Supporting Statistical Analysis for Research

2.6 Column operations

The two common column operations are renaming columns, rename(), and selecting columns, select(). The select() function has a number of helper functions that make it easier to select a set of columns, such as, starts_with(), ends_with(), contains(), everything() and the slice operator.

2.6.1 Examples

1. Renaming the variables of the cps data.

   cps_in <- 
     cps_in %>%
     rename(
       id = X1,
       no_deg = nodeg,
       real_earn_74 = re74,
       real_earn_75 = re75,
       real_earn_78 = re78
       )
   cps <-
     cps_in
   
   head(cps, 3)    

   # A tibble: 3 x 11
        id   trt   age  educ black  hisp  marr no_deg real_earn_74
     <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>        <dbl>
   1     1     0    45    11     0     0     1      1       21517.
   2     2     0    21    14     0     0     0      0        3176.
   3     3     0    38    12     0     0     1      0       23039.
   # ... with 2 more variables: real_earn_75 <dbl>, real_earn_78 <dbl>

   The above variable names are Snake coded, each word is separated using the underscore, _.

2. Reordering the variables of the cps data.

   We will make the first two columns of the tibble the id and age variables.

   cps <-
     cps %>%
     select(id, age, everything())
   
   head(cps, 3)    

   # A tibble: 3 x 11
        id   age   trt  educ black  hisp  marr no_deg real_earn_74
     <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>        <dbl>
   1     1    45     0    11     0     0     1      1       21517.
   2     2    21     0    14     0     0     0      0        3176.
   3     3    38     0    12     0     0     1      0       23039.
   # ... with 2 more variables: real_earn_75 <dbl>, real_earn_78 <dbl>

   The everything() function fills in the names of the other variables that were not listed.

3. Selecting variables (subsetting)

   We will select all the variables except the real_earn_78 variable using inclusion.

   cps_part1 <-
     cps %>%
     select(id, age, trt, educ, black, hisp, marr, no_deg, real_earn_74, real_earn_75)
   
   head(cps_part1, 3)

   # A tibble: 3 x 10
        id   age   trt  educ black  hisp  marr no_deg real_earn_74
     <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>        <dbl>
   1     1    45     0    11     0     0     1      1       21517.
   2     2    21     0    14     0     0     0      0        3176.
   3     3    38     0    12     0     0     1      0       23039.
   # ... with 1 more variable: real_earn_75 <dbl>

   We will select all the variables except the real_earn_74 and real_earn_75 variables using exclusion.

   cps_78 <-
     cps %>%
     select(-real_earn_74, -real_earn_75)
   
   head(cps_78, 3)    

   # A tibble: 3 x 9
        id   age   trt  educ black  hisp  marr no_deg real_earn_78
     <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>        <dbl>
   1     1    45     0    11     0     0     1      1       25565.
   2     2    21     0    14     0     0     0      0       13496.
   3     3    38     0    12     0     0     1      0       25565.

4. Removing a variable from a tibble.

   subsetting to a single column of a tibble results in a one column tibble.

   cps %>%
     select(age) %>%
     str()

   Classes 'spec_tbl_df', 'tbl_df', 'tbl' and 'data.frame':    15992 obs. of  1 variable:
    $ age: num  45 21 38 48 18 22 48 18 48 45 ...
    - attr(*, "spec")=
     .. cols(
     ..   X1 = col_double(),
     ..   trt = col_double(),
     ..   age = col_double(),
     ..   educ = col_double(),
     ..   black = col_double(),
     ..   hisp = col_double(),
     ..   marr = col_double(),
     ..   nodeg = col_double(),
     ..   re74 = col_double(),
     ..   re75 = col_double(),
     ..   re78 = col_double()
     .. )

   The pull() function is used to get a column from a tibble as a vector.

   cps %>%
     pull(age) %>%
     str()

    num [1:15992] 45 21 38 48 18 22 48 18 48 45 ...