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Asked: 2026-05-23T15:32:31+00:00

I am working with some US govt data which has a lengthy list of

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I am working with some US govt data which has a lengthy list of cities and zip codes.
After some work, the data is in the following format.

dat1 = data.frame(keyword=c("Bremen", "Brent", "Centreville, AL", "Chelsea, AL", "Bailytown, Alabama", "Calera, Alabama",
              "54023", "54024"), tag=c(rep("AlabamCity",2), rep("AlabamaCityST",2), rep("AlabamaCityState",2), rep("AlabamaZipCode",2)))
dat1

However, there are certain keywords which aren’t properly working. So in the below example, there are two ‘zip codes’ which
are labeled as ‘AlabamaCity’ and ‘AlabamaCityState’. For some reason, the original data set from the government has several zipcodes
which aren’t properly grouped with the other zip codes.

dat2 = data.frame(keyword=c("Bremen", "Brent", "50143", "Chelsea, AL", "Bailytown, Alabama", "52348",
              "54023", "54024"), tag=c(rep("AlabamCity",2), rep("AlabamaCityST",2), rep("AlabamaCityState",2), rep("AlabamaZipCode",2)))
dat2

I wanted to know how I could iterate through the entire list of keywords and delete all the rows with numeric values (they’re acctually saved as character
values) which don’t have a ‘AlabamaZipCode’ tag. So the previous data should end up looking like.

dat3 = data.frame(keyword=c("Bremen", "Brent", "Chelsea, AL", "Bailytown, Alabama", "54023", "54024"), 
          tag=c(rep("AlabamCity",2), rep("AlabamaCityST",1), rep("AlabamaCityState",1), rep("AlabamaZipCode",2)))
dat3

The challange seems to be that there are certain numeric values which I want to keep and others which I want to delete.
Can anyone help.

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1 Answer

  1. Editorial Team

    I think two grepl expressions should do the trick:

    > dat2[ !( grepl("City", dat2$tag) &  grepl("^\\d", dat2$keyword) ) , ]
             keyword              tag
1             Bremen       AlabamCity
2              Brent       AlabamCity
4        Chelsea, AL    AlabamaCityST
5 Bailytown, Alabama AlabamaCityState
7              54023   AlabamaZipCode
8              54024   AlabamaZipCode

    You are eliminating the rows where there are digits in keyword and “City” in tag

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