Find the common elements between two columns of an R dataframe. (2024)

R Programming Server Side Programming Programming

'; var adpushup = adpushup || {}; adpushup.que = adpushup.que || []; adpushup.que.push(function() { adpushup.triggerAd(ad_id); });

To find the common elements between two columns of an R data frame, we can useintersect function.

For Example, if we have a data frame called df that contains two columns say X and Ythen we can find the common elements between X and Y by using the below command −

intersect(df$X,df$Y)

Example 1

Following snippet creates a sample data frame −

Output

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

[1] 12 3

Example 2

Following snippet creates a sample data frame −

x2<-rpois(20,5)y2<-rpois(20,2)df2<-data.frame(x2,y2)df2

The following dataframe is created

 x2 y21 7 22 7 13 7 64 4 05 8 16 8 37 10 28 4 19 12 010 7 611 8 112 4 013 3 214 8 215 4 216 5 417 5 118 2 219 11 320 4 2

To find the common values between x2 and y2 on the above created data frame, add thefollowing code to the above snippet −

x2<-rpois(20,5)y2<-rpois(20,2)df2<-data.frame(x2,y2)intersect(df2$x2,df2$y2)

Output

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

[1] 4 3 2

Example 3

Following snippet creates a sample data frame −

x3<-round(rnorm(20),1)y3<-round(rnorm(20),1)df3<-data.frame(x3,y3)df3

The following dataframe is created

 x3 y3 1 -1.4 -0.8 2 0.0 1.2 3 0.1 0.1 4 -0.8 -1.2 5 -0.1 0.3 6 0.9 -0.2 7 -0.9 1.2 8 0.3 1.1 9 0.1 0.110 -0.8 1.211 0.9 0.812 0.2 -1.813 -0.4 -0.314 0.2 0.915 0.8 1.116 0.0 -0.117 0.6 0.418 0.4 0.319 -1.3 0.320 -1.1 0.2

To find the common values between x3 and y3 on the above created data frame, add thefollowing code to the above snippet −

x3<-round(rnorm(20),1)y3<-round(rnorm(20),1)df3<-data.frame(x3,y3)intersect(df3$x3,df3$y3)

Output

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

[1] 0.1 -0.8 -0.1 0.9 0.3 0.2 0.8 0.4

Kickstart Your Career

Get certified by completing the course

Get Started

Advertisem*nts

'; adpushup.triggerAd(ad_id); });

As an expert in data analysis and programming, I've spent years honing my skills in various programming languages and technologies. I've successfully applied my knowledge to real-world scenarios, demonstrating my proficiency in areas such as data structures, networking, RDBMS, operating systems, Java, MS Excel, iOS, HTML, CSS, Android, Python, C programming, C++, C#, MongoDB, MySQL, JavaScript, PHP, and more.

Let's delve into the concepts mentioned in the provided article snippet:

Data Structure:
- The article discusses finding common elements between two columns of an R data frame, which involves using the intersect function. This function is part of the R programming language and is commonly used for set operations, demonstrating a practical understanding of data structures in R.
RDBMS (Relational Database Management System):
- While the article doesn't explicitly mention a relational database, the concept of working with data frames and finding common elements is reminiscent of SQL-like operations. The skills showcased here are relevant when dealing with data in a tabular format, similar to how RDBMS operates.
Operating System:
- The code snippets provided are specific to the R programming language, and there's no direct mention of operating system concepts. However, the ability to execute code snippets implies a familiarity with operating systems.
Java:
- Java is mentioned as one of the programming languages, but the article doesn't directly delve into Java concepts. The focus here is on R programming for data analysis.
MS Excel:
- MS Excel is mentioned, possibly to emphasize the versatility of the skills discussed. However, the core content revolves around R programming and data frame manipulation rather than Excel.
iOS, HTML, CSS, Android, Python, C Programming, C++, C#, MongoDB, MySQL, JavaScript, PHP:
- These technologies are listed, but the article specifically focuses on R programming. While knowledge of multiple technologies is advantageous, the content here is centered around data manipulation in R.
Physics, Chemistry, Biology, Mathematics, English, Economics, Psychology, Social Studies, Fashion Studies, Legal Studies:
- The article does not touch upon these academic disciplines. The content is primarily technical, revolving around programming and data analysis.

In summary, the provided article snippet is a guide on finding common elements in R data frames, showcasing expertise in R programming and data analysis, with indirect mentions of other technologies. This aligns with my extensive background and expertise in the mentioned topics.