R for Data Science

R for Data Science

This is a practice run of a workshop I plan to give at the Kansas City Developer Conference in July and I hope at a couple of other conferences this year. It will probably be a little rough, and I am looking for feedback on how to improve it.

In this one-day introductory hands-on workshop you will learn how to analyze data using the R language to gain insight and understanding.

The workshop is based on selected parts of R for Data Science by Hadley Wickham and Garrett Grolemund, available free on-line at https://r4ds.had.co.nz/

Topics will include data visualization, data wrangling and transformation, exploratory data analysis, R programming concepts, and tools for communicating results.

We will begin with some data visualization using the versatile ggplot2 package, which is based on a set of consistent and coherent principles, known as the "grammar of graphics," for describing and building graphs.

Next you will learn about data transformations using the dplyr package. You will learn how to filter data tables into subsets, arrange the order of the rows, select specific columns, and mutate data tables by modifying or adding columns. You will also learn how to create grouped summaries like counts, totals, means, etc.

Then you will use what you have learned so far to do some exploratory data analysis. This involves formulating questions to answer, transforming and visualizing our data to try to answer those questions, and then using the results to refine our questions or ask new ones.

After this you will learn some R programming concepts, including writing R functions, working with vectors and lists, and various forms of iteration. You will also learn a little about functional programming in R using the purrr package.

RMarkdown is a powerful tool for creating fully reproducible documents that combine narrative prose, code, and the output of code. You will learn how you can use RMarkdown to communicate the results of a data analysis and generate static and dynamic documents, reports, books, dashboards etc. in many formats.

Finally you will learn about Shiny, an open source R web framework that lets you create web applications in R. This will give you a chance to practice many of the things you will have learned throughout the workshop.

No prior knowledge of R is required, but the workshop will assume you are generally numerically literate and have prior experience with at least one other programming language. The workshop will use R Studio Cloud, so all you need on your computer is a web browser. You may also want to install R and R Studio on your computer, but this isn't required.

We'll have at least one morning break, a break for lunch (on your own), and one afternoon break. Food and beverages are allowed in the room.