Notes for dapR1 Live R Session; Week 1, Semester 1

Notes for dapR1 Live R Session; Week 1, Semester 1

Week 1 Summary of the Session

  • Overview of Session: Basic features of RStudio and reading data files.

  • Access RStudio: Via the link on the Learn page in the “Quick Links” section.

    • Click on “RStudio Server Online (by Notable)” and select “RStudio” for the notebook server.

  • Purpose of Notes: Provided to help you remember main points shown in real-time during the session.

Using R as a Calculator

  • R can operate in interactive mode, functioning as a calculator.

  • Console Panel: Located on the left in RStudio, often requires selection.

    • Input commands after the ‘>’ prompt, followed by pressing Return.

Basic Calculations

  • Example command:

    • 2*8

    • Output: ## [1] 16

    • More complex calculation:

    • 2*(8+3)

    • Output: ## [1] 22

Storing Results in Variables

  • Assigning Values: Use the assignment operator “<-” to store results.

    • Example:

    • mynumber <- 2*(8+3)

  • Accessing Variables: Type name in console to see its value.

    • mynumber

    • Output: ## [1] 22

  • Using Variables in Calculations:

    • mynumber/2

    • Output: ## [1] 11

Loading Packages and Reading in Data

  • Special functions often require loading packages in R.

  • Packages Panel: Located in the bottom right of RStudio, shows a list of packages.

  • Loading Tidyverse Package:

    • Command:

    • library(tidyverse)

  • Reading Data: Execute a command to read a dataset used in the lecture.

    • Command:

    • mydata <- read_csv("https://uoepsy.github.io/data/lecture1_data.csv")

    • Uses function read_csv() with URL in quotes.

Examining the Data

  • Displaying the Data: Can simply type the variable name.

    • Command:

    • mydata

    • Output:
      ## # A tibble: 5 x 9 ## ID Hair_colour Likert_item Likert_values Degree ReactionTime Height_cm ## <chr> <chr> <chr> <dbl> <chr> <dbl> <dbl> ## 1 ID101 Brown Strongly Agree 5 No 1.2 191. ## 2 ID102 Brown Agree 4 No 0.9 181. ## 3 ID103 Blonde Agree 4 Yes 3.2 165. ## 4 ID104 Blonde Disagree 2 Yes 55.5 177. ## 5 ID105 Black Strongly Disagr~ 1 Yes 2.1 201 ## # i 2 more variables: Weight_kg <dbl>, IQ <dbl>

    • Understanding Output Format:

    • Data shown as a “tibble”, with 5 rows and 9 columns.

    • Each column has variable names and types (e.g., “chr” = character, “dbl” = double).

Using Structure and Glimpse Commands

  • Structure Command: Gives a detailed structure of the dataset.

    • Command:

    • str(mydata)

    • Output:

    ## spc_tbl_ [5 x 9] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ ID  : chr [1:5] "ID101" "ID102" "ID103" "ID104" ...
##  $ Hair_colour  : chr [1:5] "Brown" "Brown" "Blonde" "Blonde" ...
##  $ Likert_item : chr [1:5] "Strongly Agree" "Agree" "Agree" "Disagree" ...
##  $ Likert_values: num [1:5] 5 4 4 2 1
##  $ Degree  : chr [1:5] "No" "No" "Yes" "Yes" ...
##  $ ReactionTime : num [1:5] 1.2 0.9 3.2 55.5 2.1
##  $ Height_cm  : num [1:5] 191 181 165 177 201
##  $ Weight_kg  : num [1:5] 88.9 76.6 52 81.5 105.8
##  $ IQ  : num [1:5] 100 105 99 120 131

  • Glimpse Command: Provides a concise overview.

    • Command:

    • glimpse(mydata)

    • Output:
      ## Rows: 5 ## Columns: 9 ## $ ID <chr> "ID101", "ID102", "ID103", "ID104", "ID105" ## $ Hair_colour <chr> "Brown", "Brown", "Blonde", "Blonde", "Black" ## $ Likert_item <chr> "Strongly Agree", "Agree", "Agree", "Disagree", "Strongl~ ## $ Likert_values <dbl> 5, 4, 4, 2, 1 ## $ Degree <chr> "No", "No", "Yes", "Yes", "Yes" ## $ ReactionTime <dbl> 1.2, 0.9, 3.2, 55.5, 2.1 ## $ Height_cm <dbl> 191.2, 180.8, 165.3, 177.1, 201.0 ## $ Weight_kg <dbl> 88.9, 76.6, 52.0, 81.5, 105.8 ## $ IQ <dbl> 100, 105, 99, 120, 131

Accessing Specific Data Points

  • Accessing a Specific Column: Use dollar sign operator.

    • Command:

    • mydata$Hair_colour

    • Output: ## [1] "Brown" "Brown" "Blonde" "Blonde" "Black"

  • Accessing Specific Cells:

    • Example: Row 3, Column 4:

    • Command: mydata[3,4]

    • Output:
      ## # A tibble: 1 x 1 ## Likert_values 3

    • Whole Row Access:

    • Command: mydata[3,]

    • Whole Column Access:

    • Command: mydata[,4]

Dimensions of the Data

  • Number of Rows:

    • Command:

    • nrow(mydata)

    • Output: ## [1] 5

  • Number of Columns:

    • Command:

    • ncol(mydata)

    • Output: ## [1] 9

  • Overall Dimensions:

    • Command:

    • dim(mydata)

    • Output: ## [1] 5 9

Adding Data to the Dataset

  • Adding a New Column: Assign values to a new column using the dollar-sign operator.

    • Command:

    • mydata$fav_animal <- c("cat","dog","horse","rabbit","donkey")

    • Viewing the Updated Dataset:

    • Command: mydata

  • Using c() Function: Used to create a list in R.

Declaring a Factor

  • A factor is a variable interpreted as a set of categories (levels).

  • Example: The “Degree” variable could be a factor with levels “Yes” and “No”.

  • Declaring Factor:

    • Command:

    • mydata$Degree <- factor(mydata$Degree)

  • Confirming Factor Status: Run the str() command.

Uploading, Creating, and Exporting Files

  • Upload Files: Click the “Upload” button in the Files panel to upload files from your computer.

  • Saving a Dataset as CSV:

    • Command:

    • write_csv(mydata, file="mydata.csv")

    • The file will appear in the “Files” tab.

  • Exporting Files to Local Disk:

    • Select file checkbox, click “More” and choose “Export”.

Creating and Using R Markdown Documents

  • Purpose of R Markdown: To save work and include R code chunks that can run within the document.

  • Creating R Markdown Document:

    • Through: File > New File > R Markdown

    • This creates a new file with example contents that can be edited.