# Description

This 2-day course provides a brief introduction to the basics of programming and the fundamentals of the R language and its use for data analysis–including exploratory data analysis, linear and logistic regression, variable selection, model diagnostics, and prediction.

Please follow the Pre-Workshop Instructions prior to coming to the workshop.

# Syllabus

### DAY 1 - April 13

**What is Programming?**: we introduce the concept of programming without a computer to get students thinking critically**Basics of Programming**: the basics of general programming–including programming languages, data structures, control structures, and functions**Fundamentals of R & RStudio**: the basics of the R language–including objects, subsetting, indexing, data input & output, and packages

### DAY 2 - April 27

**Exploratory Data Analysis**: all the necessary tools to investigate your data before performing any formal modeling–from summary statistics to visualization including plotting histograms, boxplots, and scatterplots**Linear Regression**: everything you need to know to begin fitting linear models–from simple t-tests to estimation of regression coefficients, variable selection, model diagnostics, and prediction**Logistic Regression**: the basics of generalized linear models (GLMs) with an emphasis on binary response data–we extend the theory and modeling strategies of linear regression

# Schedule

### DAY 1 - April 13

Time | ||
---|---|---|

11:00 - 12:00 | Introduction to Programming | |

12:00 - 12:15 | Exercise 1 | |

12:15 - 1:00 | Lunch | |

1:00 - 1:45 | Data Structures | |

1:45 - 2:00 | Exercise 2 | Solution |

2:00 - 2:20 | Break | |

2:20 - 3:00 | Subsetting | |

3:00 - 3:15 | Exercise 3 | Solution |

3:15 - 3:30 | Break | |

3:30 - 4:10 | Control Structures & Functions | |

4:10 - 4:40 | Exercise 4 | Solution |

4:40 - 5:00 | Packages & Closing Remarks |

### DAY 2 - April 27

Time | ||
---|---|---|

10:00 - 11:15 | Exploratory Data Analysis | Shell Code, Complete Code |

11:15 - 11:45 | Exercise 1 | |

11:45 - 12:15 | Lunch | |

12:15 - 12:45 | Discuss Exercise 1 Solutions | Solution Code |

12:45 - 2:15 | Linear Regression | Shell Code, Complete Code |

2:15 - 2:45 | Exercise 2 | |

2:45 - 3:00 | Break | |

3:00 - 3:30 | Discuss Exercise 2 Solutions | Solution Code |

3:30 - 4:00 | Logistic Regression | Shell Code, Complete Code |

# Pre-Workshop Instructions

### Step 1: Download and install R

First, visit The R Project for Statistical Computing. Click on `CRAN`

under the Download section on the left-hand side of the page. Then, click on any of the nearby websites under the USA section near the bottom of the page. For example, the link from the University of California, Berkley, CA or University of California, Los Angeles, CA are both fine. Download R for your platform (Linux, Mac, or Windows), open the downloaded file and follow the instructions.

### Step 2: Download and install RStudio

RStudio is a set of integrated tools designed to help you be more productive with R. Also, it is far more user-friendly than base R. You will be doing essentially all of your programming in RStudio. To download RStudio, visit the download page, scroll down to “Installers for Supported Platforms,” and click on the appropriate installer for your platform. Finally, open the downloaded file and follow the instructions.

# Authors

Chris Galbraith (galbraic@uci.edu)

Micah Jackson (gmicahjackson@gmail.com)