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R for Data: Online Courses and Tutorials

A research guide on using R to work with data.

LinkedIn Learning courses on R

Learning R (2019, 3 hours, beginner level)

https://www.linkedin.com/learning/learning-r-2 

(Free access to LinkedIn Learning courses with Bucknell login.)

Learn the basics of R and get started finding insights from your own data, in this course with professor and data scientist Barton Poulson. The lessons explain how to get started with R, including installing R, RStudio, and code packages that extend R’s power. You also see first-hand how to use R and RStudio for beginner-level data modeling, visualization, and statistical analysis. By the end of the course, you’ll have a thorough introduction to the power and flexibility of R, and understand how to leverage this tool to explore and analyze a wide variety of data.

  • Learning objectives:
  • Installing R and RStudio
  • Navigating the RStudio environment
  • Importing data from a spreadsheet
  • Working with the tidyverse
  • Piping commands with %>%
  • Visualizing data with R base graphics and ggplot2
  • Visualizing hierarchical clusters
  • Selecting cases and subgroups
  • Recoding variables
  • Calculating frequencies
  • Calculating descriptives
  • Calculating correlations
  • Computing a linear regression

R Essential Training Part 1: Wrangling and Visualizing Data (2020, 4 hours, beginner + intermediate)

https://www.linkedin.com/learning/r-essential-training-wrangling-and-visualizing-data

(Free access to LinkedIn Learning courses with Bucknell login.)

This training series provides a thorough introduction to R, with detailed instruction for installing and navigating R and RStudio and hands-on examples, from exploratory graphics to neural networks. In part one, instructor Barton Poulson shows how to get R and popular R packages up and running and start importing, cleaning, and converting data for analysis. He also shows how to create visualizations such as bar charts, histograms, and scatterplots and transform categorical, qualitative, and outlier data to best meet your research questions and the requirements of your algorithms.

Learning objectives:

  • Installing R
  • Entering data
  • Packages for R
  • Importing XLS, XML, and JSON data
  • Visualizing data with ggplot2
  • Creating charts, histograms, scatterplots, and graphs
  • Converting data
  • Filtering cases and subgroups
  • Recoding data
  • Creating scale scores

The course includes a video tutorial on built-in datasets in R (for hands-on practice): 

https://www.linkedin.com/learning/r-essential-training-wrangling-and-visualizing-data/r-s-built-in-datasets?u=50365505 

  • Edgar Anderson’s Iris Data (50 flowers from each of 3 species of iris with measurements; frequently used to model classification)
  • Titanic Dataset (categorical data on Titanic passengers, including their gender, age, cabin class, and survival)
  • Anscombe’s Quartet (4 small datasets with identical means and other summary statistics but drastically different data points; used to illustrate the importance of graphing data distribution when interpreting the results)

R Essential Training Part 2: Modeling Data (2020, 4 hours, beginner + intermediate)

https://www.linkedin.com/learning/r-essential-training-part-2-modeling-data 

(Free access to LinkedIn Learning courses with Bucknell login.)

This training series provides a thorough introduction to R, with detailed instruction for working with R and RStudio and hands-on examples, from exploratory graphics to neural networks. In part two, Modeling Data, instructor Barton Poulson shows how to compute statistics, analyze data, predict outcomes, and group and classify cases. These are the fundamental techniques you need to generate meaningful insights for your organization.

Learning objectives:

  • Computing frequencies and correlations
  • Computing descriptive statistics
  • Conducting an item analysis
  • Comparing proportions
  • Comparing paired means
  • Comparing multiple means
  • Predicting outcomes with linear and logistic regression
  • Grouping cases with k-means clustering
  • Classifying cases with k-nearest neighbors
  • Creating ensemble models