How to use R to create baseball Splits from Game Logs - r

Question

I'm trying to use R to recreate Baseball Splits as found on MLB.com. The splits are created from Game Logs and provide different cuts of the data. For example, home games vs. away games, day games vs. night games, August vs. September and many more all in one convenient table. I believe the ratios (AVG, OBP SLG) can all be added via mutate once the basic splits have been totaled.
My Question is, what's the best and most efficient way to create these splits and how should the data be shaped. The game log obviously has additional (hidden) column(s) that contain the Split topics. The nature of the problem leads me to believe purrr might be a tool to employ but I can't quite wrap my mind around how to approach this one.
Here is how I believe the data should be shaped and a link to a sample game log. I would appreciate any thoughts, ideas or solutions to this problem.
Links and images of Game Logs and Splits for National outfielder Juan Soto are set forth below.
Game Logs: Juan Soto Game Log
Splits: Juan Soto Game Splits
Splits

Answer 1

I've gone through the dataset, although I'm not sure if the sum values match, and neither the averages relative to the images above.
You're right about mutating for creating the values you suggest.
However, hopefully my approach can help you get what you're after.
library(tidyverse)
library(data.table)
game.splits <- "https://raw.githubusercontent.com/MundyMSDS/GAMELOG/main/SAMPLE_GAME_LOG.csv"
game.splits <- fread(game.splits, fill = TRUE)
game.splits.pivot <- game.splits
game.splits.pivot$Var1 <- ifelse(game.splits.pivot$Var1 %in% "HOME", 1, 0)
game.splits.pivot$Var2 <- ifelse(game.splits.pivot$Var2 %in% "NIGHT", 3, 2)
game.splits.pivot$Var3 <- ifelse(game.splits.pivot$Var3 %in% "SEPTEMBER", 5, 4)
game.splits.pivot <- game.splits.pivot %>% pivot_longer(-c(1:16, 20))
colnames(game.splits.pivot)[19] <- "name_c"
game.splits.pivot <- game.splits.pivot[, -c(17, 18)]
game.splits.pivot <- game.splits.pivot %>% pivot_longer(-c(1:3, 17))
#test
game.splits.pivot_test <- game.splits.pivot[, -c(1, 2, 3)]
game.splits.pivot_test <- aggregate(value ~ name_c + name, game.splits.pivot_test, sum)
game.splits.pivot_test <- game.splits.pivot_test %>% pivot_wider(names_from = name, values_from = value)
lc_name <- tibble(name_c = 0:5, split = c("HOME", "AWAY", "DAY", "NIGHT", "AUGUST", "SEPTEMBER"))
game.splits.pivot_test <- game.splits.pivot_test %>%
inner_join(lc_name, by = "name_c") %>%
arrange(name_c) %>%
select(-name_c)
game.splits.pivot_test <- game.splits.pivot_test[, c(14, 3, 9, 6, 1, 2, 7, 10, 4, 8, 12, 11, 5, 13)]
A look into the dataset:
# A tibble: 6 x 14
split AB R H `2B` `3B` HR RBI BB IBB SO SB CS TB
<chr> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
1 HOME 88 24 32 5 0 9 23 15 5 12 1 2 64
2 AWAY 66 15 22 9 0 4 14 26 7 16 5 0 43
3 DAY 29 21 18 4 0 5 17 12 4 3 4 0 37
4 NIGHT 125 18 36 10 0 8 20 29 8 25 2 2 70
5 AUGUST 90 21 33 6 0 11 25 13 1 13 1 1 72
6 SEPTEMBER 64 18 21 8 0 2 12 28 11 15 5 1 35

Answer 2

This turned out to be more straight-forward than I had thought. The following solution relies upon pivot_longer to shape the data and summarise_if to tally the splits - no rbinds or purrr needed.
library(tidyverse)
game.splits <- "https://raw.githubusercontent.com/MundyMSDS/GAMELOG/main/SAMPLE_GAME_LOG.csv"
game.splits <- read_csv(game.splits)
game.splits %>%
pivot_longer(Var1:Var3, names_to = "split") %>%
group_by(split) %>%
arrange(split) %>%
select(split, value, everything()) %>%
ungroup() %>%
select(split, value, everything()) %>%
select(-Date, -OPP) %>%
mutate(value = str_c(split, "_", value)) %>%
group_by(value) %>%
summarise_if(is.numeric, sum) %>%
mutate(value= str_replace(value, "(Var\\d_)",""))
#> # A tibble: 6 x 14
#> value AB R H TB `2B` `3B` HR RBI BB IBB SO SB
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 AWAY 88 24 32 64 5 0 9 23 15 5 12 1
#> 2 HOME 66 15 22 43 9 0 4 14 26 7 16 5
#> 3 DAY 29 21 18 37 4 0 5 17 12 4 3 4
#> 4 NIGHT 125 18 36 70 10 0 8 20 29 8 25 2
#> 5 AUGUST 90 21 33 72 6 0 11 25 13 1 13 1
#> 6 SEPTE~ 64 18 21 35 8 0 2 12 28 11 15 5
Created on 2021-03-03 by the reprex package (v0.3.0)