01. Introduction to RStudio⚓︎

Module items⚓︎

R Script file⚓︎

Copy the code below ➜ Paste into [[RStudio console]] ➜ Hit Enter

source(url("https://raw.githubusercontent.com/ttezcann/ssric-reg/refs/heads/main/regression/docs/assets/r-scripts/0_packages_data.R")); 
download.file("https://raw.githubusercontent.com/ttezcann/ssric-reg/refs/heads/main/regression/docs/assets/r-scripts/01-intro-rstudio.R", "01-intro-rstudio.R"); 
file.edit("01-intro-rstudio.R")

Lab assignment⚓︎

Generating a table

Sample lab assignment⚓︎

Sample: Generating a table

Learning outcomes⚓︎

1. Differentiate R and RStudio
2. Use RStudio Cloud (Posit)
3. Explain what R packages are
4. Understand the General Social Survey (GSS) structure
5. Learn the steps of using RStudio

Suggested reading⚓︎

Why do we use software for statistical analysis?⚓︎

• Software helps us to collect, organize, analyze, interpret and design data.

  • Many statistical methods are too complex to be calculated by hand or with calculators.
  • Software can process repetitive computational tasks rapidly.
  • We can save our exact analytical steps as source code, which is a fundamental requirement for making scientific research transparent and reproducible.

• For example, imagine we collected data from 1,000 students, and asked their happiness level, stress level, and class status.

  • happiness (What's your happiness level?) [1=unhappy, 2=neutral, 3=happy]
  • stress (What's your stress level?) [1=stressed, 2=neutral, 3=not stressed]
  • class (What's your class status?) [1=senior, 2=non-senior]

• Then, the data looks like this:

  id	happiness	stress	class
  1	3	2	1
  2	2	1	2
  3	2	3	2
  4	1	2	1
  ...	...	...	...
  1,000	2	1	2

• When dealing with a dataset of 1,000 students, manual calculation, i.e. how many students are not stressed, becomes practically impossible.

Software⚓︎

What is R?⚓︎

• R is a free, open-source programming language and software environment specifically designed for statistical computing, data analysis, and graphics.

  

What is RStudio?⚓︎

• RStudio is an Integrated Development Environment (IDE), which is a software application designed to make writing, editing, and executing R code much easier and more organized.

  • RStudio helps keep R more organized, and provides an interface by adding many convenient features and tools.
    • Free and open source: We can run RStudio from home and use it for the rest of our life.
    • Advanced models: Currently, the CRAN package repository features more than 23,000 available packages.
    • Growing popularity: Learning RStudio should stand us in good stead for future social research.

  

• R is like a car's engine; works without a wheel, but impossible to steer.

  • RStudio is like a wheel; gives you control and direction. The engine still does all the work. R always works at the background.

  

What is RStudio Cloud?⚓︎

• RStudio Cloud (currently known as Posit Cloud) is a browser-based version of the RStudio environment where you can write code, run analyses, install packages, and manage projects without installing R or RStudio on your own computer.

  • Although the instructions are for RStudio Cloud, all steps are same in other options.
  • Creating a “free account” will allow you 25 hours per month of connect time.

  

Options to use RStudio⚓︎

• There are three options to use RStudio
  • (1) Create a free RStudio Cloud account
  • (2) Install R and RStudio to your personal computer
  • (3) Using RStudio on a university lab computer

Data: General Social Survey (GSS)⚓︎

• We'll use General Social Survey ([[GSS]]) for the modules.
  • The GSS gathers data on contemporary American society in order to monitor and explain trends and constants in attitudes, behaviors, and attributes.
  • Hundreds of trends have been tracked since 1972.
  • In addition, since the GSS adopted questions from earlier surveys, trends can be followed for up to 80 years.

RStudio interface⚓︎

Main look⚓︎

• (1) Script file: This pane is where you write, edit, and save your R code or scripts.
• (2) Environment: This pane shows the objects currently loaded in memory, such as datasets, variables, and functions.
• (3) Console: This pane shows the codes that were run, immediate output, or error messages.
  • You will paste the script file codes here and hit "Enter" when you open RStudio.
• (4) Files/plots/packages/help: This pane lets you browse files, view tables and figures, manage packages, and access help documentation.

More detailed look⚓︎

• (1) Menu bar: The top strip with File, Edit, Code, View, Plots, and other menus. This is where you access all global settings and commands.
• (2) Script editor: The upper-left panel where you write and save your R code. Code here does not run until you tell it to.
• (3) Save button: Saves your current script to disk.
• (4) Run button: Executes the currently selected line(s). Keyboard shortcut: Cmd/Ctrl + Enter.
• (5) Source button: Runs the entire script from top to bottom, as if you submitted it all at once. Useful for running a complete analysis.
• (6) Environment tab: The upper-right panel showing every object currently loaded in memory.
• (7) History tab: This contains the history of all the commands we have recently run. We can go to this tab and re-run any commands we have run previously.
• (8) Data: This is where all your currently open data will appear. GSS data is there.
• (9) Files tab: This shows the files and folders within the project folder.
• (10) Plots tab: This displays any graphs you produce.
• (11) Packages tab: This will show all the installed and loaded packages.
• (12) Viewer tab: This displays any tables you produce.
• (13) Table view: The frequency distribution of marital status variable is here. You highlight the code in the script file, click "Run", and the table appears here.
• (14) Console tab: This shows the codes that were run, immediate output, or error messages.
  • You will paste the script file codes here and hit "Enter" when you open RStudio.

Packages⚓︎

• [[R packages]] are written by community contributors, and they are available for anyone to use.

  • They plug into RStudio to expand the software's built-in capabilities.
  • There is a massive ecosystem of thousands of free, open-source packages created by the community.
  • Most are hosted on CRAN (the Comprehensive R Archive Network), while development versions are often shared on platforms like GitHub.
  • Packages provide pre-written tools for highly specific tasks, such as data visualization and complex statistical modeling.
    • R is like a base car. It's functional, ready to drive. It has engine, steering, brakes.
    • R packages are like add-ons; GPS navigation, sunroof, heated seats, backup camera.

  

  • We will use several packages. Tidyverse is one of the packages we’ll use:

    

Steps of using RStudio⚓︎

[[Copy the code|ref]]: Copy the specific R script file code⚓︎

1. You will see a box of code at the top of each module, which comes with a different script file.
2. Hover over the code and click the "Copy" icon.

Open RStudio Cloud website: Use [[RStudio console|ref]]⚓︎

1. Open RStudio Cloud website and go to "RStudio labs".
2. Paste the code into RStudio console.
3. Hit Enter.

• The code you paste will:
  • Install and load all required packages
  • Download the "gss" data,
  • Silently relabel the variables, and
  • Open the script file.

Wait⚓︎

1. You will see a STOP sign.
2. And, codes are running in the console. You should wait until the 🛑 STOP 🛑 sign in the console disappears and no more code is running in the console.
3. When you see the script file opens and, "gss" appears under the "Environment - Data section," everything is all set.

[[Highlighting and running|ref]]⚓︎

1. We highlight the codes
2. And, click “Run”
3. Clicking “Run” generates the analysis (a frequency table for this example)