.5 4 7 NA 7.0 5 NA 8 8.0 6 7 8 7.5 7 5 2 3.5 8 5 NA 5.0 9 5 6 5.5 10 NA 8 8.0 11 7 8 7.5 12 5 9 7.0 13 5 9 7.0 14 NA 9 9.0 15 5 6 5.5 16 5 NA 5.0 17 5 9 7.0 18 7 9 8.0 19 7 9 8.0 20 NA NA NaN
Example
y1<-sample(c(NA,rnorm(5)),20,replace=TRUE) y2<-sample(c(NA,rnorm(5)),20,replace=TRUE) df2<-data.frame(y1,y2) df2
Output
y1 y2 1 0.5896447 1.8711656 2 0.9310379 1.1159848 3 2.9883385 0.6290764 4 NA -0.6323118 5 1.4797316 NA 6 2.9883385 -0.1533375 7 0.9310379 1.8711656 8 -0.1495998 0.6290764 9 0.5896447 1.8711656 10 0.5896447 NA 11 2.9883385 -0.1533375 12 0.5896447 -0.6323118 13 2.9883385 0.6290764 14 -0.1495998 1.8711656 15 1.4797316 1.8711656 16 -0.1495998 -0.6323118 17 -0.1495998 0.6290764 18 2.9883385 -0.6323118 19 2.9883385 -0.1533375 20 2.9883385 -0.1533375
Finding the row means for rows in df2 −
Example
df2$RowMeans<-rowMeans(df2,na.rm=TRUE) df2
Output
y1 y2 RowMeans 1 0.5896447 1.8711656 1.23040515 2 0.9310379 1.1159848 1.02351135 3 2.9883385 0.6290764 1.80870743 4 NA -0.6323118 -0.63231175 5 1.4797316 NA 1.47973158 6 2.9883385 -0.1533375 1.41750051 7 0.9310379 1.8711656 1.40110175 8 -0.1495998 0.6290764 0.23973829 9 0.5896447 1.8711656 1.23040515 10 0.5896447 NA 0.58964468 11 2.9883385 -0.1533375 1.41750051 12 0.5896447 -0.6323118 -0.02133354 13 2.9883385 0.6290764 1.80870743 14 -0.1495998 1.8711656 0.86078292 15 1.4797316 1.8711656 1.67544859 16 -0.1495998 -0.6323118 -0.39095576 17 -0.1495998 0.6290764 0.23973829 18 2.9883385 -0.6323118 1.17801337 19 2.9883385 -0.1533375 1.41750051 20 2.9883385 -0.1533375 1.41750051
Updated on: 08-Feb-2021
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