Data Structure
Networking
RDBMS
Operating System
Java
MS Excel
iOS
HTML
CSS
Android
Python
C Programming
C++
C#
MongoDB
MySQL
Javascript
PHP
Physics
Chemistry
Biology
Mathematics
English
Economics
Psychology
Social Studies
Fashion Studies
Legal Studies
- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who
Find the frequency of non-NA values in each row of an R dataframe.
To find the frequency of non-NA values in each row of an R data frame, we can use apply function along with na.omit function.
For Example, if we have a data frame called df that contains some NA values then we can find the frequency of non-NA values in each row of df by using the command as follows −
apply(df,1, function(x) length(na.omit(x)))
Example 1
Following snippet creates a sample data frame −
x1<-sample(c(NA,2,5),20,replace=TRUE) x2<-sample(c(NA,10,15),20,replace=TRUE) x3<-sample(c(NA,1,3),20,replace=TRUE) df1<-data.frame(x1,x2,x3) df1
The following dataframe is created
x1 x2 x3 1 5 10 3 2 2 10 1 3 2 10 NA 4 2 15 3 5 NA 10 1 6 5 NA NA 7 5 15 NA 8 NA 15 3 9 2 NA NA 10 2 10 NA 11 NA 15 NA 12 5 15 3 13 5 10 3 14 5 NA 3 15 2 NA 1 16 NA 15 NA 17 NA 15 NA 18 2 NA 3 19 2 15 3 20 2 10 NA
To find the frequency of non-NA values in each row of df1 on the above created data frame, add the following code to the above snippet −
x1<-sample(c(NA,2,5),20,replace=TRUE) x2<-sample(c(NA,10,15),20,replace=TRUE) x3<-sample(c(NA,1,3),20,replace=TRUE) df1<-data.frame(x1,x2,x3) apply(df1,1, function(x) length(na.omit(x)))
Output
If you execute all the above given snippets as a single program, it generates the following Output −
[1] 3 3 2 3 2 1 2 2 1 2 1 3 3 2 2 1 1 2 3 2
Example 2
Following snippet creates a sample data frame −
y1<-sample(c(NA,round(rnorm(3),2)),20,replace=TRUE) y2<-sample(c(NA,round(rnorm(3,10,2),2)),20,replace=TRUE) y3<-sample(c(NA,round(rnorm(3,25,3.2),2)),20,replace=TRUE) df2<-data.frame(y1,y2,y3) df2
The following dataframe is created
y1 y2 y3 1 0.27 NA 24.02 2 0.27 9.30 24.02 3 0.49 NA NA 4 NA 8.10 25.60 5 0.49 11.11 29.34 6 -0.16 NA 25.60 7 0.49 9.30 29.34 8 -0.16 8.10 25.60 9 -0.16 11.11 24.02 10 0.49 8.10 29.34 11 NA 11.11 25.60 12 0.27 9.30 29.34 13 -0.16 NA NA 14 0.27 8.10 25.60 15 NA 8.10 29.34 16 0.27 NA NA 17 -0.16 9.30 24.02 18 NA 9.30 NA 19 0.49 11.11 25.60 20 0.49 NA 25.60
To find the frequency of non-NA values in each row of df2 on the above created data frame, add the following code to the above snippet −
y1<-sample(c(NA,round(rnorm(3),2)),20,replace=TRUE) y2<-sample(c(NA,round(rnorm(3,10,2),2)),20,replace=TRUE) y3<-sample(c(NA,round(rnorm(3,25,3.2),2)),20,replace=TRUE) df2<-data.frame(y1,y2,y3) apply(df2,1, function(x) length(na.omit(x)))
Output
If you execute all the above given snippets as a single program, it generates the following Output −
[1] 2 3 1 2 3 2 3 3 3 3 2 3 1 3 2 1 3 1 3 2
Example 3
Following snippet creates a sample data frame −
z1<-sample(c(NA,runif(2,1,5)),20,replace=TRUE) z2<-sample(c(NA,runif(2,5,10)),20,replace=TRUE) z3<-sample(c(NA,runif(3,1,2)),20,replace=TRUE) df3<-data.frame(z1,z2,z3) df3
The following dataframe is created
z1 z2 z3 1 2.237191 NA 1.242077 2 1.137563 8.614818 1.120255 3 NA 9.840686 NA 4 2.237191 9.840686 1.242077 5 1.137563 NA NA 6 NA 9.840686 NA 7 1.137563 NA 1.242077 8 2.237191 9.840686 1.877137 9 1.137563 8.614818 1.120255 10 2.237191 9.840686 NA 11 1.137563 8.614818 1.877137 12 NA 8.614818 1.877137 13 NA NA 1.877137 14 1.137563 8.614818 NA 15 NA 9.840686 1.242077 16 1.137563 9.840686 1.877137 17 2.237191 9.840686 NA 18 1.137563 8.614818 1.242077 19 NA 8.614818 1.120255 20 2.237191 NA 1.242077
To find the frequency of non-NA values in each row of df2 on the above created data frame, add the following code to the above snippet −
z1<-sample(c(NA,runif(2,1,5)),20,replace=TRUE) z2<-sample(c(NA,runif(2,5,10)),20,replace=TRUE) z3<-sample(c(NA,runif(3,1,2)),20,replace=TRUE) df3<-data.frame(z1,z2,z3) apply(df3,1, function(x) length(na.omit(x)))
Output
If you execute all the above given snippets as a single program, it generates the following Output −
[1] 2 3 1 3 1 1 2 3 3 2 3 2 1 2 2 3 2 3 2 2
179 Views
