animal_crossing.Rmd

---
title: "Animal Crossing"
author: "Ted Laderas"
date: "`r Sys.Date()`"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
library(DT)
library(ggimage)
```


# 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.

```{r}
critic <- readr::read_tsv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-05-05/critic.tsv')
user_reviews <- readr::read_tsv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-05-05/user_reviews.tsv')
items <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-05-05/items.csv')
villagers <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-05-05/villagers.csv')

```

# Villagers

```{r}
skimr::skim(villagers)
```

## Personalities by Species

```{r fig.width=10, fig.height=3}
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)) 
```


```{r}


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()

out_image <- unique_combos %>%
  mutate(species=factor(species, levels=level_order)) %>%
  ggplot() + aes(x=species, y=personality, image=url, name=name) +
  geom_count() +
  geom_raster(fill="white", color="black") +
  geom_image(asp=1.5, size=0.025) + 
   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")

out_image

ggsave(plot=out_image, filename = "unique_animal_personalities.png", width=10, height = 5, dpi = 150)
```

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

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

# Items

```{r}
skimr::skim(items)
```

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

# Most Expensive Items

```{r}
library(gt)

most_expensive <- function(category_name=NULL){
  
  if(!is.null(category_name)){
    items <- items %>%
      filter(category == category_name)
  }
  
  items %>% 
  top_n(10, sell_value) %>%
  arrange(desc(sell_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
      )
    }
  )
  
}


most_expensive("Hats")
```



```{r}
most_expensive()
```

# Most Expensive Furniture

```{r}
most_expensive("Furniture")
```


# Most Expensive Hats

```{r}
library(gt)
most_expensive("Hats")
```

```{r}
most_expensive("Flooring")

```


```{r}
most_expensive("Fossils")
```

# Priceless Items by Category

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


# What was your question?

Given your inital exploration of the data, what was the question you wanted to answer?

# What were your findings?

Put your findings and your visualization code here.

```{r}
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) 
```

# What did you learn?

Were there any lessons you learned? Any cool packages you want to talk about?