Creating multiple new columns in a dataframe in an iterative way - r

Question

-- Small edit made to test data. Columns are no longer grouped by round, but instead grouped by Team as is the case in the real dataset.
I have tried a variety of methods for creating multiple new columns, while minimising the repetition in the code. My initial and successful method requires excessive copy and pasting, but I would like to minimise this as much as possible. Below is example data for the problem:
df <- tribble(~R1TeamX, ~R2TeamX,~R3TeamX, ~R1TeamY,~R2TeamY, ~R3TeamY,
10, 11, 12, 15, 19, 20,
11, 13, 14, 25, 18, 15)
This example data is for three rounds with the scores for both team X and team Y. I am looking to create additional columns, finding the difference between the scores of Team X and Y. The real dataset has upwards of 30 rounds.
My initial solution used mutate and works as follows:
df <- df %>%
mutate(R1Diff = R1TeamX - R1TeamY,
R2Diff = R2TeamX - R2TeamY,
R3Diff = R3TeamX - R3TeamY)
While this does the job, it is not scaleable. I have attempted to reduce this down to less code using str_c & mutate, but cannot identify the correct looping method to make this work for several lines of code. Below is my attempt at standardising the code so far:
teamx <- str_c("R", 1:3, "TeamX")
teamy <- str_c("R", 1:3, "TeamY")
round_diff <- str_c("R", 1:3, "Diff")
df <- df %>%
mutate(!!round_diff[1] := UQ(parse_quo(teamx[1], global_env())) - UQ(parse_quo(teamy[1], global_env())),
!!round_diff[2] := UQ(parse_quo(teamx[2], global_env())) - UQ(parse_quo(teamy[2], global_env())),
!!round_diff[3] := UQ(parse_quo(teamx[3], global_env())) - UQ(parse_quo(teamy[3], global_env())))
While additional code is required, this standardises my input to some degree reducing some of the leg work, but I know there must be some way to reduce this into a single line. I have explored mutate_at and for loops to no avail. I suspect this problem could also be tackled with purrr::map, but I do have enough ability in this area to identify the correct approach.
Any help would be greatly appreciated.

Answer 1

Whilst it's possible to do this in dplyr and tidyr functions, remember you still have some useful base R options open to you. This method uses lapply and makes the assumption that your columns are alternating between team X and team Y
seq(length(df)/2) %>%
lapply(function(x) df[[x]] - df[[x + 1]]) %>%
as.data.frame() %>%
setNames(paste0("R", seq(length(df)/2), "Diff")) %>%
cbind(df,.)
#> R1TeamX R1TeamY R2TeamX R2TeamY R3TeamX R3TeamY R1Diff R2Diff R3Diff
#> 1 10 11 12 15 19 20 -1 -1 -3
#> 2 11 13 14 25 18 15 -2 -1 -11

Answer 2

try to do it this way
library(tidyverse)
df %>%
mutate(id = row_number()) %>%
pivot_longer(
-id,
names_to = c("set", ".value"),
names_pattern = "(R\\d+Team)(X|Y)"
) %>%
mutate(Diff = X - Y) %>%
pivot_longer(-c(id, set)) %>%
pivot_wider(id, names_from = c(set, name), values_from = value, names_sep = "")

Answer 3

Here's a solution that I believe is robust with respect to the number of rounds, the number of opponents of Team X and the order in which the results are stored.
First, make the data tidy: remove information about Teams and Rounds from column names.
newDF <- df %>%
mutate(id = row_number()) %>%
pivot_longer(
-id,
names_to = c("Round", "Team"),
names_pattern = "R(\\d+)Team(X|Y)",
values_to="Score"
)
Now calculate the differences in scores
newDF %>%
# Calculate difference in scores
mutate(Team=ifelse(Team == "X", Team, "Opponent")) %>%
pivot_wider(values_from=Score, names_from=Team) %>%
mutate(Diff=X - Opponent) %>%
select(-Opponent) %>%
# Bring in identity of oponent
left_join(
newDF %>%
filter(Team != "X") %>%
select(-Score) %>%
rename(Opposition=Team),
by=c("id", "Round")
)
Giving
# A tibble: 6 x 5
id Round X Diff Opposition
<int> <chr> <dbl> <dbl> <chr>
1 1 1 10 -5 Y
2 1 2 11 -8 Y
3 1 3 12 -8 Y
4 2 1 11 -14 Y
5 2 2 13 -5 Y
6 2 3 14 -1 Y
based on OP's revised input data.