Kickstart with R:
Your Gateway to Data Science

IOC-R Week 1

Plan

• Unlock the Power of Your Data with
• Rstudio
• Setting Up an R Project
• Data Import & Export
• Need Some Help?
• Let’s Practice !

Unlock the Power of Your Data with

Welcome to R!

R is a programming language and software environment designed for statistical computing and data analysis.

Why R?

• Biologist friendly!
• Free and open-source
• Supported by a huge community

What you can do with R?

• Analyze your data
• Visualize your findings
• Automate your workflow
• Share reproducible research

The official website: https://www.r-project.org

The R Console

In the R console:

• > (The prompt): R is ready for your command. Type your code and press Enter to run it.
• [1] (Output): This indicates the start of an output line.
• + (Continuation): R is waiting for you to finish your command. You can either finish the command or press Esc to cancel it.

In the R Console, the > prompt lets you know that R is ready for your command. Just type your code and hit Enter. When you see the output, it will start with [1], which indicates the position of the first element. If you see a + at the beginning of a new line, it means R is waiting for you to finish your command, this often happens if you forget a parenthesis or a comma. You can either finish it by typing the rest of the code or press the Esc key to cancel the input completely and get a fresh > prompt.

Rstudio

Rstudio Login

If you use the RStudio Server, you need to connect to your account first.

Rstudio IDE

Integrated Development Environment (IDE)

1. Console: enter and execute R commands interactively.
2. Environment/History: tracks your variables and past commands.
3. Files/Plots/Packages: managing your files, viewing graphs, or installing tools.

Rstudio cheat sheet (and French version)

Everything you need in one organized window. Save your work as a script, RStudio includes many useful features which let us code easily. notebook, show results

Getting Started

• Open your RStudio.
• Create an R script: File -> New File -> R Script or click . (Will appeared at 4)
• Write your codes in the script.
• Save script: Cmd/Ctrl + S, name your script.
• Execute code: Click or press Cmd/Ctrl + Enter

Getting Started

Copy paste following codes into your script and save the script as test.R.

• Basic codes (commands):

# Use `#` to write a comment (line) in your script
# Math with R:
2 + 2    # Yes, R can do math!
sqrt(16) # Square root.

# Create a variable:
x <- 5 # Now x is 5. You can reuse it!
x + 1

# Inspect your environment:
## Look at the "Environment" pane, do you see your x?
ls()

# Load a package:
library(stats)

# Get help of a function (documentation):
?mean

# Quit RStudio properly
q()

Some Configuration

Go to Tools -> Global Options…

• Under the General tab:

  • Uncheck the box “Restore .RData…”
  • Set “Save workspace to .RData on exit” to Never.

Then click “Apply”.

Some Configuration

Go to Tools -> Global Options…

• Under the Code tab:

In the Display panel, check the box “Use rainbow parentheses”

Then click “Apply”.

Some Configuration

Go to Tools -> Global Options…

• Under the Appearance tab:

You can select a theme for you RStudio, e.g., the “Cobalt” for a dark theme.

• Under the Pane Layout tab:

You can arrange the four panels as you prefer.

In addition, click “Add Column” lets you open an extra “Source” code column, allowing you to view your scripts side by side!

Then click “Apply”.

Setting Up an R Project

Create an R Project

An R project keeps everything for your project in one working directory and helps R to know where to look for your files.

1. In RStudio, go to File -> New Project
2. Choose New Directory and click New Project
3. Set a folder name (e.g., r_week1) and select where to save it.
4. Click Create Project

You’ll notice RStudio restarts, and now your project is set up!

An R Project is a way to keep everything for your work (scripts, data, outputs) in one place. Think of it like a special folder where RStudio automatically knows where to look for your files. It helps avoid problems with messy file paths.

Create an R Project

A Well Organized Project Folder

• Tree structure:

r_week1              # Your working directory    
├── r_week1.Rproj    # The R project configuration file
├── README.md           # A short introduction of the project
├── data                # Raw data files
│   └── raw_count.tsv
├── scripts             # Your R code files
│   └── 01-analysis.R
│   └── 02-figures.R
└── outputs             # Results, e.g., graphs, reports

• Managing Files (via the File pane):
  • To create a new folder: click . The folder will be created in the current directory.
  • To move a file or folder: check the box next to its name, click and then select Move… to choose a new destination.

• Create data and scripts folders inside your working directory.
• Move the test.R into the scripts folder of your working directory.
  

example of bad home organisation: pillow in kitchen and microoven in the bathroom The trunk is the main folder (your R Project). Branches are subfolders for scripts, data, and output, ect. By building a clear structure, you’ll: Always know where to find things. Save time when coding in R or sharing your project with others. Avoid messy files scattered everywhere.

Understanding File Paths

How to tell your computer where to find the test.R?

• Absolute path, starts from the very top of your computer’s folder. E.g.:
  • Linux/Mac: /Users/lijiao/Documents/r_week1/scripts/test.R
  • Windows: C:\Users\lijiao\Documents\r_week1\scripts\test.R

Understanding File Paths

A path tells your computer where to find files/folders.

• Relative path, starts from your working directory (folder). E.g.:
  • Linux/Mac: scripts/test.R
  • Windows: scripts\test.R

Where Are You?

r_week1              # Your working directory
├── r_week1.Rproj    # The R project configuration file
├── data             # Raw data files
└── scripts          # Your R code files
    └── test.R

> getwd() # Get working directory
[1] "/Users/lijiao/Documents/r_week1"

What are the absolute and relative paths of the folder “data”?

absolute path: /Users/lijiao/Documents/r_week1/data

relative path: data

> file.exists("/Users/lijiao/Documents/r_week1/data")
[1] TRUE
> file.exists("data")
[1] TRUE

Data Import & Export

Import Data

• Supported formats:
  • text files (.txt, .csv, etc.)
  • Excel files
  • data from other softwares (e.g.: SAS)
• Click-button methods:
  • From menu bar: File -> Import Dataset -> Select the format -> Choose your file
  • From the “Environment” pane: click Import Dataset -> Select the format -> Choose your file
  • From the “Files” pane: Navigate to your file location -> Click the file and select Import Dataset (need the {readr} package)

Pop-up window using the {readr} import wizard.

Import Data

Importing your first file: shiny_sample_data.csv

1. Right click the link and select “Download Linked File As…” (or the equivalent on your system).
2. Save the file into the data folder of the R project r_week1
3. Import the data with one of the three click-button ways.

When you select the file, a preview window will pop up. RStudio tries to guess the data’s properties, but you should always confirm them.

Think before you import the data

• How do you want to name the imported data?
• Is there a header?
• What is the column separator?
• Which sign is used as the decimal?

Once you’ve checked these settings and the preview looks correct, click Import.

Import Data

Export Data

We’ll use the built-in iris dataset and save it in the CSV format using the write_csv() function from the {readr} package.

head(iris) # show the first lines of the dataset
View(iris) # preview the whole dataset
?write_csv # get help of the function

Goal: Save the built-in iris dataset as a CSV file named iris_export.csv inside the outputs folder of the project.

1. Create the outputs folder.
2. Type the following command and execute it:

write_csv(iris, file = "outputs/iris_export.csv")

Go to the Files pane, can you find the saved file?

(We’ll talk about other formats in the future!)

Once you’ve cleaned, analyzed, or modified a dataset in R, you’ll want to save it. While you can save data in many formats, we’ll use the most common one, a CSV file, as our example. We’ll use the built-in iris dataset.

Need Some Help?

Different Solutions

• Google it!
• Forums: Stack Overflow, Cirad(francophone), etc.
• Ask/discuss your question via Slack.
• AI tools (like ChatGPT or Gemini) can be very helpful resources😉, but be careful of false answers! (More details in session 6)

Key points:

• Be specific.
• Provide context: code, error, etc.
• Always verify.
• Protect sensitive data.

google/forum stack overFlow - what are you trying to do / what did you try? - the warning or error message / your code / your data structure - language name / function or package name / a specific problem - try and iterate

PDF of Slides

To save the slides as a PDF:

• Open the presentation in a web browser
• Press “e” to enable PDF export mode
• Press Cmd/Ctrl + P to open the in-browser print dialog.
• Change layout to “Landscape”.
• Select “save as PDF” as the printer option and save the file.

After saving, press “e” again to return to presentation mode.

Let’s Practice !

Today’s Goals

• Get familiar with the RStudio
• Create an R project in RStudio
• Import data file into RStudio
• Export data from RStudio