1.0Code and Statshttps://blog.hwr-berlin.de/codeandstatsMarkus Löcherhttps://blog.hwr-berlin.de/codeandstats/author/loecher/Grouping Duplicates - Code and Statsrich600338<blockquote class="wp-embedded-content" data-secret="1qdwheD952"><a href="https://blog.hwr-berlin.de/codeandstats/identifying-duplicates/">Grouping Duplicates</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://blog.hwr-berlin.de/codeandstats/identifying-duplicates/embed/#?secret=1qdwheD952" width="600" height="338" title="“Grouping Duplicates” — Code and Stats" data-secret="1qdwheD952" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script>
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Both base R and data.table offer their duplicated() functions which are useful if your main goals is to identify and possibly delete duplicates. For example BirthDay <- as.Date(c(rep("1970-08-08",2), "1950-03-01", "1963-12-10",rep("1968-10-18",2))) HomeTown = c(rep("New Brunswick,NJ",2),"Springfield,OH","Buffalo,NY",rep("Portland,OR",2)) df <-data.frame(BirthDay,HomeTown) duplicated(df) ## [1] FALSE TRUE FALSE FALSE FALSE TRUE df[duplicated(df), ] ## BirthDay HomeTown ## 2 1970-08-08 New Brunswick,NJ … Continue reading