This folder contains sample datasets for use in exercises and examples throughout the R training course.

Available Datasets

1. sales_data.csv

Purpose: Beginner/Intermediate exercises - data manipulation - Format: Wide format (months as columns) - Columns: store, product, jan, feb, mar, apr, may, jun - Use for: - Data reading/writing practice - Reshaping wide-to-long - Group by operations - Visualization exercises

2. student_grades.csv

Purpose: Beginner/Intermediate exercises - Columns: student_id, name, age, math, english, science, grade - Use for: - Basic data frame operations - Computing summary statistics - Creating visualizations - Data manipulation exercises

3. pharmacy_study.csv

Purpose: Advanced exercises - ANOVA and hypothesis testing - Columns: participant_id, drug, gender, age, bp_before, bp_after - Use for: - Two-way ANOVA - Hypothesis testing - Paired tests - Interaction effects

4. treatment_data.csv

Purpose: Advanced exercises - experimental design - Columns: subject_id, group, pre_treatment, post_treatment, age, gender - Use for: - Paired t-tests - Repeated measures ANOVA - ANCOVA - Effect size calculations

5. experimental_design.csv

Purpose: Advanced exercises - two-factor ANOVA - Columns: subject, factor1, factor2, response - Use for: - Two-way ANOVA - Interaction effects - Post-hoc tests - Effect size calculations

Usage

Reading Datasets in R

# Basic CSV reading
data <- read.csv("datasets/sales_data.csv")

# With readr package (recommended)
library(readr)
data <- read_csv("datasets/sales_data.csv")

Using Relative Paths

# From project root
sales <- read.csv("datasets/sales_data.csv")

# Using 'here' package
library(here)
sales <- read.csv(here("datasets", "sales_data.csv"))

Built-in Datasets

R also includes many built-in datasets that are used throughout the course:

  • mtcars - Motor Trend car data
  • iris - Fisher's iris measurements
  • mtcars - Car fuel efficiency data

Loading Built-in Datasets

# Load built-in dataset
data(mtcars)

Creating Your Own Data

You can also create your own sample data in R:

# Create sample data
set.seed(123)
my_data <- data.frame(
  id = 1:100,
  group = sample(c("A", "B", "C"), 100, replace = TRUE),
  score = rnorm(100, mean = 75, sd = 10)
)

Data Requirements by Level

Beginner

  • Basic structures (sales_data.csv, student_grades.csv)
  • Small, simple datasets
  • Good for indexing and subsetting

Intermediate

  • Real-world styled data
  • Multiple variables
  • Suitable for dplyr and ggplot2
  • Can be used for both sales_data.csv and student_grades.csv

Advanced

  • Experimental designs (pharmacy_study.csv, treatment_data.csv, experimental_design.csv)
  • Suitable for statistical tests
  • Balanced or unbalanced designs
  • Multiple factors and interactions

Notes

  • All datasets are in CSV format for easy import
  • Datasets are intentionally simple for learning
  • Some datasets use created data suitable for statistical exercises
  • Datasets may be used across multiple lessons
  • Feel free to modify datasets for additional practice

Contributing

If you create additional datasets: - Keep them realistic and educational - Include a description in this README - Ensure data can be used in exercises - Provide clear variable names - Include appropriate sample sizes