- U.S. Air Force - Operations Research Analyst
- Air Force Institute of Technology - Assistant Professor
- University of Cincinnati - Adjunct Assistant Professor
- Website: bradleyboehmke.github.io
- Twitter: @bradleyboehmke
- Code: GitHub

- Me

- Time: 1:15 - 4:15
- Location: Lindner 608

- Companion website: uc-r.github.io
- Slides: uc-r.github.io/r_bootcamp

- R Programming literacy
- Data visualization

- Computer
- R
- IDE (i.e. RStudio)
- Internet (not absolute)

Provides an intensive, hands-on introduction to the R programming language. Prepares students with the fundamental programming skills required to start your journey to becoming a modern day data analyst.

Upon successfully completing this course, students will:

- Be up and running with R
- Understand the different types of data R can work with
- Understand the different structures in which R holds data
- Be able to import data into R
- Perform basic data wrangling activities with R
- Compute basic descriptive statistics with R
- Visualize their data with base R and ggplot graphics

- Getting started with R
- Importing data into R
- Understanding data structures
- Understanding data types

- Shaping and transforming your data
- Base R graphics
- ggplot graphics library
- case studies

- Frees us from point-n-click analysis software
- Allows us to customize our analyses
- Allows us to build analytic applications

- Forces us to think about our analytic processes

- Many statistical programming languages now leverage C++ and Java to speed up computation time

- Provides reproducibility that spreadsheet analysis cannot
- Literate statistical programming is on the rise

- .csv, .txt, .xls, etc. files
- web scraping
- databases: MySQL, Oracle, PostgreSQL, etc.
- SPSS, Stat, SAS

- easy to create "tidy" data
- works well with numerics, characters, dates, missing values
- robust regex capabilities

- joining disparate data sets
- selecting, filtering, summarizing
- great "pipe-line" process

- R is known for its visualization capabilities
- ggplot
- interactive plotting - easily leverage D3.js libraries

- built for statistical analyses
- thousands of libraries provide many statistical capabilities
- easy to build your own algorithms

- RMarkdown (produce slides, HTML web pages, pdf)
- Shiny
- Reproducibility (communicate to your future self!)

*“Programming is like kicking yourself in the face, sooner or later your nose will bleed.”*

- Kyle Woodbury

- Go to https://cran.r-project.org/
- Click "Download R for Mac/Windows"
- Download the appropriate file:
- Windows users click Base, and download the installer for the latest R version
- Mac users select the file R-3.X.X.pkg that aligns with your OS version

- Follow the instructions of the installer.

- Go to RStudio for desktop https://www.rstudio.com/products/rstudio/download/
- Select the install file for your OS
- Follow the instructions of the installer.

**Note:** There are other R IDE's available: Emacs, Microsoft R Open, Notepad++, etc.