The goal of this material is to provide a concise introduction to R & RStudio with sufficient training on fundamentals that students can understand R syntax, resolve common R errors, and complete basic data analysis. No prior knowledge of R is assumed, nor other programming experience. Each session is intended to take about 3 hours. After completion students should be familiar with R and able to teach themselves more advanced techniques using online resources.

Prepared for Public Policy 290 "Analytics for Government Policy" by Avi Feller, Spring 2016.

R Markdown file. Topics covered:

• Overview of RStudio and RMarkdown
• Math functions
• Assignment operator <- and =
• Importing data: getwd(), rio import()
• Environmental variables: ls() and rm()
• Sequences: colon operator : and seq()
• Vectors, vector functions length(), sum(), mean(), max(), min(), summary()
• Random numbers: set.seed() and runif()
• Getting help: ?, help(), help(package=)
• Calling functions
• Creating vectors: c(), rep()
• Subsetting vectors with bracket notation []
• Data frames: data.frame(), nrow(), ncol(), dim(), names(), str()
• Subsetting data frames: bracket notation [], $, subset()
• Factors: levels(), as.character()
• Object types: class(), str()
• Data analysis: table(), prop.table()
• Visualization: qplot() from ggplot2.

R Markdown file. Topics covered:

• Missing values: is.na(), na.rm option
• More data frames: cbind(), rbind(), colMeans(), rowMeans()
• More ggplot2: custom labeling, themes, colors, sizes; ggplot(), geom_point(), geom_density(), geom_path(), geom_line(), ggtitle(), geom_text(), geom_label(), labs(), ggsave()
• Flow control: for loops, if else blocks
• More vectors: ifelse(), %in%
• Cleaner text output with cat()
• Creating functions: basic functions, function arguments, returning results, argument defaults

Future content: day 3 would focus on data analysis using regression, apply and replicate, and merging files.

• Books
  • R for Data Science
  • Advanced R
  • Efficient R
• Trainings
  • R Bootcamp by Chris Paciorek
  • D-Lab's R Fundamentals
• Courses
  • Stat 133
  • Stat 243 by Chris Paciorek