RStudio Guide

To get started, click on the 'workspace' folder in the bottom right pane of RStudio. Click on the file entitled "Step 1...". You may want to drag the console pane to be smaller so that you have more room to work. You'll complete each task for Step 1 in that R Markdown file. Remember, you must run the cells with the play button at the top right of each cell for a task before moving onto the next task in the R Markdown file. Continue until you have completed all tasks in this step. Then when you are ready to move onto the next step, you'll come back and click on the file for the next step until you have completed all tasks in all steps of the lab.

Working with R Functions and Writing Reproducible R Scripts

To review the concepts covered in this step, please refer to the Working with R Code in RStudio module of the Building Your First R Analytics Solution course.

Understanding how to work with R functions and write reproducible R scripts is important because it allows for efficient data analysis and ensures that your work can be replicated by others. This step will help you practice calling R functions, passing arguments to functions, and writing reproducible R scripts.

In this step, you'll dive deeper into the world of R programming. First, you'll practice loading R packages into the environment. You'll practice calling R functions, which are predefined pieces of code that perform a specific task. You'll also practice passing arguments to functions, which are the inputs that the function uses to perform its task. Finally, you'll practice writing reproducible R scripts, which are scripts that can be run by others to reproduce your analysis. The goal is to improve your R programming skills and learn to write code that others can easily understand and replicate.

Task 1.1: Understanding R Packages and Functions

Load the ggplot2 library (it is already installed). Use the qplot function to make a scatterplot.
Use the provided x and y variables, which correspond to the first two arguments of qplot.

library(ggplot2)
qplot(x, y)

Task 1.2: Call a Function with Named Arguments

The qplot() function has a named argument main that allows you to add a main title to your plot. Add main as a named argument and give your plot the title 'My Plot'

qplot(x, y, main = 'My Plot')

Task 1.4: Writing a Reproducible R Script

Examine the provided R script. This script is written in a way that is not reproducible, If you run it, you will encounter an error. Fix the script so that it becomes reproducible.

library(readr)
student_scores <- read_csv('student_scores.csv')
subjects <- c('math_score', 'english_score', 'science_score')
colMeans(student_scores[subjects])

Organizing Your Work with RStudio Projects

To review the concepts covered in this step, please refer to the Organizing Your Work with RStudio Projects module of the Building Your First R 3 Analytics Solution course.

Knowing how to organize your work with RStudio projects is important because it helps keep your work organized and makes it easier to share with others. This step will help you practice creating RStudio projects, adding files to projects, and referring to files in R scripts.

In this step, you'll learn to keep your work organized with RStudio projects. You'll practice creating RStudio projects, which are a way of keeping related files and settings together in one place. You'll also practice adding files to projects and referring to these files in your R scripts. The goal is to improve your project management skills and learn to work more efficiently in RStudio. You'll be using the getwd, dir, and read_csv functions.

Task 2.1: Create a new RStudio project

Create a new RStudio project in the workspace directory.

Task 2.3: Check the current working directory

When a project is open, your working directory will be set to the directory of the project.

In the Console window, check the current working directory to make sure it's the directory of your RStudio project.

getwd()

Task 2.4: List the files in your project directory

List the files in your project directory to make sure the data file is there.

dir()

Task 2.5: Use the Console to read files in the project directory

In the Console, load the readr package. Then, use the read_csv() function to load the data into the R environment.

library(readr)
data <- read_csv('student_scores.csv')

Communicating and Documenting Your Analysis with R Markdown

To review the concepts covered in this step, please refer to the Communicating and Documenting Your Analysis with R Markdown module of the Building Your First R 3 Analytics Solution course.

Being able to communicate and document your analysis with R Markdown is important because it allows you to share your findings in a clear and reproducible way. This step will help you practice creating R Markdown documents, working with code and text in these documents, and controlling the appearance of the output.

In this step, you'll learn to communicate and document your analysis with R Markdown. You'll practice creating R Markdown documents, which are a way of interweaving code, results, and text in a single document. You'll also practice working with code and text in these documents, and controlling the appearance of the output. The goal is to improve your communication skills and learn to create reproducible reports.

Task 3.1: Add a code chunk to your R Markdown file

Add a code chunk. Name the code chunk setup and change the options so that the chunk will be run, but neither the code nor its output will appear in the document.

```{r setup, include=FALSE}

```

Task 3.2: Load the data into your R Markdown file

Within your code block, load the student scores data into your R Markdown file using the read_csv() function from the readr package. Save the data to a variable named data.

library(readr)
data <- read_csv('student_scores.csv')

Task 3.3: Add a code chunk that is visible in the document

Create a new code chunk named calculate_average. Set the options so that the code will appear in the document. Within this code chunk, calculate the average of the age column and save it to a variable named average_age.

```{r calculate_average, echo=TRUE}
average_age <- mean(data$age)
```

Task 3.4: Add text to your R Markdown file

Below the code chunk, write the text "The average age in the sample is ___". In the blank space, use inline R code to print the average age, rounded to 1 decimal place.

Task 3.5: Knit your R Markdown file

Knit your R Markdown file to an HTML document.