01_inclass_solutions.qmd

---
title: "Learn R Part II In-class Exercises"
format: html
editor: visual
---

Welcome to Quarto! This is where you will try out all of the hands-on exercises in the workshop. Begin by running these first two code chunks:

```{r}
# load packages
library(tidyverse)
```

```{r}
# load data
tswift <- read_csv("data/taylor_swift_spotify.csv")
nat_parks <- read_csv("data/nat_parks_visitors.csv")
```

## 00. Quick review

Using an `if_else()` statement, create a new variable in `tswift` that indicates if a song is "long" or "short". Name the variable `long_short`.

Songs are considered "long" if `duration_ms` is greater than 250000.

```{r}
# create long_short variable
tswift <- tswift |> 
  mutate(long_short = if_else(duration_ms > 250000,
                              "long",
                              "short"))
```

## 01. How do I aggregate by collapsing?

Using `group_by()` and `summarise()`, calculate average `danceability` by `long_short`.

On average, are Taylor Swift's longer or shorter songs more "danceable"?

***Shorter***

```{r}
tswift |> 
  group_by(long_short) |> 
  summarise(avg_danceability = mean(danceability))
```

## 02. How do I aggregate *without* collapsing?

Alter the `tswift` data frame to add a variable that calculates average acousticness by album (without collapsing).

Bonus: Can you determine if the song "Cruel Summer" is more or less acoustic than the Lover album average?

***Less acoustic (.12 vs. .33)***

```{r}
# add average acousticness by album
tswift <- tswift |> 
  group_by(album) |> 
  mutate(avg_acoustic = mean(acousticness))

# find Cruel Summer
tswift |> 
  select(name, acousticness, avg_acoustic) |> 
  filter(name == "Cruel Summer")
```

## 03. How do I tidy data?

Pivot the `nat_parks` data frame longer so that year and visitors each make a column.

Hint: Pivot the year columns only. To specify them, you can use either of these structures:

`cols = start:stop`

`cols = -c(column1, column2`)

```{r}
# pivot data long
nat_parks_long <- nat_parks |> 
  pivot_longer(cols = 3:7,
               names_to = "year",
               values_to = "visitors")

nat_parks_long
```