Jan 16-17, 2019
8:30am - 4:30pm
Instructors: Trisha Adamus, Erin Jonaitis, Maria Kamenetsky, Christina Koch
Helpers: Sarah Graves, Kim O'Keefe, Patrick Shriwise, Sailendharan Sudakaran, Heather Shimon
Registration is required and will be available just below, starting at 5:00pm on Monday, 10 December 2018. Make sure to read all details below before registering and to choose appropriately between UW-Madison’s Data Carpentry and Software Carpentry workshops.
This hands-on workshop teaches basic concepts, skills and tools for working more effectively with data. The workshop is for any researcher who has data they want to analyze, and no prior computational experience is required.
For researchers who have already been programming and seek to expand their capabilities, our UW-Madison Software Carpentry workshop (January 14-15) will likely be more appropriate. These two workshops are NOT intended to be taken back-to-back, and you can learn about future workshops at UW-Madison by joining the mailing list of UW-Madison’s Data Science Hub.
What is Data Carpentry? Data Carpentry develops and teaches workshops on the fundamental data skills needed to conduct research. Its lessons are domain specific, building on learners' existing knowledge to enable them to quickly apply skills learned to their own research. Participants will be encouraged to help one another and to apply what they have learned to their own research problems. For more information on what we teach and why, please see our paper "Good Enough Practices for Scientific Computing".
Who: The course is aimed at UW-Madison-affiliated students, faculty, staff, and other researchers.
Where: Orchard Room, Discovery Building, 330 N. Orchard St., Madison, WI, 53715. Get directions with OpenStreetMap or Google Maps.
When: Jan 16-17, 2019. Add to your Google Calendar.
Requirements: Participants must bring a laptop with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.) that they have administrative privileges on. They should have a few specific software packages installed (listed below). They are also required to abide by Data Carpentry's Code of Conduct.
Accessibility: We are committed to making this workshop accessible to everybody. The workshop organizers have checked that:
Materials will be provided in advance of the workshop and large-print handouts are available if needed by notifying the organizers in advance. If we can help making learning easier for you (e.g. sign-language interpreters, lactation facilities) please get in touch (using contact details below) and we will attempt to provide them.
Contact: Please email facilitator@datascience.wisc.edu for more information.
Please be sure to complete these surveys before and after the workshop.
| Before starting | Pre-workshop survey |
| 8:30 - 9:00am | Software set-up help |
| 9:00 - 9:15am | Introduction [ Slides ] |
| 9:15 - 10:30am | Data organization in spreadsheets |
| 10:30 - 10:45am | Break |
| 10:45am - 12:00pm | OpenRefine for data cleaning |
| 12:00 - 1:00pm | Lunch |
| 1:00 - 2:30pm | Data management with SQL |
| 2:30 - 2:45pm | Break |
| 2:45 - 4:30pm | Data management with SQL |
| 4:30 - 4:45pm | Wrap-Up |
| 4:45pm | END |
| 8:30 - 9:00 | Installation Help |
| 9:00 - 9:15 | Day 2 intro [Slides] |
| 9:15 - 10:30 | Intro to R |
| 10:30 - 10:45 | Break |
| 10:45 - 12:00 | Data manipulation with dplyr |
| 12:00 - 1:00 | Lunch Break |
| 1:00 - 2:30 | Data Visualization with ggplot2 |
| 2:30 - 2:45 | Coffee |
| 2:45 - 3:15 | Reproducible reports with R Markdown |
| 3:15 - 4:00 | Capstone Project [Solutions] |
| 4:00 - 4:15 | Wrap up [ Slides ] and fill out the post-workshop survey (see above) |
| END |
We will use this collaborative document for chatting, taking notes, and sharing URLs and bits of code.
To participate in a Data Carpentry workshop, you will need access to the software described below. In addition, you will need an up-to-date web browser.
We maintain a list of common issues that occur during installation as a reference for instructors that may be useful on the Configuration Problems and Solutions wiki page. If you need help with the setup, please come to Data Science Hub office hours on January 9th, 2019 (9:30-11:30) or January 10th, 2019 (3-5) in Hub Central of the Discovery Building.
R is a programming language that is especially powerful for data exploration, visualization, and statistical analysis. To interact with R, we use RStudio.
Install R by downloading and running this .exe file from CRAN. Also, please install the RStudio IDE. Note that if you have separate user and admin accounts, you should run the installers as administrator (right-click on .exe file and select "Run as administrator" instead of double-clicking). Otherwise problems may occur later, for example when installing R packages.
Install R by downloading and running this .pkg file from CRAN. Also, please install the RStudio IDE.
You can download the binary files for your distribution
from CRAN. Or
you can use your package manager (e.g. for Debian/Ubuntu
run sudo apt-get install r-base and for Fedora run
sudo dnf install R). Also, please install the
RStudio IDE.
SQL is a specialized programming language used with databases. We use a simple database manager called SQLite in our lessons.
Follow the link to install SQLite for Windows installs SQLite for Windows. You will also need to install the DB Browser for SQLite .
SQLite comes pre-installed on macOS but you will need to install the DB Browser for SQLite .
SQLite comes pre-installed on Linux but you will need to install the DB Browser for SQLite.
If you installed Anaconda, it also has a copy of SQLite
without support to readline.
Instructors will provide a workaround for it if needed.
For this lesson you will need OpenRefine and a web browser. Note: this is a Java program that runs on your machine (not in the cloud). It runs inside a web browser, but no web connection is needed.
Check that you have either the Firefox or the Chrome browser installed and set as your default browser. OpenRefine runs in your default browser. It will not run correctly in Internet Explorer.
Download software from http://openrefine.org/
Create a new directory called OpenRefine.
Unzip the downloaded file into the OpenRefine directory by right-clicking and selecting "Extract ...".
Go to your newly created OpenRefine directory.
Launch OpenRefine by clicking openrefine.exe (this will launch a command prompt window, but you can ignore that - just wait for OpenRefine to open in the browser).
If you are using a different browser, or if OpenRefine does not automatically open for you, point your browser at http://127.0.0.1:3333/ or http://localhost:3333 to use the program.
Check that you have either the Firefox or the Chrome browser installed and set as your default browser. OpenRefine runs in your default browser. It may not run correctly in Safari.
Download software from http://openrefine.org/.
Create a new directory called OpenRefine.
Unzip the downloaded file into the OpenRefine directory by double-clicking it.
Go to your newly created OpenRefine directory.
Launch OpenRefine by dragging the icon into the Applications folder.
Use Ctrl-click/Open ... to launch it.
If you are using a different browser, or if OpenRefine does not automatically open for you, point your browser at http://127.0.0.1:3333/ or http://localhost:3333 to use the program.
Check that you have either the Firefox or the Chrome browser installed and set as your default browser. OpenRefine runs in your default browser.
Download software from http://openrefine.org/.
Make a directory called OpenRefine.
Unzip the downloaded file into the OpenRefine directory.
Go to your newly created OpenRefine directory.
Launch OpenRefine by entering ./refine into the terminal within the OpenRefine directory.
If you are using a different browser, or if OpenRefine does not automatically open for you, point your browser at http://127.0.0.1:3333/ or http://localhost:3333 to use the program.