Hi Shawn,
Here is a solution using base R (no dplyr). The only regex appears in the
statement to get the common prefixes.
colsPrefixes <- sub("_Planned$","",colnames(gross_test)[
grep("_Planned$",colnames(gross_test))])
f <- function(s) {
gross_test[,paste(s,"Diff",sep="_")] <<-
gross_test[,paste(s,"Actual",sep="_")] -
gross_test[,paste(s,"Planned",sep="_")]
}
tmpOut <- sapply(colsPrefixes,f)
HTH,
Eric
On Tue, Aug 6, 2019 at 3:34 PM Shawn Way <s...@meco.com> wrote:
> I have a tibble that has a large number of variables and because I'm
> partial lazy (and I really want to know how to do this), I would like find
> out if it possible to partial column matching with the mutate function in
> the tidyverse.
>
> I have a tibble with the following
>
> > gross_test <- gross_df %>%
> +
>
> select(Job,Mfg_Labor_Hrs_Planned,Mfg_Labor_Hrs_Actual,Eng_Labor_Hrs_Planned,Eng_Labor_Hrs_Actual)
> > gross_test
> # A tibble: 6 x 5
> Job Mfg_Labor_Hrs_Pla~ Mfg_Labor_Hrs_Ac~ Eng_Labor_Hrs_Pl~
> Eng_Labor_Hrs_Ac~
> * <dbl> <dbl> <dbl> <dbl>
> <dbl>
> 1 9892 950. 1082. 133
> 302.
> 2 9893 950. 1082. 133
> 302.
> 3 9652 950. 1082. 133
> 302.
> 4 9894 950. 1082. 133
> 302.
> 5 9652 950. 1082. 133
> 302.
> 6 9894 950. 1082. 133
> 302.
> >
>
> The column names follow a pattern of (.+)_Planned and (.+)_Actual and
> there are a large number of them. What I would like to do is the following:
>
> mutate(Mfg_Labor_Hrs_Diff= Mfg_Labor_Hrs_Actual- Mfg_Labor_Hrs_Planned)
>
> using a regex. Something like the following
>
> mutate( $1_Diff = $1_Actual - $1_Planned)
>
> Where $1 was Mfg_Labor_Hrs. This would iterate over the entire set of
> columns doing Mfg_Labor_Hrs and then Eng_Labor_Hrs, etc.
>
> Is this even possible or will I need to explicitly write out the mutate
> for each combination?
>
> Thanks for looking at this.
>
> Shawn Way, P.E.
>
>
>
> ______________________________________________
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