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programming with Data in R

Midwest Big Data Summer School

May 16 2022

Heike Hofmann, hofmann@iastate.edu.

In Introduction to R you get a head start with the statistical software R. R has gained a huge number of users in the last years and the number of methods from add-on packages is growing rapidly. The course will be data centric, with lots of different data sets that illustrate examples of the different techniques used for different problems.

The course will be a mix of instruction and follow-up exercises. You are encouraged to bring your own laptops, with software already loaded. A list of software is available to help you prepare your machine for the workshop.

Slides and timetable

Date	Notes	Lecture and Resources
1:30 – 2:50	Setting Up	This will make sure that everybody's system is up and running. You should try to install R ahead of time, so that this time can be used to address potential problems. 0-getting-started.R 0-getting-started.Rmd
	Motivating Example	First taste of R: Jumping right into it to get an idea of how to use R and what it is capable of. 1-example.R 1-example.Rmd penguins.csv styles.css
	Basics	Working with data and learning about vectors, indexing vectors, and additional statistical functions. 2-basics.R 2-basics.Rmd R Reference Card
2:50 – 3:00	Short Break
3:00 – 4:30	R Objects and Data Structures	Using and examining the other data structures that are available in R. 3-r-objects.R 3-r-objects.Rmd
	The tidyverse	A good conceptual framework is key for a principled analysis of data. The [tidyverse](https://cran.r-project.org/web/packages/tidyverse/vignettes/manifesto.html) is a set of R packages that does exactly that: a sane approach to data analysis 4-dplyr.R 4-dplyr.Rmd
	Examples for dplyr	Practice your dplyr chops while considering your happiness 5-dplyr-examples.Rmd

Learning outcomes

After the end of the this one course, we expect you to be able to do the following:

• Use R for scientific/statistical calculations
• Be able to create or read in data and have the ability to manipulate the data accordingly
• Have the ability to explore data set characteristics and calculate summary statistics for real data sets
• Use the help functionality to find the functions you need to do what you want to do
• Install, use, and search for helpful external packages
• How to use basic programming constructs to make working with data easier
• Create documents that contain your R code/output/results and convert between HTML/pdf/Word at the click of a button.

Getting Ready on your own machine

You can bring your own laptop and work on it, if you like to. In order to keep up with everybody else, we're asking that you start installing the following pieces of software ahead of coming to the workshop:

• Install the newest R version (4.0.x) (from www.r-project.org)
• It is recommended that you install the latest version of RStudio (from www.rstudio.org)

Useful links

Recommended Reading:

• R for ecologists, yes, even though you might not be an ecologists, this is still good.
• RStudio resources, RStudio provides a lot of fantastic resources, in particular the cheat sheet series.
• DataCamp courses, provide in-depth, learn at your own speed, courses to learn R and python for data science.

This work is licensed under a Creative Commons Attribution 3.0 Unported License.