# Create a sample of data frame column by excluding NAs in R

To create a random sample by excluding missing values of a data frame column, we can use sample function and the negation of is.na with the column of the data frame.

For Example, if we have a data frame called df that contains a column X which has some NAs then we can create a random sample of size 100 of X values by using the following command −

sample(df$X[!is.na(df$X)],100,replace=TRUE).

## Example 1

Following is the code snippet to create dataframe −

x<-rep(c(NA,2,5,10,15),times=4)
df1<-data.frame(x)
df1

The following dataframe is created −

By the
x
1   NA
2    2
3    5
4   10
5   15
6   NA
7    2
8    5
9   10
10  15
11  NA
12   2
13   5
14  10
15  15
16  NA
17   2
18   5
19  10
20  15

To create a random sample of x of size 100 by excluding NAs on the above created data frame, add the above code to the following snippet −

x<-rep(c(NA,2,5,10,15),times=4)
df1<-data.frame(x)
sample(df1$x[!is.na(df1$x)],100,replace=TRUE)

## Output

If you execute all the above given snippets as a single program, it generates the following Output −

[1] 10 10 5 10 5 2 2 2 15 2 2 5 10 10 2 15 10 10 2 5 2 2 10 2
10
[26] 15 2 10 10 2 10 5 2 15 15 10 5 2 5 2 15 5 10 10 10 10 5 15 2
10
[51] 10 15 5 10 15 10 2 10 15 15 15 10 15 15 2 5 5 15 2 15 15 5 2 2
5
[76] 5 2 10 2 10 2 15 10 5 15 2 10 5 15 15 15 10 2 10 5 15 5 5 15
2

## Example 2

Following snippet creates a sample data frame −

y<-rep(c(NA,rnorm(1),rnorm(1),rnorm(1)),times=5)
df2<-data.frame(y)
df2

The following dataframe is created −

            y
1           NA
2   -1.2548971
3    1.1956757
4    0.6556753
5           NA
6   -1.2548971
7    1.1956757
8    0.6556753
9           NA
10  -1.2548971
11   1.1956757
12   0.6556753
13          NA
14  -1.2548971
15   1.1956757
16   0.6556753
17          NA
18  -1.2548971
19   1.1956757
20   0.6556753

To create a random sample of y of size 100 by excluding NAs on the above created data frame, add the following code to the above snippet −

y<-rep(c(NA,rnorm(1),rnorm(1),rnorm(1)),times=5)
df2<-data.frame(y)
sample(df2$y[!is.na(df2$y)],50,replace=TRUE)

## Output

If you execute all the above given snippets as a single program, it generates the following Output −

[1] 0.6556753 -1.2548971 0.6556753 1.1956757 0.6556753 0.6556753
[7] -1.2548971 0.6556753 0.6556753 0.6556753 -1.2548971 1.1956757
[13] 0.6556753 -1.2548971 -1.2548971 -1.2548971 0.6556753 1.1956757
[19] -1.2548971 -1.2548971 0.6556753 -1.2548971 1.1956757 1.1956757
[25] 0.6556753 0.6556753 1.1956757 1.1956757 -1.2548971 0.6556753
[31] 0.6556753 1.1956757 0.6556753 1.1956757 0.6556753 0.6556753
[37] 0.6556753 -1.2548971 1.1956757 0.6556753 0.6556753 -1.2548971
[43] -1.2548971 0.6556753 1.1956757 0.6556753 -1.2548971 1.1956757
[49] -1.2548971 -1.2548971

## Example 3

Following snippet creates a sample data frame −

z<-rep(c(NA,rpois(1,5),rpois(1,2),rpois(1,10),rpois(1,3)),times=4)
df3<-data.frame(z)
df3

The following dataframe is created −

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

To create a random sample of z of size 100 by excluding NAs on the above created data frame, add the following code to the above snippet −

z<-rep(c(NA,rpois(1,5),rpois(1,2),rpois(1,10),rpois(1,3)),times=4)
df3<-data.frame(z)
sample(df3$z[!is.na(df3$z)],200,replace=TRUE)

## Output

If you execute all the above given snippets as a single program, it generates the following Output −

[1] 10 2 2 2 7 2 1 2 10 2 10 2 1 1 7 1 10 2 10 1 2 10 7 1
7
[26] 1 2 10 2 2 10 10 2 7 10 7 7 7 10 2 1 2 2 10 2 2 10 10 7
7
[51] 1 7 1 10 2 10 7 2 7 2 10 2 1 7 7 7 2 2 10 10 10 10 7 7
2
[76] 2 2 1 1 7 7 7 2 1 7 1 2 10 10 2 10 10 10 7 2 10 10 2 10
7
[101] 7 10 7 2 10 2 10 10 7 10 2 2 2 1 1 1 7 10 7 10 7 7 2 2
7
[126] 10 2 2 2 2 1 10 1 2 7 10 10 1 10 10 7 7 2 2 7 2 2 1 2
10
[151] 7 2 7 10 10 1 10 7 2 7 2 7 1 10 7 2 2 2 1 10 10 2 10 1
1
[176] 7 10 1 10 1 1 2 2 1 2 10 1 10 7 7 2 7 10 10 1 10 1 1 1
7