There is a popular video game captivating folks at home these days called “Animal Crossing”. When I first heard this, I imagined animals crossing a road to get to the other side, like the old game Frog Xing or Frogger. Alas, the game has nothing to do with crossing busy traffic roads!

“Animal Crossing” is a Japanese game whose original title (Doubutsu No Mori どうぶつの森) translates to something more like “Animal Forest”. From what I gather, it’s a SIMs-like game where you build up an island, interacting with both virtual (in-game) and real characters (actual people). My friend was totally unfazed when I showed her a picture of fish whose face only a mother could love (aka a Sculpin) because she’d grown accustomed to catching valuable tarantulas by night and selling them by day for the game. A rather strange-sounding way to make money…

Every Tuesday, some wonderful people share an interesting data set that becomes a source of data analysis. It’s a great way to practice “data wrangling” – that is, filtering, cleaning, and taking care of problematic data in a quest to tame it, understand it, and find some interesting insights. It’s called #TidyTuesday and comes with a fresh dataset, background information, and article that makes use of the data. This past Tuesday the data just so happened to pertain to Animal Crossing!

The “Tidy” aspect of “TidyTuesday” refers to ‘cleaning’ or ‘tidying’ the data. In R Programming, there’s a package called the “Tidyverse” that comes with many tools and enhanced functions great for tidying data. Why on Tuesday? I’m not sure, but it does add a spark of excitement to this day of the week for me. #TidyFriday or #TidyThursday wouldn’t sound bad, either.

This week’s Animal Crossing data included stats on all of the villagers (computer characters) on the island and items that can be bought/sold. There’s a plethora of information on each, like a villager’s personality type, birthday, and theme song! I decided to practice my R data wrangling skills while answering curiosities that popped into my mind as I examined this data.

(Check out my github site for the full analysis & R code, which I’ll update every Tuesday as I’m able!)

Items Analysis

# Analysis 1: Which items can be bought with ‘miles’ and then sold for bells?
# Answer: 19 “Nook Inc” items from the Nook Miles System. Not the e-reader Nook, from the Tanuki character Tom Nook.

items_miles % filter(buy_currency == "miles" & sell_currency == "bells")
view(items_miles)

# Analysis 2: Which ‘items’ give the highest profit (sell minus buy value)?
# Answer: None. All items have a sell_value < buy_value.
I guess you can’t sell things for more than you bought them for…

# Analysis 3: Which items have a greatest difference between buy and sell value?
# Answer: Royal Crown, Crown, Gold Armor, Golden Casket (?!?), Grand Piano

items_bells % filter(buy_currency == "bells" & sell_currency == "bells")
items_bells %>% filter(sell_value > buy_value)
# Create value difference column and add to items_bells table
value_dif <- items_bells$buy_value - items_bells$sell_value items_bells$value_dif % top_n(wt = value_dif, n = 5) # Done!! Expand Console width to view last col. The trick was to create the value_dif in a new column and add it to the table. top_n(items_bells %>% filter(buy_value > sell_value), n = 5)

My question is…is a Golden Casket really what I think it is? Who would use this?? (I can only think of one person, who is rumored to have a golden toilet).

…oh! It’s real! (The casket is in the lower left corner, I believe.)

# Analysis 4: Which category of items is the most expensive?
# Furniture, Hats, Bugs
# Cheapest: Flowers, Fruit, Photos, Socks, Tools

items_bells %>%
group_by(category) %>%
dplyr::summarize_at(vars(buy_value, sell_value), funs(mean(., na.rm = TRUE)))

Villagers Analysis

# Analysis 5: Are there more male or female characters?
# Answer: 187 Females, 204 males! Slightly more males!
# count() does group_by() + tally() https://dplyr.tidyverse.org/reference/tally.html
villagers %>%
count(gender)

# Analysis 6: Which Personality Types are the most common?
# Answer: Lazy, Normal, Cranky/Jock/Snooty, Peppy

# Analysis 6b:Are there any personality types with only 1 character?
# Nope! But the least common type is ‘uchi’ (a translation of “sisterly/big sister” in Japanese).

villagers %>%
count(personality) %>%
arrange(desc(n))

To be honest…Agnes the black pig scares me…

# Analysis 7: Who has their own song?
# Four special villagers have their own song: Angus, Diva, Gwen, and Zell.

villagers %>%
add_count(song) %>%
filter(n == 1)

# Analysis 8: Who has a birthday today (5/6)?
# Answer: Tank the Rhino! He looks cuter than I imagined from his name.

villagers %>%
filter(birthday == "5-6")