{"id":328091,"date":"2022-06-29T12:44:00","date_gmt":"2022-06-29T18:44:00","guid":{"rendered":"https:\/\/datasciencetut.com\/?p=448"},"modified":"2022-06-29T12:44:00","modified_gmt":"2022-06-29T18:44:00","slug":"how-to-find-unmatched-records-in-r-2","status":"publish","type":"post","link":"https:\/\/www.r-bloggers.com\/2022\/06\/how-to-find-unmatched-records-in-r-2\/","title":{"rendered":"How to Find Unmatched Records in R"},"content":{"rendered":"\r\n\r\n
\r\n[This article was first published on Data Science Tutorials<\/a><\/strong>, and kindly contributed to R-bloggers<\/a>]. (You can report issue about the content on this page here<\/a>)\r\n
Want to share your content on R-bloggers? click here<\/a> if you have a blog, or here<\/a> if you don't.\r\n<\/div>\n

The post How to Find Unmatched Records in R<\/a> appeared first on Data Science Tutorials<\/a><\/p>\n\n

How to Find Unmatched Records in R?, To retrieve all rows in one data frame that do not have matching values in another data frame, use R\u2019s anti_join() function from the dplyr package.<\/p>\n\n\n\n

The basic syntax used by this function is as follows.<\/p>\n\n\n\n

How to Remove Columns from a data frame in R \u2013 Data Science Tutorials<\/a><\/p>\n\n\n\n

anti_join(df1, df2, by='col_name')<\/pre>\n\n\n\n
The usage of this syntax is demonstrated in the examples that follow.<\/p>\n\n\n\n
Example 1: Use anti_join() with One Column<\/h2>\n\n\n\n
Suppose we have the two R data frames shown below:<\/p>\n\n\n\n
Let\u2019s build data frames<\/p>\n\n\n\n
df1 <- data.frame(Q1 = c('a', 'b', 'c', 'd', 'e', 'f'),\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Q2 = c(152, 514, 114, 218, 322, 323))\r\ndf2 <- data.frame(Q1 = c('a', 'a', 'a', 'b', 'b', 'b'),\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Q3 = c(523, 324, 233, 134, 237, 141))<\/pre>\n\n\n\n
To return all rows in the first data frame that don\u2019t have a matching Q1 in the second data frame, we can use the anti_join() function.<\/p>\n\n\n\n
Bind together two data frames by their rows or columns in R (datasciencetut.com)<\/a><\/p>\n\n\n\n
library(dplyr)<\/pre>\n\n\n\n
use the \u2018Q1\u2019 column to perform anti join<\/p>\n\n\n\n
anti_join(df1, df2, by='Q1')\r\n\u00a0 Q1\u00a0 Q2\n1\u00a0 c 114\n2\u00a0 d 218\n3\u00a0 e 322\n4\u00a0 f 323<\/pre>\n\n\n\n
We can see that there are exactly 4 Q1\u2019s from the first data frame that does not have a matching Q1 name in the second data frame.<\/p>\n\n\n\n
Example 2: Use anti_join() with Multiple Columns<\/h2>\n\n\n\n
Suppose we have the two R data frames shown below.<\/p>\n\n\n\n
How to Join Data Frames for different column names in R (datasciencetut.com)<\/a><\/p>\n\n\n\n
Let\u2019s create a data frames<\/p>\n\n\n\n
df1 <- data.frame(team=c('A', 'A', 'A', 'B', 'B', 'B'),\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 position=c('G', 'G', 'F', 'G', 'F', 'C'),\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 points=c(152, 114, 219, 254, 356, 441))\r\ndf2 <- data.frame(team=c('A', 'A', 'A', 'B', 'B', 'B'),\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 position=c('G', 'G', 'C', 'G', 'F', 'F'),\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 points=c(142, 214, 319, 133, 517, 422))<\/pre>\n\n\n\n
All rows in the first data frame that lack a matching team and position in the second data frame can be returned using the anti_join() function:<\/p>\n\n\n\n
library(dplyr)<\/pre>\n\n\n\n
utilizing the columns for \u201cteam\u201d and \u201cposition,\u201d perform anti _join.<\/p>\n\n\n\n
How to Count Distinct Values in R \u2013 Data Science Tutorials<\/a><\/p>\n\n\n\n
anti_join(df1, df2, by=c('team', 'position'))\r\n   team position points\n1\u00a0\u00a0\u00a0 A\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 F\u00a0\u00a0\u00a0 219\n2\u00a0\u00a0\u00a0 B\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 C\u00a0\u00a0\u00a0 441<\/pre>\n\n\n\n
We can see that there are exactly two records from the first data frame that do not have a matching team name and position in the second data frame.<\/p>\n\n\n  \n  \n  
<\/div>\n  \n    \n