Introduction to Data Analysis with R

Installation

• Install R and RStudio (front-end for R)
• Google 'RStudio' or 'RStudio Desktop' and follow the installation instructions
• Open RStudio only; no need to interact with the R icon

Basic Operations

• RStudio can be used as a graphing calculator
  • Example: 5 + 7, abs(-17)
• Variable assignment
  • Example: x <- -12, y <- c(-12, 6, 0, -1)
  • Use <- for assignment (preferred over =)
• Operations on variables
  • Example: x + 7, abs(x), y * 2, abs(y)*

Data Import

• Import data sets via the file browser in the lower right of RStudio
  • Example: Import an Excel or CSV file
  • Use read_excel function
  • Store the data frame in a variable
  • Use View() to see the imported data

Scooby-Doo Data Set Example

• Example data set from Tidy Tuesday
  • Use library(readxl) to import Excel data
  • Install the readxl package using install.packages('readxl')
  • Import and view the data set: Scooby <- read_excel('file_path')
• Summary statistics
  • Calculate mean: mean(Scooby$runtime)
  • Handle missing values: mean(Scooby$imdb, na.rm = TRUE)

Scripting

• Use R scripts to document and encode your analysis
  • Example: Create a new script with File -> New File -> R Script
  • Save and execute script lines with cmd + Enter (Mac) or ctrl + Enter (PC)
  • Load packages with library(tidyverse)

Data Manipulation

• Built-in data sets: data()
• Viewing data sets: View(mpg), ?mpg, glimpse(mpg)
• Filter rows with filter function
  • Example: filter(mpg, cty >= 20)
  • Save filtered data: mpg_efficient <- filter(mpg, cty >= 20)
• Add or change columns with mutate
  • Example: mpg_metric <- mutate(mpg, cty_metric = cty * conversion_factor)
• Use pipes %>% to chain commands
  • Example: mpg %>% filter(cty >= 20) %>% mutate(cty_metric = cty * conversion_factor)

Grouped Summaries

• Group data with group_by and summarize with summarize
  • Example: mpg %>% group_by(class) %>% summarize(mean_cty = mean(cty, na.rm = TRUE), median_cty = median(cty, na.rm = TRUE))

Data Visualization

• Use ggplot2 for plotting
  • Example: ggplot(mpg, aes(x = cty)) + geom_histogram()
  • Add layers: + labs(x = 'City Mileage')
  • Scatter plot: ggplot(mpg, aes(x = cty, y = hwy)) + geom_point()
  • Add regression line: + geom_smooth(method = 'lm')
  • Color by category: aes(color = class)
  • Use color palettes: + scale_color_brewer(palette = 'Dark2')

Reporting with R Markdown

• Create R Markdown documents for sharing results
  • Example: File -> New File -> R Markdown
  • Add code chunks with triple backticks
  • Render documents with the knit button
  • Modify template: library(tidyverse), glimpse(mpg)

Tips

• Comments in code: Use # to add comments
• Customize code chunks: Use gear icon in RStudio to adjust chunk options
• Explore R Markdown cheat sheets for formatting options