Published

February 26, 2024

Exercises

Introduction to DPLYR with National Park Visitation Data

Download as R Script

Load National Park Visitation data

Code

np_data <- read.csv("https://raw.githubusercontent.com/melaniewalsh/Neat-Datasets/main/1979-2020-National-Park-Visits-By-State.csv",
 stringsAsFactors = FALSE)

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

Install tidyverse

Code

install.packages("tidyverse")

Load dplyr library

Code

library(dplyr)

Exercise 1

Select 2 columns from the data using a DPLYR function. Save this 2-column dataframe to the variable smaller_df. Make sure to use the pipe %>% operator!

Code

smaller_df <- Your code here

How does the number of visits to Washington national parks compare to another state?

Exercise 2

Filter the dataframe for only values in the state of Washington and save to the variable wa_parks

Code

Your code here

Exercise 3

Calculate the sum total of RecreationVisits to Washington by using summarize() on the smaller dataframe wa_parks

Code

Your code here

Exercise 4

Filter the dataframe for only values in another state (your choice) and save to a variable. Calculate the sum total of RecreationVisits to this state by using summarize().

Code

Your code here

How do the number of visits to these 2 states compare to one another?

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

## Introduction to DPLYR with National Park Visitation Data

<a href="Intro-to-DPLYR-NP.R" download="Intro-to-DPLYR-NP.R">Download as R Script</a>  

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

# Load National Park Visitation data
```{r}
np_data <- read.csv("https://raw.githubusercontent.com/melaniewalsh/Neat-Datasets/main/1979-2020-National-Park-Visits-By-State.csv",
 stringsAsFactors = FALSE)
```

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

# Install tidyverse

```{r}
install.packages("tidyverse")
```

# Load dplyr library
```{r}
library(dplyr)
```

# Exercise 1
Select 2 columns from the data using a DPLYR function.
Save this 2-column dataframe to the variable smaller_df.
Make sure to use the pipe %>% operator!
```{r}
smaller_df <- Your code here
```

How does the number of visits to Washington national parks compare to another state?

# Exercise 2
Filter the dataframe for only values in the state of Washington and save to the variable wa_parks

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

# Exercise 3
Calculate the sum total of RecreationVisits to Washington by using summarize() on the smaller dataframe wa_parks

```{r}
Your code here
```
# Exercise 4

Filter the dataframe for only values in another state (your choice) and save to a variable.
Calculate the sum total of RecreationVisits to this state by using summarize(). 

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

How do the number of visits to these 2 states compare to one another?