Week 11 - Preparation: Computer set-up and data imporation

CHC9012 | National Taiwan Normal University

???? Goals

Prepare your computer:

• Projects: How to set-up a project in RStudio?

• Advanced scripts: How to use Markdown documents to share better scripts?

• Data importation: Where to find your PCIbex results file, and how to import it to RStudio?

???? Part 1: Prepare your computer: R projects

Why?

We want to be organized and not messy!

• Section 1.1: Prepare the folder in your computer.

• Section 1.2: Create the related R project.

1.1 Setting up your folders

1. Navigate to your Documents (or ???件) folder.

2. Create your Root Folder: CHC9012_Spring2025

3. Add the Project Subfolders:
   • ???? Slides: Presentation files.
   • ???? PCIbex_design: Scripts & materials.
   • ???? PreprocessingAnalysesR: Datasets & R code.

You should obtain something like this

Pay attention to the names

Generally, it is better not to use any space when naming folders and files!

1.2 Initializing Your R Project

1. Launch New Project Wizard
Click the blue cube icon in the top-left corner of RStudio.

2. Select project type
Choose “Existing Directory” to link R to your folder structure.

3. Finalize Connection
Browse to PreprocessingAnalysesR, check “Open in a new session”, and click Create Project.

Benefit: An .Rproj file sets your “Working Directory” automatically!

Other advantages of creating projects

1. One RStudio window = One project
2. Switch between them without worrying about the working directory of trying to find the right files!
3. List of your projects on the top-right corner of RStudio. Here is mine for example:

???? In-class exercise!

Task list:

• Find the R script from last week and move it to the PreprocessingAnalysesR folder.
• Open the script within your newly created project.

???? Part 2: Better and reproducible scripts with R Markdown

From raw script to functional: R Markdown

1. Create the file ????
Go to New File > R Markdown. This moves you from raw code to a “living” document.

2. Configure (YAML) ???�?
The header between --- controls your title, author, and output format (HTML/PDF).

3. Text + Code chunks ????
Write text normally; insert Code Chunks ({r}) for analysis.

4. Run your codes ??��??
Use the green Play Button to test individual steps.

5. The Final “Knit” ??��
Click Knit to stitch everything into a polished report automatically!

???? In-class exercise!

Task list:

• Create a new Markdown document, and call it ScriptWeek10_Markdown.
• Use the script from last week, but adapt it to the Markdown format. Be creative!

???? Part 3: Download your PCIbex results and import them into RStudio

Fake experiment for this class

Try the experiment

The code of the fake experiment is given on the website

Fake results!

An AI robot did the experiment several times. I also pretended that I was a “bad” participant several times. The results are just to show you how to analyze data from PCIbex, they do not have other value!

• 3.1. Download the results file
• 3.2. Importing the data into RStudio

• On the PCIbex platform, click on the Results button.

• Then, click on Download.

• Finally, place the file in the folder you just created on your computer!

• First, open a new Markdown document. Call it DataImportFromPCIbex.

• Second, create an r block, paste the following code, and run it to activate the function.

read.pcibex <- function(filepath, auto.colnames=TRUE, fun.col=function(col,cols){cols[cols==col]<-paste(col,"Ibex",sep=".");return(cols)}) {
  n.cols <- max(count.fields(filepath,sep=",",quote=NULL),na.rm=TRUE)
  if (auto.colnames){
    cols <- c()
    con <- file(filepath, "r")
    while ( TRUE ) {
      line <- readLines(con, n = 1, warn=FALSE)
      if ( length(line) == 0) {
        break
      }
      m <- regmatches(line,regexec("^# (\\d+)\\. (.+)\\.$",line))[[1]]
      if (length(m) == 3) {
        index <- as.numeric(m[2])
        value <- m[3]
        if (is.function(fun.col)){
         cols <- fun.col(value,cols)
        }
        cols[index] <- value
        if (index == n.cols){
          break
        }
      }
    }
    close(con)
    return(read.csv(filepath, comment.char="#", header=FALSE, col.names=cols))
  }
  else{
    return(read.csv(filepath, comment.char="#", header=FALSE, col.names=seq(1:n.cols)))
  }
}

• Third, import the data using this function. The code is as below. We store the data into a new dataset (variable) that we call data.

data <- read.pcibex("results_FAKE_FOR_CHC9012.csv")

• Finally, it is always a good idea to have a look at the data that you just imported. You can do that through these lines of code.

head(data, 20)

str(data)

summary(data)

???? In-class exercise!

Task list:

• Look at the whole table. To do so, click on the variable, or just write view(data) in the console
• Manually, identify and take notes of (1) what information is missing, (2) which rows should be erased/kept, and (3) which columns should be deleted or added.

???? Assignment for next week

No assignment for next week! Please carefully review the content of these past two weeks, and finalize the data collection of your own project.