Tidy Tuesday featuring Animal Crossing New Horizons data: https://github.com/rfordatascience/tidytuesday/tree/master/data/2020/2020-05-05
library(here)
library(tidyverse)
theme_set(theme_classic())items <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-05-05/items.csv')
#> Parsed with column specification:
#> cols(
#> num_id = col_double(),
#> id = col_character(),
#> name = col_character(),
#> category = col_character(),
#> orderable = col_logical(),
#> sell_value = col_double(),
#> sell_currency = col_character(),
#> buy_value = col_double(),
#> buy_currency = col_character(),
#> sources = col_character(),
#> customizable = col_logical(),
#> recipe = col_double(),
#> recipe_id = col_character(),
#> games_id = col_character(),
#> id_full = col_character(),
#> image_url = col_character()
#> )
#> Warning: 2 parsing failures.
#> row col expected actual file
#> 4472 customizable 1/0/T/F/TRUE/FALSE Yes 'https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-05-05/items.csv'
#> 4473 customizable 1/0/T/F/TRUE/FALSE Yes 'https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-05-05/items.csv'
head(items)
#> # A tibble: 6 x 16
#> num_id id name category orderable sell_value sell_currency buy_value buy_currency sources
#> <dbl> <chr> <chr> <chr> <lgl> <dbl> <chr> <dbl> <chr> <chr>
#> 1 12 3d-g… 3D G… Accesso… NA 122 bells 490 bells <NA>
#> 2 14 a-tee A Tee Tops NA 140 bells 560 bells <NA>
#> 3 17 abst… Abst… Wallpap… TRUE 390 bells 1560 bells Nook's…
#> 4 19 acad… Acad… Dresses NA 520 bells 2080 bells <NA>
#> 5 20 acan… Acan… Fossils FALSE 2000 bells NA <NA> Underg…
#> 6 21 acce… Acce… Furnitu… TRUE 375 bells 1500 bells Nook's…
#> # … with 6 more variables: customizable <lgl>, recipe <dbl>, recipe_id <chr>, games_id <chr>,
#> # id_full <chr>, image_url <chr>
villagers <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-05-05/villagers.csv')
#> Parsed with column specification:
#> cols(
#> row_n = col_double(),
#> id = col_character(),
#> name = col_character(),
#> gender = col_character(),
#> species = col_character(),
#> birthday = col_character(),
#> personality = col_character(),
#> song = col_character(),
#> phrase = col_character(),
#> full_id = col_character(),
#> url = col_character()
#> )
head(villagers)
#> # A tibble: 6 x 11
#> row_n id name gender species birthday personality song phrase full_id url
#> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 2 admir… Admir… male bird 1-27 cranky Steep … aye a… villager… https://villagerdb.…
#> 2 3 agent… Agent… female squirrel 7-2 peppy DJ K.K. sidek… villager… https://villagerdb.…
#> 3 4 agnes Agnes female pig 4-21 uchi K.K. H… snuff… villager… https://villagerdb.…
#> 4 6 al Al male gorilla 10-18 lazy Steep … Ayyee… villager… https://villagerdb.…
#> 5 7 alfon… Alfon… male alligat… 6-9 lazy Forest… it'sa… villager… https://villagerdb.…
#> 6 8 alice Alice female koala 8-19 normal Surfin… guvnor villager… https://villagerdb.…nature_categories <- c("Bugs", "Fish", "Fossils")
items_nature <- items %>%
filter(category %in% nature_categories)
head(items_nature)
#> # A tibble: 6 x 16
#> num_id id name category orderable sell_value sell_currency buy_value buy_currency sources
#> <dbl> <chr> <chr> <chr> <lgl> <dbl> <chr> <dbl> <chr> <chr>
#> 1 20 acan… Acan… Fossils FALSE 2000 bells NA <NA> Underg…
#> 2 46 agri… Agri… Bugs FALSE 3000 bells 12000 bells <NA>
#> 3 86 amber Amber Fossils FALSE 1200 bells NA <NA> Underg…
#> 4 93 ammo… Ammo… Fossils FALSE 1100 bells NA <NA> Underg…
#> 5 102 anch… Anch… Fish FALSE 200 bells NA <NA> North:…
#> 6 102 anch… Anch… Fish FALSE 200 bells NA <NA> South:…
#> # … with 6 more variables: customizable <lgl>, recipe <dbl>, recipe_id <chr>, games_id <chr>,
#> # id_full <chr>, image_url <chr>items_nature %>%
ggplot(aes(x=category, y=sell_value, color=category)) +
geom_jitter()
items_nature %>%
ggplot(aes(x=sell_value, fill=category)) +
geom_histogram()
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
items_profit <- items %>%
mutate(net_profit = sell_value - buy_value) %>%
select(name, category, sell_value, buy_value, net_profit)
items_profit %>%
filter(net_profit > 0) %>%
arrange(desc(net_profit))
#> # A tibble: 15 x 5
#> name category sell_value buy_value net_profit
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 Nook Inc. Flooring Flooring 6000 1200 4800
#> 2 Nook Inc. Wall Wallpaper 6000 1200 4800
#> 3 Nook Inc. Botanical Rug Flooring 5000 1000 4000
#> 4 Nook Inc. Rug Flooring 5000 1000 4000
#> 5 Nook Inc. Blouson Tops 4000 800 3200
#> 6 Nook Inc. Knapsack Accessories 4000 800 3200
#> 7 Nook Inc. Umbrella Umbrellas 3500 700 2800
#> 8 Nook Inc. Aloha Shirt Tops 3000 600 2400
#> 9 Nook Inc. Tee Tops 3000 600 2400
#> 10 Nook Inc. Bandanna Hats 2500 500 2000
#> 11 Nook Inc. Cap Hats 2500 500 2000
#> 12 Nook Inc. Eye Mask Accessories 2000 400 1600
#> 13 Nook Inc. Slippers Shoes 2000 400 1600
#> 14 Nook Inc. Socks Socks 2000 400 1600
#> 15 Nook Inc. Uchiwa Fan Tools 2000 400 1600