September 27-28, 2018
09:00 - 17:00
Instructors: San Emmanuel James, Katrin Tirok
Helpers: Armstrong Dzomba, Tadi Gutsa, Vagner Fonseca, Yared Abera
Data Carpentry workshops are for any researcher who has data they want to analyze, and no prior computational experience is required. This hands-on workshop teaches basic concepts, skills and tools for working more effectively with data.
We will cover Data Organization in spreadsheets, Data Cleaning with Open Refine, and Data Analysis and Visualisation in R. Participants should bring their laptops and plan to participate actively. By the end of the workshop learners should be able to more effectively manage and analyze data and be able to apply the tools and approaches directly to their ongoing research.
For more information on what we teach and why, please see our paper "Best Practices for Scientific Computing".
Who: The course is aimed at graduate students and other researchers. Although the focus is on academic applications, anyone who would like to learn more about automating data processing could benefit from this material and is welcome to attend. We will work with data of animals caught in the Chihuahuan Desert in Arizona, USA, so you will be an ecologist for two days. However, the tools you will learn can easily be used with any other data. You don't need to have any previous knowledge of the tools that will be presented at the workshop. We will be teaching beginner-level material suitable for programming novices.
Where: K1 Seminar room, K-RITH Tower Buiding, Nelson Mandela Medical School. Get directions with OpenStreetMap or Google Maps.
When: September 27-28, 2018. 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.
Contact: Please email sanemmanueljames@gmail.com , katrintirok@gmail.com or mashamba-thompson@ukzn.ac.za for more information.
Registration:
Please register here.Surveys
Please be sure to complete these surveys before and after the workshop.
Day 1 - Thursday 27 Sept | Day 2 - Friday 28 Sept |
||
|---|---|---|---|
| 08:30 | Doors open/ Software installation issues | 08:30 | Doors open |
| 09:00 | Ice Breaker/Introductions | 09:00 | Warm up with R |
| 09:30 | Data organisation in Spreadsheets | 9:30 | Continuation of R: data analysis |
| 10:30 | Coffee | 10:30 | Coffee |
| 11:00 | Data Organisation/Data Cleaning with OpenRefine | 11:00 | Continuation of R: data analysis |
| 13:00 | Lunch | 13:00 | Lunch |
| 13:45 | Data Cleaning with OpenRefine | 13:45 | Continuation of R: data analysis/visualisation |
| 15:15 | Break | 15:15 | Break |
| 15:30 | Introduction to R | 15:30 | Continuation of R: visualisation |
| 17:00 | End | 17:00 | 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.
For this workshop you will need a spreadsheet program. Many people already have Microsoft Excel installed, and if you do, you're set! If you need a spreadsheet program, there are a few other options, like OpenOffice and LibreOffice. Install instructions for LibreOffice, which is free and open source, are here.
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 google-refine.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.
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 yum install R). Also, please install the
RStudio IDE.