Welcome | Mastering R for Epidemiologic Research

Functional programming in R

Tue, 16 Mar 2021 00:00:00 +0000

Required content

Lecture and exercises: Functional programming in R (see below)
Slides

Want more practice? Try these suggested exercises

Exercises from R For Data Science, 1st Edition: iteration
- Exercises 21.5.3
  - Questions 1-5

Bonus material

Lecture: Functional programming in R

Exercises

You can find the material for the exercises in 09-purrr_basics on Posit Cloud

If you prefer to download the material locally, run this code:

masterepir::use_module("purrr_basics")

Videos

Videos for each section of the lecture are available at this YouTube playlist.

You can also watch the playlist (and skip around to different sections) here:

Quarto: Whole Game

Tue, 16 Mar 2021 00:00:00 +0000

Required content

Lecture and exercises: Quarto: Whole Game (see below). No slides.

Bonus material

The Quarto Website

Lecture: Quarto: Whole Game

Exercises

You can find the material for the exercises in 10-quarto_whole_game on Posit Cloud

If you prefer to download the material locally, run this code:

masterepir::use_module("quarto_whole_game")

Videos

Videos for each section of the lecture are available at this YouTube playlist.

You can also watch the playlist (and skip around to different sections) here:

Quarto: The Basics

Tue, 16 Mar 2021 00:00:00 +0000

Required content

Lecture and exercises: Quarto: The Basics (see below)
Slides

Bonus material

Lecture: Quarto: The Basics

Exercises

You can find the material for the exercises in 11-quarto_basics on Posit Cloud

If you prefer to download the material locally, run this code:

masterepir::use_module("quarto_basics")

Videos

Videos for each section of the lecture are available at this YouTube playlist.

You can also watch the playlist (and skip around to different sections) here:

What's Next?

Tue, 16 Mar 2021 00:00:00 +0000

Required content

Lecture: What’s Next? (see below)

Lecture: What’s Next?

Videos

Videos for each section of the lecture are available at this YouTube playlist.

01 - Best Practices in R and What’s Next

You can also watch the playlist (and skip around to different sections) here:

Types, Vectors, and Functions

Tue, 02 Mar 2021 00:00:00 +0000

Required content

Lecture and exercises: Types and functions (see below)
Slides

Want more practice? Try these suggested exercises

Exercises from R For Data Science: Functions
- Exercises 26.2.5
  - Questions 1 and 6
- Exercises 26.3.5
  - Question 1
- Exercises 26.4.4
  - Questions 1-3
- Exercises 26.5.1
  - Question 1

Bonus material

Lecture: Types and functions

Exercises

You can find the material for the exercises in 08-types_and_functions on Posit Cloud

If you prefer to download the material locally, run this code:

masterepir::use_module("types_and_functions")

Videos

Videos for each section of the lecture are available at this YouTube playlist.

You can also watch the playlist (and skip around to different sections) here:

Modeling data

Tue, 23 Feb 2021 00:00:00 +0000

Required content

Assignment 2: Modeling data (see below)
Slides
In addition to completing the tasks in the assignment, we must be able to run your code successfully on Posit Cloud. Use a blank slate and restart your R session regularly, running all of the code to make sure it works correctly. Regularly checking your code with a blank slate will make sure things work as you intend.

Bonus material

Tidy Models with R: Modeling in Base R

Lecture: Modeling data

Exercises

You can find the assignment under 07-modeling_and_broom.

Please complete the assignment on Posit Cloud. You do not need to formally submit the assignment; we can see your workspace, so the most recent state of your exercises.qmd file is what we will grade.

If you prefer to download the material locally, run this code:

masterepir::use_module("modeling_and_broom")

Videos

Watch this video to prepare for Assignment 2

Videos for each section of the lecture are available at this YouTube playlist.

Modeling in R

You can also watch the playlist (and skip around to different sections) here:

Data Science Whole Game

Tue, 23 Feb 2021 00:00:00 +0000

Required content

Assignment 3: Data Science Whole Game (see below)
In addition to completing the tasks in the assignment, we must be able to run your code successfully on Posit Cloud. Use a blank slate and restart your R session regularly, running all of the code to make sure it works correctly. Regularly checking your code with a blank slate will make sure things work as you intend.

Assignment: Data Science Whole Game

In a culmination of the tools we’ve learned so far, this assignment will take you through an entire analysis–the whole game of data science, so to speak–where you will predict survival on the Titanic.

You can find the assignment under 12-ds_whole_game on Posit Cloud

Please complete the assignment on Posit Cloud. You do not need to formally submit the assignment; we can see your workspace, so the most recent state of your project.qmd file is what we will grade.

If you prefer to download the material locally, run this code:

masterepir::use_module("ds_whole_game")

Tidy Tuesday

Tue, 23 Feb 2021 00:00:00 +0000

Required content

Assignment 4: Tidy Tuesday (see below)
In addition to completing the tasks in the assignment, we must be able to run your code successfully on Posit Cloud. Use a blank slate and restart your R session regularly, running all of the code to make sure it works correctly. Regularly checking your code with a blank slate will make sure things work as you intend.

Assignment: Tidy Tuesday

Assignment materials

You can find the material for the assignment in 13-tidy_tuesday on Posit Cloud

If you prefer to download the material locally, run this code:

masterepir::use_module("tidy_tuesday")

Videos

Watch this video to prepare for Assignment 4.

Videos for each section of the lecture are available at this YouTube playlist.

01 - Tidy Tuesday

You can also watch the playlist (and skip around to different sections) here:

Tidying data

Tue, 16 Feb 2021 00:00:00 +0000

Required content

Lecture and exercises: Tidying data (see below)
Slides

Want more practice? Try these suggested exercises

Exercises from R For Data Science, 1st Edition: Tidy data
- Exercises 12.3.3
  - Questions 1-4
- Exercises 12.4.3
  - Questions 1 and 2
- Exercises 12.5.1
  - Questions 1 and 2

Bonus material

Lecture: Tidying data

Exercises

You can find the material for the exercises in 06-tidyr_basics on Posit Cloud

If you prefer to download the material locally, run this code:

masterepir::use_module("tidyr_basics")

Videos

Videos for each section of the lecture are available at this YouTube playlist.

You can also watch the playlist (and skip around to different sections) here:

ggplot2 Recreation Challenge

Tue, 09 Feb 2021 00:00:00 +0000

Required content

Assignment 1: ggplot2 Recreation Challenge (see below).
Slides
In addition to completing the tasks in the assignment, we must be able to run your code successfully on Posit Cloud. Use a blank slate and restart your R session regularly, running all of the code to make sure it works correctly. Regularly checking your code with a blank slate will make sure things work as you intend.

Bonus material

Assignment 1: ggplot2 Recreation Challenge

Assignment materials

You can find the assignment under 05-ggplot_recreate_challenge.

masterepir::use_module("ggplot_recreate_challenge")

Videos

Watch this video to prepare for Assignment 1

Videos for each section of the lecture are available at this YouTube playlist.

01 - ggplot2 Recreation Challenge

You can also watch the playlist (and skip around to different sections) here:

Visualizing Data (the Basics)

Tue, 02 Feb 2021 00:00:00 +0000

Required content

Lecture and exercises: Visualizing data (the basics) (see below)
Slides

Bonus material

Lecture: Visualizing data (the basics)

Exercises

You can find the material for the exercises in 03-ggplot_basics on Posit Cloud

If you prefer to download the material locally, run this code:

masterepir::use_module("ggplot_basics")

Videos

Videos for each section of the lecture are available at this YouTube playlist.

You can also watch the playlist (and skip around to different sections) here:

Visualizing Data (Customization)

Tue, 02 Feb 2021 00:00:00 +0000

Required content

Lecture and exercises: Visualizing data (customization) (see below)
Slides

Want more practice? Try these suggested exercises

Exercises from R For Data Science: Data visualisation
- Exercises 2.5.5
  - Questions 1-7

Lecture: Visualizing data (customization)

Exercises

You can find the material for the exercises in 04-ggplot_customizing on Posit Cloud

If you prefer to download the material locally, run this code:

masterepir::use_module("ggplot_customizing")

Videos

Videos for each section of the lecture are available at this YouTube playlist.

You can also watch the playlist (and skip around to different sections) here:

Transforming Data

Tue, 26 Jan 2021 00:00:00 +0000

Required content

Lecture and exercises: Transforming data (see below)
Slides

Want more practice? Try these suggested exercises

Exercises from R For Data Science: Data transformation
- Exercises 4.2.5
  - Questions 1, 2, and 5
- Exercises 4.3.5
  - Questions 6 and 7
- Exercises 4.5.7
  - Questions 3 and 5

Bonus material

Lecture: Transforming data

Exercises

You can find the material for the exercises in 02-dplyr_5verbs on Posit Cloud

If you prefer to download the material locally, run this code:

masterepir::use_module("dplyr_5verbs")

Videos

Videos for each section of the lecture are available at this YouTube playlist.

You can also watch the playlist (and skip around to different sections) here:

Setup and Introduction

Tue, 19 Jan 2021 00:00:00 +0000

Required content

Lecture: Course Introduction (see below)
Install R and RStudio (see below).
Create an Posit Cloud account (a free acount is fine!) and join the course workspace. (see below)
R For Data Science: Introduction
STATS 545: R Basics

Install the software you need

For this course, we need to install R, RStudio, and the packages we’ll be using. This is a three step process!

Check out STAT 545: Install for more guidance.

Install the latest version of R.

Note: There are two types of installations for Mac users based on the type of processor chip you have: Intel or ARM (M* chips, like M1, M2, or M3) Check “About this Mac” on your computer and look for the Chip information. If you have an Intel chip, install the Intel 64-bit build; if you have an M* chip such as M1, M2, or M3, install the Apple silicon arm64 build.

Install the latest version of RStudio
Follow the instructions below to install all the packages we will be using in the course

Open RStudio and run this code in the console. NOTE: R may ask you to install source packages instead of binaries. (Do you want to install from sources the packages which need compilation? (Yes/no/cancel). Say “no” for now. We’ll circle back to what this means later.

options(repos = "https://cran.rstudio.com/")
install.packages("remotes")
remotes::install_github("malcolmbarrett/masterepir")

This will take some time as many packages will be downloaded. When complete, you’ll see DONE (masterepir) displayed in the console.

NOTE: You may encounter an error trying to install certain packages that require additional software. A common source of such errors is needing Rtools on Windows or xcode on Mac. Read more here on how to set these up on your local computer: https://r-pkgs.org/setup.html#setup-tools

Set up an Posit Cloud account

We’ll also be using Posit Cloud. Sign up for a free account. Then join the Posit Cloud course workspace (click on the Cloud icon at the top of the page). The Posit Cloud workspace will have all the R packages and course material you need. In other words, the course material will just work here without you having to do any setup! If you are having trouble with your local setup, we highly recommend just using this workspace.

Important: Please turn in all your assignments on Posit Cloud, even if you’re working on your local computer. For assignments, you don’t need to do anything to “submit.” We will have access to the most recent state of your assignment.

For grading, it’s critical that we can run your assignment. Make sure you restart your R session often while working (In RStudio, go to Session > Restart R) and re-run your code so you know it’s working properly. See “Start R with a Blank Slate” for more information.

Install suggested tools

I also recommend you do some additional setup on your local computer for future use. Note: These are already set up on Posit Cloud, so you don’t need to do anything there.

Install tinytex to use PDF output in Quarto

install.packages("tinytex")
tinytex::install_tinytex()

Some Mac users see an error along the lines of /usr/local/bin not writable when installing TinyTex. Follow these instructions if you encounter this error.

Set up a blank slate for RStudio sessions (this helps with reproducibility)

install.packages("usethis")
usethis::use_blank_slate()

Set ragg as your graphics device for RStudio. This will improve your data visualization output in the RStudio viewer, particularly on Windows.

install.packages("ragg")

After installing ragg, set it as the backend for RStudio: Tools > Global Options > General > Graphics > Backend > AGG

Lecture: Course Introduction

Videos for each section of the lecture are available at this YouTube playlist.

You can also watch the playlist (and skip around to different sections) here:

Reading Data

Tue, 19 Jan 2021 00:00:00 +0000

Required content

Lecture and exercises: Reading data (see below)
Slides

Want more practice? Try these suggested exercises

Exercises from R For Data Science: Data Import
- Exercises 11.2.2
  - Questions 1, 4, and 5

Lecture: Reading data

Exercises

You can find the material for the exercises in 01-reading_data on Posit Cloud

If you prefer to download the material locally, run this code:

masterepir::use_module("reading_data")

Videos for each section of the lecture are available at this YouTube playlist.

You can also watch the playlist (and skip around to different sections) here:

Welcome | Mastering R for Epidemiologic Research

Functional programming in R

Required content

Suggested content

Want more practice? Try these suggested exercises

Bonus material

Lecture: Functional programming in R

Exercises

Videos

Quarto: Whole Game

Required content

Suggested content

Bonus material

Lecture: Quarto: Whole Game

Exercises

Videos

Quarto: The Basics

Required content

Bonus material

Lecture: Quarto: The Basics

Exercises

Videos

What's Next?

Required content

Suggested content

Lecture: What’s Next?

Videos

Types, Vectors, and Functions

Required content

Suggested content

Want more practice? Try these suggested exercises

Bonus material

Lecture: Types and functions

Exercises

Videos

Modeling data

Required content

Bonus material

Lecture: Modeling data

Exercises

Videos

Data Science Whole Game

Required content

Assignment: Data Science Whole Game

Tidy Tuesday

Required content

Assignment: Tidy Tuesday

Assignment materials

Videos

Tidying data

Required content

Suggested content

Want more practice? Try these suggested exercises

Bonus material

Lecture: Tidying data

Exercises

Videos

ggplot2 Recreation Challenge

Required content

Suggested content

Bonus material

Assignment 1: ggplot2 Recreation Challenge

Assignment materials

Videos

Visualizing Data (the Basics)

Required content

Suggested content

Bonus material

Lecture: Visualizing data (the basics)

Exercises

Videos

Visualizing Data (Customization)

Required content

Want more practice? Try these suggested exercises

Lecture: Visualizing data (customization)

Exercises

Videos

Transforming Data

Required content

Suggested content