Research and Data Services

Data Support, Training, and Software

Workshops and Training

Upcoming Data Workshops

See all HSL Research & Data Services current workshops

Excel Workshops, Videos, and Training Materials

Upcoming Live Excel Workshops

Live workshops are currently being held via Zoom. All are free, though registration is required. Click on the links below to register:

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 Workshop Materials

Handouts and the Excel practice file from our "Excel Bites" workshops:

Additional Excel Training Resources

Excel and other Microsoft training is available through these UVA subscriptions: 

Set-up Instructions for R Workshops

Software Installations:

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

1. R:

You’ll need the most recent version of R, 4.0.2. 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.

2. RStudio:

Download and install RStudio Desktop version >= 1.3

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.

3. tidyverse package:

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:

Attaching packages

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.

  1. Best option Dual monitors side-by-side
  2. Second choice: Computer projected onto tv or large monitor
  3. Second choice: Two computers side-by-side
  4. Second choice: One computer (you) and one tv (instructor)
  5. Third choice: One computer (you) and one tablet (instructor)
  6. 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.

Unzipping/Extracting Files for Windows

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:

  1. 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.
  2. 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:
  3. Click once on the zip file, then right-mouse and choose Extract All:
  4. 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:

  5. 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: 
  6. 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:

  7. Now double-click on the file ending in .Rproj. This is a Project file for our workshop. Your RStudio on your computer should open.
  8. 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 (see A below). If you have not extracted the files correctly, you will see just the .proj file (see B below):

    A: correctly unzipped and ready to go



    B: NOT unzipped - you'll need to unzip/extract before proceeding:

Additional Data and Computing Workshops

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 

Past Workshops

Winter/Spring 2021

Data Analysis, Visualization, and Research Software Workshops

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 Analysis, Visualization, and Research Software Workshops

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 

Data Analysis, Visualization, and Research Software Workshops

Excel Bites: basic navigation
Excel Bites: formulas
Excel Bites: merging and separating data
Excel Bites: PivotTable
Excel Bites: PivotCharts

Winter/Spring 2020 

Data Analysis, Visualization, and Research Software Workshops

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 

Data Analysis, Visualization, and Research Software Workshops

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 

Data Analysis, Visualization, and Research Software Workshops

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 

Data Analysis, Visualization, and Research Software Workshops

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 

Data Analysis, Visualization, and Research Software Workshops

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

Data Analysis, Visualization, and Computing Workshops

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

Data Analysis, Visualization, and Computing Workshops

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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