Package: tibble

Function: add_column()

1. Add a new column (year) to the data

Review the data (d1)

# A tibble: 3 x 5
     id item1 item2 item3 item4
  <dbl> <dbl> <dbl> <dbl> <dbl>
1    10     3     5     3    NA
2    11     3     5     1     5
3    12     3     1     3     5

Add participation year equal to “2018-19” for all IDs.

d1 %>%
  tibble::add_column(year = "2018-19")
# A tibble: 3 x 6
     id item1 item2 item3 item4 year   
  <dbl> <dbl> <dbl> <dbl> <dbl> <chr>  
1    10     3     5     3    NA 2018-19
2    11     3     5     1     5 2018-19
3    12     3     1     3     5 2018-19

I can also change the location of the new column by adding the argument .before or .after.

d1 %>%
  tibble::add_column(year = "2018-19", .after = "id")
# A tibble: 3 x 6
     id year    item1 item2 item3 item4
  <dbl> <chr>   <dbl> <dbl> <dbl> <dbl>
1    10 2018-19     3     5     3    NA
2    11 2018-19     3     5     1     5
3    12 2018-19     3     1     3     5


Package: dplyr

Function: mutate()

1. Add a new column (year) to the data

Review the data (d1)

# A tibble: 3 x 5
     id item1 item2 item3 item4
  <dbl> <dbl> <dbl> <dbl> <dbl>
1    10     3     5     3    NA
2    11     3     5     1     5
3    12     3     1     3     5

I can use dplyr::mutate() to create our new variable “year” and assign it a value that is applied to every row. In this case I want to assign a value of 1, for the first year of the study. I can also add the argument .before or .after to change the location of the new variable.

d1 %>%
  dplyr::mutate(year = 1, .after = id)
# A tibble: 3 x 6
     id  year item1 item2 item3 item4
  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1    10     1     3     5     3    NA
2    11     1     3     5     1     5
3    12     1     3     1     3     5

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