See all HSL Research & Data Services current workshops

Introduction to R 11/3

Data Preparation - Taming Wild Data with R 11/10

Data Visualization in R 11/17

**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, **4.0.2**. 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.

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.

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 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.

Excel Bites - Sample Excel File for All Sessions

Handouts for Each Date

**Data Analysis and Software Workshops**

**On-Demand Training: recorded workshops, tutorials and guides**

**Data Analysis and Visualization**

Topic | Category | Description | Format |
---|---|---|---|

Wrangling, exploration, and analysis with R: STAT 545 | R | Explore, clean, visualize and analyze data using R | Series of guides |

R for Data Science | R | Free e-book with excellent, engaging exercises | Online book |

RStudio Cloud | R | Create a free account to access Primers with live auto-graded exercises on several popular packages and features of R | Tutorials |

**Research, Statistical, and Office Software**

Topic | Category | Description | Format |
---|---|---|---|

SPSS Learning Modules (UCLA) | SPSS | Comprehensive set of guides that include SPSS fundamentals and advanced data analysis examples | Guide |

Excel 2019 Bible | Excel | Comprehensive guide for Excel 2019. Learn to incorporate templates, implement formulas, create pivot tables, analyze data, and more. (UVA only) | Online Book |

Learning Excel 2019 | Excel | Learn about creating and opening workbooks, entering text and numbers, working with formulas, basic formatting, inserting charts, and sharing and printing workbooks. (UVA only) | Online Course |

**See additional Tutorials of interest**

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 Fall 2020

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 Fall 2020

Topics including Geographic Mapping, Demographics, and more

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

Claude Moore Health Sciences Library

1350 Jefferson Park Avenue P.O. Box 800722

Charlottesville, VA 22908 (Directions)

Contact Us

Staff Directory

(434) 924-5444

Feedback

© 2020 by the Rector and Visitors of the University of Virginia

Copyright & Privacy

- Last Updated: Nov 23, 2020 9:14 AM
- URL: https://guides.hsl.virginia.edu/data
- Print Page