# How to replace missing values with row means in an R data frame?

If we have similar characteristics in each column of an R data frame then we can replace the missing values with row means. To replace the missing values with row means we can use the na.aggregate function of zoo package but we would need to use the transposed version of the data frame as na.aggregate works for column means.

## Example1

Consider the below data frame −

Live Demo

x1<−sample(c(NA,1,5),20,replace=TRUE)
x2<−sample(c(NA,10,25),20,replace=TRUE)
x3<−rpois(20,5)
df1<−data.frame(x1,x2,x3)
df1

## Output

 x1 x2 x3
1 5 10 4
2 1 NA 2
3 NA NA 5
4 5 NA 2
5 1 25 8
6 1 10 2
7 1 NA 4
8 5 NA 4
9 5 25 3
10 1 NA 5
11 1 NA 7
12 5 NA 6
13 1 25 4
14 5 NA 8
15 1 25 6
16 NA 10 6
17 5 10 5
18 5 25 8
19 NA 25 3
20 NA 25 5

## Example

library(zoo)
df1[]<−t(na.aggregate(t(df1)))
df1

## Output

 x1 x2 x3
1 5 10.0 4
2 1 1.5 2
3 5 5.0 5
4 5 3.5 2
5 1 25.0 8
6 1 10.0 2
7 1 2.5 4
8 5 4.5 4
9 5 25.0 3
10 1 3.0 5
11 1 4.0 7
12 5 5.5 6
13 1 25.0 4
14 5 6.5 8
15 1 25.0 6
16 8 10.0 6
17 5 10.0 5
18 5 25.0 8
19 14 25.0 3
20 15 25.0 5

## Example2

Live Demo

y1<−sample(c(NA,525,235,401),20,replace=TRUE)
y2<−rnorm(20,500,51.24)
y3<−sample(c(NA,35,47),20,replace=TRUE)
df2<−data.frame(y1,y2,y3)
df2

## Output

   y1   y2     y3
1 525 555.4212 47
2 401 508.7781 47
3 401 488.3973 47
4 NA 546.6707  35
5 401 497.5346 47
6 235 460.7668 35
7 NA 495.0879  35
8 401 441.4254 47
9 NA 446.8322  47
10 235 484.8106 NA
11 235 517.4665 47
12 NA 450.1524 NA
13 525 485.2432 47
14 525 506.0650 35
15 525 470.7504 47
16 NA 370.8190  35
17 525 509.6385 35
18 525 471.0552 35
19 235 468.6052 35
20 401 472.6163 47

Replacing missing values with row means −

## Example

df2[]<−t(na.aggregate(t(df2)))
df2

## Output

      y1      y2       y3
1 525.0000 555.4212 47.0000
2 401.0000 508.7781 47.0000
3 401.0000 488.3973 47.0000
4 290.8353 546.6707 35.0000
5 401.0000 497.5346 47.0000
6 235.0000 460.7668 35.0000
7 265.0440 495.0879 35.0000
8 401.0000 441.4254 47.0000
9 246.9161 446.8322 47.0000
10 235.0000 484.8106 359.9053
11 235.0000 517.4665 47.0000
12 450.1524 450.1524 450.1524
13 525.0000 485.2432 47.0000
14 525.0000 506.0650 35.0000
15 525.0000 470.7504 47.0000
16 202.9095 370.8190 35.0000
17 525.0000 509.6385 35.0000
18 525.0000 471.0552 35.0000
19 235.0000 468.6052 35.0000
20 401.0000 472.6163 47.0000

Updated on: 05-Feb-2021

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