2020_08_18_extinct_plants.Rmd

---
title: "TidyTemplate"
date: 2020-08-18
output: html_output
---

# TidyTuesday

Join the R4DS Online Learning Community in the weekly #TidyTuesday event!
Every week we post a raw dataset, a chart or article related to that dataset, and ask you to explore the data.
While the dataset will be “tamed”, it will not always be tidy! As such you might need to apply various R for Data Science techniques to wrangle the data into a true tidy format.
The goal of TidyTuesday is to apply your R skills, get feedback, explore other’s work, and connect with the greater #RStats community!
As such we encourage everyone of all skills to participate!

```{r setup, include=FALSE}

knitr::opts_chunk$set(echo = TRUE)

library(tidyverse)
library(tidytuesdayR)
library(scales)
library(tidytext)
theme_set(theme_light())

```

# Load the weekly Data

Dowload the weekly data and make available in the `tt` object.

```{r Load}

tt <- tt_load("2020-08-18")

```


# Readme

Take a look at the readme for the weekly data to get insight on the dataset.
This includes a data dictionary, source, and a link to an article on the data.

```{r Readme, eval = interactive()}

tt

plants <- tt$plants %>%
  mutate(year_last_seen = fct_relevel(year_last_seen, "Before 1900")) %>%
  separate(binomial_name, c("genus", "species"), sep = " ", remove = FALSE)

threats <- tt$threats %>%
  filter(threatened == 1) %>%
  select(-threatened) %>%
  mutate(year_last_seen = fct_relevel(year_last_seen, "Before 1900")) %>%
  separate(binomial_name, c("genus", "species"), sep = " ", remove = FALSE)

actions <- tt$actions %>%
  filter(action_taken == 1) %>%
  select(-action_taken) %>%
  mutate(year_last_seen = fct_relevel(year_last_seen, "Before 1900")) %>%
  separate(binomial_name, c("genus", "species"), sep = " ", remove = FALSE) %>%
  filter(action_type != "Unknown")
```

```{r}
plants %>%
  View()

plants %>%
  count(country = fct_lump(country, 10), sort = TRUE) %>%
  mutate(country = fct_reorder(country, n)) %>%
  ggplot(aes(n, country)) +
  geom_col()

plants %>%
  count(continent, sort = TRUE) %>%
  mutate(continent = fct_reorder(continent, n)) %>%
  ggplot(aes(n, continent)) +
  geom_col()

plants %>%
  count(red_list_category)

plants %>%
  count(group, sort = TRUE)

plants %>%
  filter(!is.na(year_last_seen)) %>%
  count(year_last_seen, continent) %>%
  ggplot(aes(year_last_seen, n, fill = continent)) +
  geom_col()

plants

by_continent_threat <- threats %>%
  count(threat_type, continent, sort = TRUE) %>%
  mutate(threat_type = fct_reorder(threat_type, n, sum))

by_continent_threat %>%
  ggplot(aes(n, threat_type, fill = continent)) +
  geom_col() +
  labs(x = "# of plants facing this threat",
       y = "",
       fill = "Continent",
       title = "What are the most common threats to plants?")

by_continent_threat %>%
  mutate(threat_type = reorder_within(threat_type, n, continent)) %>%
  ggplot(aes(n, threat_type)) +
  geom_col() +
  scale_y_reordered() +
  facet_wrap(~ continent, scales = "free") +
  labs(x = "# of plants facing this threat",
       y = "",
       fill = "Continent",
       title = "What are the most common threats to plants by continent?")

threats %>%
  filter(!is.na(year_last_seen)) %>%
  count(year_last_seen, threat_type, continent) %>%
  # filter(fct_lump(threat_type, 9, w = n) != "Other") %>%
  mutate(threat_type = fct_reorder(threat_type, -n, sum)) %>%
  ggplot(aes(year_last_seen, n, fill = continent)) +
  geom_col() +
  facet_wrap(~ threat_type) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  labs(x = "Last seen",
       y = "# of plants extinct with this threat")
```

Species

```{r}
plants %>%
  select(binomial_name) %>%
  separate(binomial_name, c("genus", "species"), sep = " ") %>%
  count(genus, sort = TRUE)

plants %>%
  count(genus = fct_lump(genus, 9), year_last_seen) %>%
  filter(genus != "Other") %>%
  ggplot(aes(year_last_seen, n, fill = genus)) +
  geom_col() +
  facet_wrap(~ genus)
```

Interesting things I learned: Brugmansia is fully extinct in the wild.

```{r}
plants %>%
  filter(red_list_category == "Extinct in the Wild") %>%
  count(genus, sort = TRUE)

plants %>%
  group_by(genus) %>%
  summarize(extinct_in_the_wild = mean(red_list_category == "Extinct in the Wild"),
            n = n()) %>%
  arrange(desc(n))
```

```{r}
actions %>%
  filter(action_type != "Unknown") %>%
  count(action_type, sort = TRUE)

actions %>%
  count(binomial_name, sort = TRUE) %>%
  View()

plants %>%
  count(continent, group, sort = TRUE) %>%
  ggplot(aes(continent, n)) +
  geom_col() +
  facet_wrap(~ group, scales = "free_y") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))

actions %>%
  distinct(binomial_name, genus) %>%
  count(genus, sort = TRUE)
```

```{r}
library(rvest)

links <- read_html("http://www.orchidspecies.com/indexbulb.htm") %>%
  html_nodes("li a")

link_text <- html_text(links)

bulbophyllum_links <- tibble(text = html_text(links),
       link = paste0("http://www.orchidspecies.com/", html_attr(links, "href"))) %>%
  mutate(link_text = str_trim(link_text)) %>%
  separate(link_text, c("genus", "species", "citation"), sep = " ", extra = "merge") %>%
  filter(!is.na(citation))

plants %>%
  inner_join(bulbophyllum_links, by = c("genus", "species")) %>%
  select(binomial_name, country, continent, link) %>%
  mutate(html = map(link, read_html)) %>%
  mutate(image = map_chr(html, ~ html_attr(html_node(., "a img"), "src"))) %>%
  transmute(binomial_name, image = paste0("http://www.orchidspecies.com/", image))
```




Threat classification scheme: https://www.iucnredlist.org/resources/threat-classification-scheme