Introduction to R and RStudio

  • Use RStudio to write and run R programs.
  • R has the usual arithmetic operators and mathematical functions.
  • Use <- to assign values to variables.
  • Use RStudio to create and manage projects with a consistent structure.
  • Treat raw data as read-only.
  • Treat generated output as disposable.

Data Structures and Subsetting Data

  • Use read.csv to read tabular data in R.
  • The basic data types in R are double, integer, complex, logical, and character.
  • Data structures such as data frames are built on top of lists and vectors, with some added attributes.
  • Indexing in R starts at 1, not 0.
  • Access individual values by location using [].
  • Access slices of data using [low:high].
  • Access arbitrary sets of data using [c(...)].
  • Use logical operations and logical vectors to access subsets of data.

Exploring Data Frames

  • Use cbind() to add a new column to a data frame.
  • Use rbind() to add a new row to a data frame.
  • Use str(), summary(), nrow(),ncol(),dim(),colnames(),head(), andtypeof()` to understand the structure of a data frame.
  • Read in a csv file using read.csv().
  • Understand what length() of a data frame represents.

R Packages and Seeking Help

  • Use install.packages() to install packages (libraries) from CRAN
  • Use help() to get online help in R
  • Use ls() to list the variables in a program
  • Use rm() to delete objects in a program

Manipulating Tibbles With Dplyr

  • Use the dplyr package to manipulate data frames.
  • Use select() to choose variables from a data frame.
  • Use filter() to choose data based on values.
  • Use group_by() and summarize() to work with subsets of data.
  • Use mutate() to create new variables.

Creating Publication-Quality Graphics with ggplot2

  • Use ggplot2 to create plots.
  • Think about graphics in layers: aesthetics, geometry, statistics, scale transformation, and grouping.

Wrap-up

  • This course covered the essentials of R and RStudio for reproducible analysis, providing a strong foundation for further learning.
  • Topics such as factors, matrices and arrays, and more advanced concepts like data frame manipulation with tidyr, control flow, and functions are critical next steps.
  • Continued practice and exploration of R will benefit you, your team, and the wider research community.