Unfortunately, the next bit of code is the data wrangling. It’s the same bit of code repeated with exceptionally minor changes, but necessary changes. I hope this is useful.
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.7
## v tidyr 1.2.0 v stringr 1.4.0
## v readr 2.1.2 v forcats 0.5.1## Warning: package 'tibble' was built under R version 4.1.2## Warning: package 'tidyr' was built under R version 4.1.2## Warning: package 'readr' was built under R version 4.1.2## Warning: package 'dplyr' was built under R version 4.1.2## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()karts <- read_csv("karts.csv")## Rows: 40 Columns: 14## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): body
## dbl (13): wg, ac, on, of, mt, sl, sw, sa, sg, tl, tw, ta, tg
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.characters <- read_csv("characters.csv")## Rows: 44 Columns: 14
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): driver
## dbl (13): wg, ac, on, of, mt, sl, sw, sa, sg, tl, tw, ta, tg
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.tires <- read_csv("tires.csv")## Rows: 21 Columns: 14
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): tire
## dbl (13): wg, ac, on, of, mt, sl, sw, sa, sg, tl, tw, ta, tg
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.gliders <- read_csv("gliders.csv")## Rows: 14 Columns: 14
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): glider
## dbl (13): wg, ac, on, of, mt, sl, sw, sa, sg, tl, tw, ta, tg
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.karts <- karts %>%
mutate(body = case_when(
body == "BiddybuggyBuggybud" ~ "Biddybuggy",
body == "SneekerBounder" ~ "Sneeker",
body == "Sports CoupeSports Coupé" ~ "Sports Coupe",
body == "Gold StandardGold Kart" ~ "Gold Standard",
body == "Mr. ScootyMr Scooty" ~ "Mr. Scooty",
body == "Standard ATVStandard Quad" ~ "Standard ATV",
TRUE ~ body
)) %>%
drop_na()
characters <- characters %>%
mutate(driver = str_sub(driver,1,nchar(driver)-3)) %>%
mutate(driver = gsub("([a-z])([A-Z])","\\1 \\2",driver)) %>%
separate(driver, into = c("keep1", "keep2", "drop1", "drop2")) %>%
mutate(drop1 = if_else(nchar(drop1) < 2, NA_character_, drop1),
keep2 = if_else(nchar(keep2) < 2, NA_character_, keep2)) %>%
replace_na(list(keep1 = "", keep2 = "", drop1 = "")) %>%
mutate(driver = str_c(keep1, keep2, drop1, sep = " ")) %>%
select(-c(keep1, keep2, drop1, drop2)) %>%
drop_na() %>%
relocate(driver) %>%
mutate(driver = str_trim(driver))## Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [44].## Warning: Expected 4 pieces. Missing pieces filled with `NA` in 42 rows [1, 2, 3,
## 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, ...].tires <- tires %>%
mutate(tire = case_when(
tire == "StandardNormal" ~ "Standard",
tire == "WoodWooden" ~ "Wood",
tire == "Blue StandardNormal Blue" ~ "Blue Standard",
tire == "Hot MonsterFunky Monster" ~ "Hot Monster",
tire == "Gold TiresGold Wheels" ~ "Gold Tires",
tire == "GLA TiresGLA Wheels" ~ "GLA Tires",
tire == "Triforce TiresTriforce Tyres" ~ "Triforce Tires",
tire == "Leaf TiresLeaf Tyres" ~ "Leaf Tires",
TRUE ~ tire
)) %>%
drop_na()kart_dupes <- karts %>%
mutate(key = as.character(paste0(wg, ac, on, of, mt, sl, sw, sa, sg, tl, tw, ta, tg)))
kart_out <- kart_dupes %>%
mutate(bodies = NA_character_) %>%
slice(0)
keys <- unique(kart_dupes$key)
for(kart_key in keys){
#print(kart_key)
new_dupes <- kart_dupes %>%
filter(key == kart_key)
bodies <- new_dupes %>%
select(1)
list <- as.list(bodies[[1]])
y <- toString(list)
z <- new_dupes %>%
mutate(bodies = y)
kart_out <- bind_rows(kart_out, z)
}
kart_output <- kart_out %>%
select(-c(body, key)) %>%
distinct() %>%
relocate(bodies) %>%
setNames(paste0("karts.", names(kart_out)))tire_dupes <- tires %>%
mutate(key = as.character(paste0(wg, ac, on, of, mt, sl, sw, sa, sg, tl, tw, ta, tg)))
tire_out <- tire_dupes %>%
mutate(tires = NA_character_) %>%
slice(0)
keys <- unique(tire_dupes$key)
for(tire_key in keys){
#print(kart_key)
new_dupes <- tire_dupes %>%
filter(key == tire_key)
wheels <- new_dupes %>%
select(1)
list <- as.list(wheels[[1]])
y <- toString(list)
z <- new_dupes %>%
mutate(tires = y)
tire_out <- bind_rows(tire_out, z)
}
tire_output <- tire_out %>%
select(-c(tire, key)) %>%
distinct() %>%
relocate(tires) %>%
setNames(paste0("tires.", names(tire_out)))char_dupes <- characters %>%
mutate(key = as.character(paste0(wg, ac, on, of, mt, sl, sw, sa, sg, tl, tw, ta, tg)))
char_out <- char_dupes %>%
mutate(drivers = NA_character_) %>%
slice(0)
keys <- unique(char_dupes$key)
for(char_key in keys){
#print(kart_key)
new_dupes <- char_dupes %>%
filter(key == char_key)
chars <- new_dupes %>%
select(1)
list <- as.list(chars[[1]])
y <- toString(list)
z <- new_dupes %>%
mutate(drivers = y)
char_out <- bind_rows(char_out, z)
}
char_output <- char_out %>%
select(-c(driver, key)) %>%
relocate(drivers) %>%
distinct() %>%
setNames(paste0("char.", names(char_out)))glider_dupes <- gliders %>%
mutate(key = as.character(paste0(wg, ac, on, of, mt, sl, sw, sa, sg, tl, tw, ta, tg)))
glider_out <- glider_dupes %>%
mutate(glider = NA_character_) %>%
slice(0)
keys <- unique(glider_dupes$key)
for(glider_key in keys){
#print(kart_key)
new_dupes <- glider_dupes %>%
filter(key == glider_key)
glides <- new_dupes %>%
select(1)
list <- as.list(glides[[1]])
y <- toString(list)
z <- new_dupes %>%
mutate(gliders = y)
glider_out <- bind_rows(glider_out, z)
}
glider_output <- glider_out %>%
select(-c(glider, key)) %>%
distinct() %>%
relocate(gliders) %>%
setNames(paste0("glider.", names(glider_out)))characters_karts <- crossing(char_output, kart_output, .name_repair = 'unique') %>%
drop_na()
ck_tires <- crossing(characters_karts, tire_output, .name_repair = 'unique') %>%
drop_na()
all <- crossing(ck_tires, glider_output, .name_repair = 'unique') %>%
drop_na()all_temp <- all %>%
mutate(raw_wg = (all %>% select(ends_with("wg")) %>% rowSums()),
raw_ac = (all %>% select(ends_with("ac")) %>% rowSums()),
raw_on = (all %>% select(ends_with("on")) %>% rowSums()),
raw_of = (all %>% select(ends_with("of")) %>% rowSums()),
raw_mt = (all %>% select(ends_with("mt")) %>% rowSums()),
raw_sl = (all %>% select(ends_with("sl")) %>% rowSums()),
raw_sw = (all %>% select(ends_with("sw")) %>% rowSums()),
raw_sa = (all %>% select(ends_with("sa")) %>% rowSums()),
raw_sg = (all %>% select(ends_with("sg")) %>% rowSums()),
raw_tl = (all %>% select(ends_with("tl")) %>% rowSums()),
raw_tw = (all %>% select(ends_with("tw")) %>% rowSums()),
raw_ta = (all %>% select(ends_with("ta")) %>% rowSums()),
raw_tg = (all %>% select(ends_with("tg")) %>% rowSums())
) %>%
mutate(
Weight = (raw_wg + 3) / 4,
Acceleration = (raw_ac +3) /4,
"On-Road Traction" = (raw_on + 3)/4,
"Off-Road Traction" = (raw_of + 3)/4,
"Mini Turbo" = (raw_mt +3) /4,
"Ground Speed" = (raw_sl +3)/4,
"Water Speed" = (raw_sw +3) /4,
"Anti-Gravity Speed" = (raw_sa+3)/4,
"Air Speed" = (raw_sg+3)/4,
"Ground Handling" = (raw_tl+3) /4,
"Water Handling" = (raw_tw+3)/4,
"Anti-Gravity Handling" = (raw_ta+3)/4,
"Air Handling" = (raw_tg +3)/4
)
all_stats <- all_temp %>%
select(Driver = char.driver, Kart = karts.body, Tires = tires.tire, Glider = glider.glider, Weight:`Air Handling`) %>%
mutate(key = as.character(paste0(Weight, Acceleration, `On-Road Traction`, `Off-Road Traction`, `Mini Turbo`, `Ground Speed`, `Water Speed`, `Anti-Gravity Speed`, `Air Speed`, `Ground Speed`, `Water Handling`, `Anti-Gravity Handling`, `Air Handling`)))long_stats <- all_stats %>%
pivot_longer(cols = "Weight":"Air Handling", names_to = "stat", values_to = 'value')write_csv(all_stats, "all_stats_wide.csv")
write_csv(long_stats, "all_stats_long.csv")