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**Upcoming Live R Workshops**

**All workshops are online via Zoom**

- Introduction to R - Tue Jan 18 10:00AM - 12:00PM Register
- Data Preparation in R - Taming Wild Data - Wed Jan 19 10:00AM - 12:00PM Register
- Data Visualization in R - Thu Jan 20 10:00AM - 12:00PM Register
- Data Wrangling in R - Fri Jan 21 10:00AM - 12:00PM Register
- Introduction to R - Wed Feb 2 11:00AM - 1:00PM Register
- Data Preparation in R - Taming Wild Data Wed Feb 9 11:00AM - 1:00PM Register
- Data Visualization in R - Wed Feb 16 2:00PM - 4:00PM Register
- Data Wrangling in R - Wed Feb 23 11:00AM - 1:00PM Register
- Essential Stats in R - Thu Feb 24 2:00PM - 4:00PM Register
- Linear Models in R - Fri Feb 25 2:00PM - 4:00PM Register
- Introduction to R - Mon Apr 11 10:00 AM - 12:00PM Registration opens Mon Mar 14
- Data Preparation in R - Taming Wild Data - Mon Apr 18 10:00AM - 12:00PM Registration opens Mon Mar 14
- Data Visualization in R - Mon Apr 25 10:00AM - 12:00PM Registration opens Mon Mar 14
- Data Wrangling in R - Mon Mat 2 10:00AM - 12:00PM Registration opens Mon Mar 14

**Additional R Training Resources**

Online Training: there are many excellent sources of R and RStudio instruction:

- RStudio Primers: Engaging online lessons to teach you introductory R skills
- "Data wrangling, exploration, and analysis with R" course material by Jenny Bryan for Stat 545: An excellent resource for learning R, dplyr, and ggplot2

Guides and Cheat Sheets

- RStudio's cheat sheets
- R 4 Data Science: a free e-book with excellent, engaging exercises

**RStudio Software at UVA**

RStudio is freely available to download and use, but it is also installed in the Health Sciences Library's Carter Classroom (first level). It also is available through UVA Labs and Classrooms (locations with RStudio), as well as the UVA RemoteApps service.

**Software Installations:**

Prior to your first workshop session, please follow the instructions below to install necessary software and to set-up your physical space.

You’ll need the most recent version of R, **4.1.2**. Download and install it for Windows or Mac. If you have a previous R installation, please check the version by opening R and typing R.version. If you have an older Mac OS, download the latest pkg file for your appropriate version of Mac OS.

Download and install **RStudio Desktop** version >= 1.4

R and RStudio are separate downloads and installations. **R** is the underlying statistical computing environment, but using R alone is no fun. **RStudio** is a graphical integrated development environment that makes using R much easier. You need R installed before you install RStudio.

Lastly, we will need to install several core packages needed for most lessons. Launch RStudio (RStudio, *not R itself*). Ensure that you have internet access, then copy and paste the following commands, one-at-a-time, into the **Console** panel (the lower-left panel, by default) and hit the Enter/Return key.

`install.packages("tidyverse")`

*A few notes*:

- Commands are case-sensitive.
- You must be connected to the internet.
- Even if you’ve installed these packages in the past, re-install the package to download the most recent version. Many of these packages are updated often, and we may use new features in the workshop that aren’t available in older versions.
- If you’re using Windows you might see errors about not having permission to modify the existing libraries – disregard these. You can avoid this by running RStudio as an administrator (right click the RStudio icon, then click “Run as Administrator”).
- The tidyverse package is a meta-package that automatically installs 8 commonly used packages for data analysis that all play well together (see tidyverse.org for more)

Check that you’ve installed everything correctly by closing and reopening RStudio and entering the following command at the console window.

`library(tidyverse)`

Don’t worry about any messages that look something like `the following objects are masked from ...`

, or `Warning message: package ... was build under R version ...`

Running the library(tidyverse) code may produce some notes or other output, but as long as you don’t get an error message, you’re good to go.

If you see output like this:

everything installed properly and is working. **You are all set!**

If you get a message that says something like: `Error in library(somePackageName) : there is no package called 'somePackageName'`

, then the required packages did not install correctly. Please do not hesitate to email the instructors prior to class if you are still having difficulty. In your email, please copy and paste what you typed in the console, and all of the output that streams by in the console.

**Physical Space:**

Because of these workshops’ online format, here are the best options for following along during class sessions. Most of the workshop consists of live coding, so the challenge will be how to simultaneously view the instructor’s screen and your screen given that the RStudio window is large and landscape format.

*Best option*Dual monitors side-by-side*Second choice:*Computer projected onto tv or large monitor*Second choice:*Two computers side-by-side*Second choice:*One computer (you) and one tv (instructor)*Third choice:*One computer (you) and one tablet (instructor)*When all else fails:*One computer (you & instructor) and PDF of printed codes

Please do not hesitate to email the instructors prior to class if you have questions about how best to set-up your workspace.

**How to Unzip (aka Extract) Workshop Files for Windows**

Files for our workshops are packaged together in a Zip file. You'll need to download AND fully unzip (aka extract) this file to get all the files for the session. Here's how:

- From the Workshops files page, click on the .zip file for the workshop. This will save it to your computer, typically to a Downloads folder.
- Open your File Explorer (the folder icon at the bottom of your Windows screen) and click on the Downloads folder. You should see the zip file you just downloaded:

- Click once on the zip file, then
**right-mouse**and choose**Extract All:**

- You'll be prompted where to save the extracted files (see below). You may want to Browse and save them to your Desktop or somewhere you can find them. Make sure to box marked "Show extracted files when complete" is checked:

- In your Windows Explorer, navigate to where you saved the files above. You should now see a "regular" (unzipped) folder with the workshop files:

If the folder still ends in .zip and your Windows Explorer has the pink Extract tab at top, you have not yet unzipped the file. Click on the Extract All button to unzip and save as above:

- Now that you have an unzipped folder, double-click it (e.g the folder with the workshop name like 01_Intro_R). You will now see the workshop files:

- Now double-click on the file ending in .
**Rproj**. This is a Project file for our workshop and should open in RStudio on your computer. - If you've unzipped/extracted the file correctly, you should see the .RProj file AND the additional files like skeleton, etc, in the lower right window of RStudio. If you have not extracted the files correctly, you will see just the .proj file in the lower right.

Let us know if you have any questions!

**Upcoming Live Excel Workshops**

Excel Bites are live demonstration sessions via Zoom for Excel beginners.

- Excel Bites: Navigation Basics Tue Jan 25 12:30 PM - 1:00 PM
- Excel Bites: Merging and Separating Data Thu Jan 27 12:30 PM - 1:00 PM
- Excel Bites: Formulas and Functions Tue Feb 1 12:30 PM - 1:00 PM
- Excel Bites: Simple Merging Using VLOOKUP Thu Feb 3 12:30 PM - 1:00 PM
- Excel Bites: PivotTables Tue Feb 8 12:30 PM - 1:00 PM
- Excel Bites: More PivotTables and PivotCharts Thu Feb 10 12:30 PM - 1:00 PM

**Recorded Excel Workshops**

Recordings of our "Excel Bites" (short, bite-sized Excel tutorials for beginners) are available below. For the accompanying handouts and exercises, see the links below under Workshop Materials.

- Excel Bites: Basic Navigation (24 min)
- Excel Bites: Merging and Separating Data (17 min)
- Excel Bites: Formulas (23 min)
- Excel Bites: Merging Data Between Sheets with VLOOKUP (11 min)
- Excel Bites: PivotTables (15 min)
- Excel Bites: PivotCharts (9 min)

**Upcoming Live SPSS Workshops**

No live workshops scheduled at this time

**SPSS Workshop Materials**

Introduction to SPSS (2014) posted materials from a previous workshop by UVA Library Research Data Services)

**Additional SPSS Training Resources**

There are many excellent online resources for learning SPSS:

- SPSS Resources from UCLA's Institute for Digital Research and Education's Statistical Consulting Group. Includes resources such as:
- SPSS Learning Modules (UCLA) - excellent guide, broken into sections like Fundamentals and Basic Data Management
- SPSS Class Notes (UCLA) - "This class is designed for people who are just starting to use SPSS. The class notes are the scripts for the class. The SPSS class notes do not contain any of the computer output. The class notes are not meant to be an SPSS textbook or a reference manual. However, it is possible for individuals to use the class notes to help them learn SPSS even if they don’t enroll in the class"

- SPSS Statistics Essential Training - in the UVA library's LinkedIn Learning account (UVA only)
- SPSS books and guides: perform a search in Virgo, the University's Library Catalog, for SPSS and review titles such as SPSS Statistics for Dummies, Using SPSS Syntax: A Beginner's Guide, Introductory Statistics for Health and Nursing Using SPSS and many more.

**SPSS Software at UVA**

There are several options for accessing SPSS at UVA for faculty, staff, students and sponsored accounts

- Download SPSS to your computer through the UVA Software Gateway (choose Teaching and Research edition). See additional helpful information from the UVA Libraries on installing SPSS.
- Access SPSS through the "Remote Apps" (virtual desktop, formerly known as The Hive) option through UVA ITS (computing)
- SPSS is available in the Health Sciences Library on the Carter Classroom computers only. Other library computers can access SPSS through the Remote Apps method above.

See additional free, online workshops on data analysis, visualization, research computing and more from our on-Grounds partners:

University Library Research Data Services + Sciences and Research Computing Workshops

Training on data analysis, statistics, computation, and library resources. Topics include:

Parallel Computing with Matlab || Data Science with Matlab || Introduction to Shiny ||

Data preparation in R with dplyr || Data Visualization in R with ggplot2 || Data Viz in Python with Matplotlib and Pandas || Scientific Writing with LaTeX/Overleaf || Using Zotero for Research || Using Dedoose for Qualitative Research

University Library Scholar's Lab

Topics including Geographic Mapping, Demographics, and more

Winter/Spring 2021

Data Visualization in R with ggplot2

Data Preparation: taming wild data with R

Data Wrangling in R

Excel Bites: basic navigation

Excel Bites: formulas

Excel Bites: merging and separating data

Excel Bites: PivotTable

Excel Bites: PivotCharts

Excel Bites: Macros

Introduction to Qualtrics

Introduction to R

Introduction to SPSS

R Bootcamp

Fall 2020

Data Visualization in R with ggplot2

Data Preparation: taming wild data with R

Excel Bites: basic navigation

Excel Bites: formulas

Excel Bites: merging and separating data

Excel Bites: PivotTable

Excel Bites: PivotCharts

Excel Bites: Macros

Introduction to Qualtrics

Introduction to R

Introduction to SPSS

Regression in R

Statistics in R

Summer 2020

Excel Bites: basic navigation

Excel Bites: formulas

Excel Bites: merging and separating data

Excel Bites: PivotTable

Excel Bites: PivotCharts

Winter/Spring 2020

Data Visualization in R with ggplot2

Data Preparation: taming wild data with R

Introduction to NCBI Resources

Introduction to Qualtrics

Introduction to R

Introduction to SPSS

R Code-In

Regression in R

Statistics in R

Fall 2019

Introduction to R

Data Visualization in R with ggplot2

Data Preparation: taming wild data with R

Power and Sample Size in R

Essential Skills for Data in Excel

Essential Statistics with R

Funding discovery workshop

Introduction to scientific image processing with Fiji/ImageJ

Automation of image processing with Fiji/ImageJ

Browsing Genes and Genomes with Ensembl and Ensembl Genomes

Introduction to QGIS

Introduction to Qualtrics

Regression in R

Qualitative Data Analysis and Introduction to Dedoose

Introduction to SPSS

Introduction to RNASeq

Machine Learning in R

Reproducible Research Using RMarkdown and GitHub

Moving R Programs to Rivanna

Managing R Libraries

Regression in R

Survival Analysis in R

Interactive Visualization with R using Shiny

Winter/Spring 2019

Introduction to R

Introduction to SPSS

Data Visualization in R with ggplot2

Data Preparation: Taming wild data with R

Essential Skills for Data in Excel

Essential Statistics with R

Power and Sample Size in R

Predictive Analytics with R

Managing R Libraries

Reproducible Research Using RMarkdown and GitHub

Interactive Visualization with R using Shiny

Regression in R

Introduction to RNASeq

Single cell RNASeq with Seurat

Introduction to QGIS

Qualitative Data Analysis and Introduction to Dedoose

Introduction to SAS

Funding Discovery Workshop

Introduction to Qualtrics

Introduction to scientific image processing with Fiji/ImageJ

Automation of image processing with Fiji/ImageJ

Introduction to NCBI Resources

Fall 2018

Introduction to R

Introduction to SPSS

Data Visualization in R with ggplot2

Data Preparation: Taming wild data with R

Essential Skills for Data in Excel

Essential Statistics with R

Introduction to QGIS

Introduction to Stata

Introduction to SAS

Funding Discovery Workshop

Introduction to Qualtrics

Introduction to scientific image processing with Fiji/ImageJ

SciFinder Skills Enhancement

Reaxys Medicinal Chemistry

Introduction to NCBI Resources

Regression in R

Automation of image processing with Fiji/ImageJ

Predictive Analytics with R

Building Shiny Web Applications in R

Winter/Spring 2018

Introduction to R

Introduction to SPSS

Data Manipulation in R with dplyr

Managing Your Spreadsheet Data

Interactive Visualization with R

Predictive Analytics with R

Funding Discovery Workshop

Survey Design with Analysis in Mind (Qualtrics)

Data Manipulation in R with dplyr

Essential Statistics with R

Using SPSS Syntax

Introduction to SAS

Building Shiny Web Applications in R

Data Visualization in R with ggplot2

Predictive Analytics with R

Essential Statistics with R

Using SPSS Syntax

Fall 2017

Coordinated under the UVA BioConnector branding, these workshops were led by SOM Research Computing, Health Sciences Library, Public Health Sciences, University Library, and Advanced Research Computing Services

Advanced Data Manipulation with R - 2017-10-03

Advanced Data Visualization with ggplot2 - 2017-10-30

Automated Image Analysis with ImageJ - 2017-10-19

Building Shiny Web Applications in R - 2017-04-18

Data cleansing in Python using Pandas and the Jupyter Notebook - 2017-10-09

Data visualization in Python - 2017-10-12

Essential Statistics with R - 2017-11-13

Introduction to Cloud Computing with AWS - 2017-09-26

Introduction to the Command Line - 2017-09-07

Introduction to Dedoose- 2017-10-31

Introduction to Docker Containers - 2017-10-31

Introduction to Git and GitHub 2017-09-22

Introduction to Ivy - 2017-09-19

Introduction to Matlab - 2017-11-07

Introduction to Python - 2017-09-05, 2017-09-11

Introduction to R - 2017-09-06, 2017-09-12

Introduction to Rivanna - 2017-09-21, 2017-11-01

Introduction to SAS - 2017-09-27

Introduction to Scientific Image Processing with Fiji/ImageJ, 2017-10-10

Introduction to SPSS - 2017-09-20

Organizing Your Spreadsheet Data - 2017-09-28, 2017-11-07

Overview of UVA Research Computing Resources - 2017-09-14

Power and Sample Size Analysis with R - 2017-10-12

Python Web apps using the Flask framework - 2017-10-23

R Package Development Tools - 2017-10-16

Reproducible Reporting with R and RMarkdown - 2017-11-6

Spring 2017

Coordinated under the UVA BioConnector branding, these workshops were led by SOM Research Computing, Health Sciences Library, Public Health Sciences, and the University Library

Advanced Data Manipulation with R - 2017-02-14

Advanced Data Visualization with R - 2017-02-21

Automated Image Analysis with ImageJ - 2017-03-29

Building Shiny Web Applications in R - 2017-04-18

Data cleansing in Python using Pandas and the Jupyter Notebook - 2017-03-15

Data visualization in Python using Matplotlib v. 2 and Bokeh - 2017-03-28

Essential Statistics with R - 2017-02-23

Introduction to Cloud Computing with AWS - 2017-04-04

Introduction to Python - 2017-02-09

Introduction to SAS - 2017-01-31

Introduction to the Command Line - 2017-01-26

Introduction to ArcGIS - 2017-03-27

Introduction to Dedoose - 2017-04-10

Introduction to SPSS - 2017-02-15

Managing Your Spreadsheet Data - 2017-03-14

Organizing Your Spreadsheet Data - 2017-01-24

Power and Sample Size Analysis with R - 2017-03-02

Python Web apps using the Flask framework - 2017-03-30

Quantitative analysis and visualization of medical images using Advanced Normalization Tools (ANTs) - 2017-04-19

R for Beginners - 2017-01-24

R for Beginners - 2017-02-02

Statistical Analysis with SAS - 2017-02-17

Survival Analysis in R with TCGA Data - 2017-03-31

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