Introduction to R for Data Science Workshop

Day 1: Get Oriented and Learn to Walk With R

• R + RStudio: orientation and introduction to the software, how to run code, RStudio panes and tabs
• R language quick start: getting users quickly up and running with R syntax and R packages
• Data manipulation with the dplyr package: learning the core functions for creating subsets, selecting data and creating and altering variables
• Using the R pipe to create streamlined and easy to read workflows
• Creating data visualizations using the ggplot2 package

Day 2: Up and Running With R

• Reading and writing text, Excel, SAS/Stata/SPSS and other file types using readr, readxl and haven. This section also includes a discussion of accessing data from databases.
• Grouping, aggregating and joining data using the dplyr package
• Getting data into the right orientation – reshaping data with the tidyr package
• Creating more effective plots in ggplot2 with faceting, scales and the theme() function
• Fine-tuning data manipulation by working with strings, dates and categorical variables with the stringr, lubridate and forcats packages

Workshop Instructors

Our instructors work at ZevRoss Spatial Anlayis, a company focused on data science, statistics and data visualization. They conduct data analysis and statistics for a wide range of clients including some of the world’s largest public health agencies and Fortune 500 companies.

Zev Ross is president of ZevRoss Spatial Analysis and has authored or co-authored more than 60 scientific research papers and maintains a popular data science blog. Zev is an RStudio certified instructor and was invited by RStudio to deliver an R workshop at the 2020 RStudio Conference where he earned a teacher rating of 4.8 out of 5.

John Shukle is a data scientist/software engineer at ZevRoss Spatial Analysis. He has authored or co-authored more than 15 publiciations. John has been using R for over 20 years and has taught and designed a number of university courses, including “Introduction to R”, “Introductin to Data Analytics using R” and “Advanced Data Analytics in the Geosciences”.

We have been teaching R workshops for nearly a decade and our clients include UNICEF, New York City Department of Health, NIH, Freddie Mac, UCLA, Columbia University, Agriculture Canada and many others. These workshops have earned an average of 4.6 stars out of 5 in hundreds of anonymous reviews and 99% of the reviewers gave the workshops 4 or 5 stars.

Workshop Format

The workshop is designed to be held on-site at your institution or remotely using Zoom or your team’s software of choice and involves a mix of slides, collaborative hands-on material and exercises. Participants use their own laptops and will be provided access to a cloud version of the R and RStudio software for the course. To fit with busy schedules the workshop can be held on weekdays or weekends. The workshop is designed for up to 15 participants (but we can work with you if you have a smaller or larger group).

Feedback from our workshops

The delivery of material was excellent. It was especially effective when responding to questions with a demonstration on the spot.”

This course is well designed. The information and exercises flow in a logical order, done at the right speed, and well communicated. This course is perfect for a group of technically savvy individuals who might not have prior knowledge of R, but can pick up the information. Well done.”

Based on a great experience I had with an introductory R course that the instructor taught at a scientific conference a few years ago, I invited the instructor to deliver the introductory R workshop at Columbia University. The workshop was a perfect introduction for students and staff to this powerful, open source statistics and visualization software, and gave them the skills and confidence to get to work using R on their own data.”

People are still talking about the training you did for us! VERY POSITIVE IMPACT!! Thank you!!!!”

A student in my lab took the R course last week. He thought it was fantastic, and then used the knowledge to write a script to simplify and help in his analysis experiments he is doing for his project. He just told me that he used to be severely limited by the number of replicates he could analyze because of a problem handling the data – now that limitation is gone and he will be able to analyze much more data. Thank you for organizing the course.”