wnba-exercise-answers.Rmd

---
title: "R Notebook"
output: 
---

```{r}
library(tidyverse)
```

```{r}
wnba_salaries <- read_csv("data/wnba_salaries.csv")
wnba_teams <- read_csv("data/wnba_teams.csv")
```

Answers to the following questions from tidyverse-joins.Rmd:

1.  Who was the tallest player in the WNBA in 2023?

```{r}
wnba_teams %>% arrange(desc(height_in))
```

2.  Which team has the most players shooting better than 50%?

```{r}
wnba_salaries %>% 
  inner_join(wnba_teams, by = c("name"="player_name")) %>% 
  filter(field_goal_pct > .5) %>% 
  count(team) %>% 
  arrange(desc(n))
```

3.  Which college has produced the most current WNBA players?

```{r}
wnba_salaries %>% 
  inner_join(wnba_teams, by = c("name"="player_name")) %>% 
  count(college) %>% 
  arrange(desc(n))
```

4.  Which college has produced players with the highest average salary?

```{r}
wnba_salaries %>% 
  inner_join(wnba_teams, by = c("name"="player_name")) %>% 
  group_by(college) %>% 
  summarise(avg_sal = mean(contract_amt, na.rm=T)) %>% 
  arrange(desc(avg_sal))
```

5.  What percent of WNBA players are not from the US?

```{r}
wnba_teams %>% 
  filter(country != "USA") %>% 
  count()/162
```

6.  How many players from the 2023 draft started the majority of their games?

```{r}
wnba_salaries %>% 
  inner_join(wnba_teams, by = c("name"="player_name")) %>% 
  filter(draft_year==2023) %>% 
  mutate(pct_start = games_start/games) %>% 
  select(name, team, games, pct_start) %>% 
  arrange(desc(pct_start))
```