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

 Live Demo

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

 Live Demo

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