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Course Description

Background
R is a free, cross-platform – Windows, Mac, and Linux – programming language, designed specifically to facilitate data management, analysis, and visualization. Boasting vibrant development and support communities, R has become an indispensable tool for bioinformaticians, statisticians, and data scientists. Created with true beginRs in mind, this training will teach participants the fundamental, transferable skills needed to unleash R’s full potential for producing publication-worthy analyses and visualizations.

Objectives
Our goal is simple: to flatten the (learning) curve. Using transparent, illustrative examples and exercises, participants will get hands-on walkthroughs on:

  • Interfacing with R using RStudio;
  • Using RStudio’s built-in help function – ? – as well as resources for troubleshooting, including rdocumentation.org, cheat sheets, vignettes, YouTube channels, and stackexchange.com;
  • Creating project files;
  • Working with the RStudio command line;
  • Identifying and changing the current working file directory;
  • Variables – local vs. global – naming conventions, and assignment operators;
  • Writing their first R script and how to properly document their code via commenting;
  • Using the ‘$’ accessor function;
  • The most common data types, including character strings, numerical, integers, and logicals;
  • How to access data entries using [] and [[ ]];
  • The most common data structure types, including vectors, lists, factors, data frames, and tibbles;
  • Package libraries and how to install them;
  • Loading data into R and basic troubleshooting when importing data;
  • Data management, manipulation, subsetting, piping, and exploration using dplyr;
  • Creating and exporting highly customizable, publication-quality data visualizations with ggplot2;
  • Using R to perform statistical analyses, including simple linear regression, χ2 contingency table analysis, t-tests, and analysis of variance.

Supplementary Material (Time Permitting)

  • Using the “group_by()” and “summarize()” functions in concert to extract information from datasets that have been organized into relevant groupings;
  • The concept of “tidy” data, the “tidyverse”, and the tools for transforming untidy data into tidy data;
  • Basic “for” loops;
  • Advanced graphics packages and dynamic, interactive plots;
  • Using Github for script version control;
  • Basic machine learning applications using the “caret” package.

Highlights
We will work in RStudio, an integrated development environment (IDE) built specifically for R. A copy of all the scripts used in class to import and tidy data, create visualizations, and perform analyses will be provided to each participant at the end of the training.

Who Should Attend?

  • Those new to programming and data science techniques;
  • Anyone interested in learning the most fundamental, transferable skills in R;
  • Beginners looking for a free alternative to proprietary software applications like STATA, SAS, and MATLAB.

Prerequisites
Participants should be comfortable with basic computer skills.

Credit
Although no grades are given for courses, each participant will receive Continuing Education Units (CEUs) based on the number of contact hours. One CEU is equal to ten contact hours.   Upon completion of this course each participant will receive a certificate, showing completion of the workshop and 2.8 CEUs.

Refund Policy
Follow the link to review Workshop Refund Policy.

Notification

  • All cancellations must be received in writing via email to Ms. Carline Coote at registrar@faes.org.
  • Cancellations received after 4:00 pm (ET) on business days or received on non-business days are time marked for the following business day.
  • All refund payments will be processed by the start of the initial workshop.
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