Published

February 26, 2024

Exercises

ggplot Customization with National Park Visitation Data

These exercises use National Park visitation data from 1979–2024. For more context about the dataset, see the data essay.

Concepts covered:

Filtering data for a specific category
Line plots with custom colors and titles
Customizing x-axis tick intervals
Abbreviating y-axis labels (millions, thousands)
Adjusting axis limits to zoom into a time period

Load National Park Visitation data

Code

np_data <- read.csv("https://raw.githubusercontent.com/melaniewalsh/responsible-datasets-in-context/main/datasets/national-parks/US-National-Parks_RecreationVisits_1979-2024.csv",
 stringsAsFactors = FALSE)

View the np_data dataframe by clicking on the spreadsheet icon in the Global Environment

Load libraries

Code

library("dplyr")
library("stringr")
library("ggplot2")
library("scales")

How have visits to a particular National Park changed over time?
What is the most interesting period of change?

Exercise 1

First, filter the dataframe for a park of your choice. Then, pick a National Park that you haven’t worked with yet, and filter the data for only that park.

Code

# Your code here

Exercise 2

Now, make a line plot that shows the number of visits per year to that park from 1979 to 2022.

2a.

Choose a color for the line.

2b.

Give the plot a title that also functions as a kind of “headline” for the most interesting story of the plot.

2c.

Change the x-axis ticks so that they increase 5 years at a time.

2d.

Change the y-axis tick labels so that they abbreviate millions to M and thousands to K.

Code

# Your code here

Exercise 3

Now, create a plot that zooms in on the most interesting time period for this particular National Park.

3a.

Change the x-axis limits so that it only shows the most interesting years.

3b.

Come up with a new title that describes this time period.

Code

# Your code here

---
title: "ggplot Customization with National Park Visitation Data (Exercise)"
date: "2024-02-26"
categories: [ggplot, advanced, exercise]
format:
  html: default
code-overflow: wrap
code-fold: show
editor: visual
df-print: kable
R.options:
  warn: false
code-tools: true
execute:
  eval: false
---

# <span style="color:green;"> Exercises </span>

## ggplot Customization with National Park Visitation Data

<span style="color:red;"> [Solutions](Ggplot-Customization-NP-Solutions.qmd) </span>

These exercises use National Park visitation data from 1979–2024. For more context about the dataset, see the [data essay](../index.qmd).

**Concepts covered:**

- Filtering data for a specific category
- Line plots with custom colors and titles
- Customizing x-axis tick intervals
- Abbreviating y-axis labels (millions, thousands)
- Adjusting axis limits to zoom into a time period

## Load National Park Visitation data
```{r}
np_data <- read.csv("https://raw.githubusercontent.com/melaniewalsh/responsible-datasets-in-context/main/datasets/national-parks/US-National-Parks_RecreationVisits_1979-2024.csv",
 stringsAsFactors = FALSE)
```

View the np_data dataframe by clicking on the spreadsheet icon in the Global Environment

## Load libraries

```{r}
#| message: false
library("dplyr")
library("stringr")
library("ggplot2")
library("scales")
```

- How have visits to a particular National Park changed over time?
- What is the most interesting period of change?

# Exercise 1
First, filter the dataframe for a park of your choice. Then, pick a National Park that you haven't worked with yet, and filter the data for only that park.

```{r}
# Your code here
```

# Exercise 2

Now, make a line plot that shows the number of visits per year to that park from 1979 to 2022.

### 2a.
Choose a color for the line.

### 2b.
Give the plot a title that also functions as a kind of "headline" for the most interesting story of the plot.

### 2c.
Change the x-axis ticks so that they increase 5 years at a time.

### 2d.
Change the y-axis tick labels so that they abbreviate millions to M and thousands to K.


```{r}
# Your code here
```

## Exercise 3

Now, create a plot that zooms in on the most interesting time period for this particular National Park.

### 3a.
Change the x-axis limits so that it only shows the most interesting years.

### 3b.
Come up with a new title that describes this time period.


```{r}
# Your code here
```