Animal Crossing

What was your dataset?

Load your dataset in with the function below. The input is the date the dataset was issued. You should be able to get this from the tt_available() function.

critic <- readr::read_tsv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-05-05/critic.tsv')

Rows: 107 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr  (2): publication, text
dbl  (1): grade
date (1): date

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.

user_reviews <- readr::read_tsv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-05-05/user_reviews.tsv')

Rows: 2999 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr  (2): user_name, text
dbl  (1): grade
date (1): date

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.

items <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-05-05/items.csv')

Warning: One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)

Rows: 4565 Columns: 16
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (10): id, name, category, sell_currency, buy_currency, sources, recipe_i...
dbl  (4): num_id, sell_value, buy_value, recipe
lgl  (2): orderable, customizable

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.

villagers <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-05-05/villagers.csv')

Rows: 391 Columns: 11
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (10): id, name, gender, species, birthday, personality, song, phrase, fu...
dbl  (1): row_n

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.

Villagers

skimr::skim(villagers)

Data summary

Name	villagers
Number of rows	391
Number of columns	11
_______________________
Column type frequency:
character	10
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
id	0	1.00	2	8	0	391	0
name	0	1.00	2	8	0	391	0
gender	0	1.00	4	6	0	2	0
species	0	1.00	3	9	0	35	0
birthday	0	1.00	3	5	0	361	0
personality	0	1.00	4	6	0	8	0
song	11	0.97	7	16	0	92	0
phrase	0	1.00	2	10	0	388	0
full_id	0	1.00	11	17	0	391	0
url	0	1.00	60	66	0	391	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
row_n	0	1	239.9	140.7	2	117.5	240	363.5	483	▇▇▇▇▇

Personalities by Species

species_count <- villagers %>%
  group_by(species)  %>%
  summarize(species_count = n()) %>%
  arrange(species_count)

datatable(species_count)

level_order <- villagers %>%
  group_by(species) %>% count() %>%
  arrange(desc(n)) %>%
  pull(species)

villagers %>%
  mutate(species=factor(species, levels=level_order)) %>%
  ggplot() + aes(x=species, y=personality, color=personality) %>%
  geom_count() + 
   theme_light() + theme(legend.position = "none") +
  theme(axis.text.x = element_text(angle = 90)) 

villagers %>% select(name, species, personality, url) %>%
  mutate(combo = paste(species, personality)) %>% select(name, combo, url) -> villager_index

unique_combos <- villagers %>%
  group_by(species, personality) %>% summarize(n=n()) %>%
  filter(n == 1) %>% mutate(combo=paste(species, personality)) %>%
  inner_join(y=villager_index, by=c("combo")) %>% ungroup()

`summarise()` has grouped output by 'species'. You can override using the
`.groups` argument.

out_image <- unique_combos %>%
  ggplot() + aes(x=species, y=personality, image=url, name=name) +
  geom_count() +
  geom_raster(fill="white", color="black") +
  geom_image(asp=1.2, size=0.03) + 
   theme_minimal() + theme(legend.position = "none") +
  theme(axis.text.x = element_text(angle = 90)) + labs(title="There can be only one", subtitle = "Unique Personality/Species combos in Animal Crossing")

Warning in geom_raster(fill = "white", color = "black"): Ignoring unknown
parameters: `colour`

out_image

ggsave(plot=out_image, filename = "unique_animal_personalities.pdf", width=10, height = 5)

pers_vil <- villagers %>% 
  group_by(personality, species) %>%
  summarize(count=n()) %>%
  #filter(count==1) %>%
  arrange(species) 

`summarise()` has grouped output by 'personality'. You can override using the
`.groups` argument.

pers_vil %>%
  arrange(desc(count)) %>%
  datatable()

Items

skimr::skim(items)

Data summary

Name	items
Number of rows	4565
Number of columns	16
_______________________
Column type frequency:
character	10
logical	2
numeric	4
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
id	0	1.00	3	29	0	4200	0
name	0	1.00	3	29	0	4200	0
category	0	1.00	4	11	0	21	0
sell_currency	36	0.99	5	5	0	1	0
buy_currency	1014	0.78	5	5	0	2	0
sources	3663	0.20	3	71	0	125	0
recipe_id	3977	0.13	4	20	0	102	0
games_id	0	1.00	2	2	0	1	0
id_full	1528	0.67	8	34	0	2704	0
image_url	1528	0.67	56	82	0	2672	0

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
orderable	2775	0.39	0.45	FAL: 976, TRU: 814
customizable	3992	0.13	0.45	FAL: 316, TRU: 257

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
num_id	0	1.00	3661.62	2187.95	12	1722	3569	5607	7443	▇▇▇▇▇
sell_value	36	0.99	2261.38	11313.23	5	240	390	1000	300000	▇▁▁▁▁
buy_value	1014	0.78	6959.65	34326.09	40	870	1300	2700	1200000	▇▁▁▁▁
recipe	3977	0.13	4.71	6.49	1	2	3	6	90	▇▁▁▁▁

library(tidyverse)
items %>% ggplot() +
  aes(x=category, y=buy_value) +
  geom_boxplot() +
  ylim(c(0,75000)) +
  theme(axis.text.x  = element_text(angle = 90))

Warning: Removed 1094 rows containing non-finite outside the scale range
(`stat_boxplot()`).

Most Expensive Items

This was the original code in my tweet:

library(gt)

 items %>% 
  top_n(10, buy_value) %>%
  arrange(desc(buy_value)) %>%
  select(name, sell_value, buy_value, category, image=image_url) %>%
  gt() %>%
   text_transform(
    locations = cells_body(vars(image)),
    fn = function(x) {
      web_image(
        url = x,
        height = 50
      )
    }
  )

Warning: Since gt v0.3.0, `columns = vars(...)` has been deprecated.
• Please use `columns = c(...)` instead.

name	sell_value	buy_value	category	image
Royal Crown	300000	1200000	Hats
Crown	250000	1000000	Hats
Gold Armor	80000	320000	Dresses
Golden Casket	80000	320000	Furniture
Grand Piano	65000	260000	Furniture
Golden Toilet	60000	240000	Furniture
Blue Steel Staircase	NA	228000	Furniture
Iron Bridge	NA	228000	Furniture
Red Steel Staircase	NA	228000	Furniture
Red Zen Bridge	NA	228000	Furniture
Zen Bridge	NA	228000	Furniture

Here’s a function.

most_expensive <- function(category_name=NULL, price_category=buy_value){
  
  if(!is.null(category_name)){
    items <- items %>%
      filter(category == category_name)
  }
  
  items %>% 
  top_n(10, {{price_category}}) %>%
  arrange(desc({{price_category}})) %>%
  select(name, sell_value, buy_value, category, image=image_url) %>%
  gt() %>%
   text_transform(
    locations = cells_body(vars(image)),
    fn = function(x) {
      web_image(
        url = x,
        height = 50
      )
    }
  )
  
}

Reproducing the above table

most_expensive()

Warning: Since gt v0.3.0, `columns = vars(...)` has been deprecated.
• Please use `columns = c(...)` instead.

name	sell_value	buy_value	category	image
Royal Crown	300000	1200000	Hats
Crown	250000	1000000	Hats
Gold Armor	80000	320000	Dresses
Golden Casket	80000	320000	Furniture
Grand Piano	65000	260000	Furniture
Golden Toilet	60000	240000	Furniture
Blue Steel Staircase	NA	228000	Furniture
Iron Bridge	NA	228000	Furniture
Red Steel Staircase	NA	228000	Furniture
Red Zen Bridge	NA	228000	Furniture
Zen Bridge	NA	228000	Furniture

Most Expensive Furniture

most_expensive("Furniture")

Warning: Since gt v0.3.0, `columns = vars(...)` has been deprecated.
• Please use `columns = c(...)` instead.

name	sell_value	buy_value	category	image
Golden Casket	80000	320000	Furniture
Grand Piano	65000	260000	Furniture
Golden Toilet	60000	240000	Furniture
Blue Steel Staircase	NA	228000	Furniture
Iron Bridge	NA	228000	Furniture
Red Steel Staircase	NA	228000	Furniture
Red Zen Bridge	NA	228000	Furniture
Zen Bridge	NA	228000	Furniture
Elaborate Kimono Stand	55000	220000	Furniture
Golden Seat	50000	200000	Furniture

Most Expensive Hats

library(gt)
most_expensive("Hats")

Warning: Since gt v0.3.0, `columns = vars(...)` has been deprecated.
• Please use `columns = c(...)` instead.

name	sell_value	buy_value	category	image
Royal Crown	300000	1200000	Hats
Crown	250000	1000000	Hats
Gold Helmet	50000	200000	Hats
Blue Rose Crown	12000	48000	Hats
Gold Rose Crown	12000	48000	Hats
Snowperson Head	7000	28000	Hats
Knight's Helmet	3750	15000	Hats
Dark Cosmos Crown	3360	13440	Hats
Chic Rose Crown	2880	11520	Hats
Purple Hyacinth Crown	2880	11520	Hats
Purple Pansy Crown	2880	11520	Hats
Purple Windflower Crown	2880	11520	Hats
Simple Mum Crown	2880	11520	Hats

Most Expensive Fossils

most_expensive("Fossils", sell_value)

Warning: Since gt v0.3.0, `columns = vars(...)` has been deprecated.
• Please use `columns = c(...)` instead.

name	sell_value	buy_value	category	image
Brachio Skull	6000	NA	Fossils
T. Rex Skull	6000	NA	Fossils
Brachio Chest	5500	NA	Fossils
Brachio Tail	5500	NA	Fossils
Dimetrodon Skull	5500	NA	Fossils
Right Megalo Side	5500	NA	Fossils
T. Rex Torso	5500	NA	Fossils
Tricera Skull	5500	NA	Fossils
Brachio Pelvis	5000	NA	Fossils
Dimetrodon Torso	5000	NA	Fossils
Diplo Skull	5000	NA	Fossils
Diplo Tail	5000	NA	Fossils
Left Quetzal Wing	5000	NA	Fossils
Right Quetzal Wing	5000	NA	Fossils
Stego Skull	5000	NA	Fossils
T. Rex Tail	5000	NA	Fossils
Tricera Torso	5000	NA	Fossils

Priceless Items by Category

items %>%
  filter(is.na(buy_value)) %>%
  ggplot(aes(x=category)) + geom_bar() +
  theme(axis.text.x = element_text(angle=90))

library(ggalluvial)

pers_vil %>% filter(species %in% c("cat", "rabbit", "dog")) %>%
ggplot(
       aes(y = count,
           axis1 = personality, axis2 = species)) +
           geom_alluvium(aes(fill = count),
                width = 0, knot.pos = 0, reverse = FALSE) +
  guides(fill = FALSE) +
  geom_stratum(width = 1/8, reverse = FALSE) +
    geom_text(stat = "stratum", infer.label = TRUE, reverse = FALSE) 

Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
of ggplot2 3.3.4.

Warning in deprecate_parameter("infer.label", msg = "Use `aes(label = after_stat(stratum))`."): The parameter `infer.label` is deprecated.
Use `aes(label = after_stat(stratum))`.