Combine Columns

Data

We’ll be using another version of the EPA PM2.5 data used in the the previous EPA - EDA pages. This version has been modified and saved locally in several csv files.

  • pm0 contains data from 1999
  • pm1 contains data from2012
  • cnames.txt is a variable containing list of characters for column names
  • wcol_df is a df holds the indices of the 5 columns we
  • site0 and site1 are for sensors in the NY state area for 1999 and 2012 respectively described as County.Code and Site.ID concatenated together with “.” as seperator
  • both contains the list of sensors that were in use for NY State in both years 1999 and 2012
library(gt)
library(dplyr)
library(tidyverse)
library(stats)   # for quantile

Case Study

Let’s concatenate both County.Code and Site.ID columns into one

  • call it county.site, with the values being separated by “.” just as the column name is
  • the resulting values will match the values in both so we can complete our analysis

Paste

named_pm0 <- named_pm0 |> 
        mutate(county.site = paste(County.Code,Site.ID,sep = "."))
named_pm1 <- named_pm1 |> 
        mutate(county.site = paste(County.Code,Site.ID,sep = "."))
head(named_pm0)
  State.Code County.Code Site.ID     Date Sample.Value county.site
1          1          27       1 19990103           NA        27.1
2          1          27       1 19990106           NA        27.1
3          1          27       1 19990109           NA        27.1
4          1          27       1 19990112        8.841        27.1
5          1          27       1 19990115       14.920        27.1
6          1          27       1 19990118        3.878        27.1
head(named_pm1)
  State.Code County.Code Site.ID     Date Sample.Value county.site
1          1           3      10 20120101          6.7        3.10
2          1           3      10 20120104          9.0        3.10
3          1           3      10 20120107          6.5        3.10
4          1           3      10 20120110          7.0        3.10
5          1           3      10 20120113          5.8        3.10
6          1           3      10 20120116          8.0        3.10