13-14 Nov 2018
9:30 am - 4:30 pm
Instructors: Tanya Major, Antje Lubcke, Mik Black, Alana Alexander, Riku Takei
Helpers: Ruth Topless, Mandy Phipps-Green
Data Carpentry develops and teaches workshops on the fundamental data skills needed to conduct research. Its target audience is researchers who have little to no prior computational experience, and 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 graduate students and other researchers. You don't need to have any previous knowledge of the tools that will be presented at the workshop.
Where: Otago Business School. Get directions with OpenStreetMap or Google Maps.
When: 13-14 Nov 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.
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 carpentries@otago.ac.nz for more information.
Please be sure to complete these surveys before and after the workshop.
Before starting | Pre-workshop survey |
9:30 | Data organization in spreadsheets |
10:30 | Coffee |
11:00 | Data organization in spreadsheets cont. |
12:00 | Lunch break |
13:00 | Introduction to R |
14:30 | Coffee | 15:00 | Introduction to R cont. | </tr>
16:00 | Wrap-up |
16:30 | END |
9:30 | Continuation of R: data analysis & visualization |
10:30 | Coffee |
11:00 | Continuation of R: data analysis & visualization |
12:00 | Lunch break |
13:00 | Digital Lab Notebooks |
14:30 | Coffee |
15:00 | Digital Lab Notebooks cont |
16:00 | Wrap-up |
16:15 | Post-workshop survey |
16:30 | 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.
When you're writing code, it's nice to have a text editor that is
optimized for writing code, with features like automatic
color-coding of key words. The default text editor on macOS and
Linux is usually set to Vim, which is not famous for being
intuitive. If you accidentally find yourself stuck in it, try
typing the escape key, followed by :q!
(colon, lower-case 'q',
exclamation mark), then hitting Return to return to the shell.
nano is a basic editor and the default that instructors use in the workshop. It is installed along with Git.
Others editors that you can use are Notepad++ or Sublime Text. Be aware that you must add its installation directory to your system path. Please ask your instructor to help you do this.
nano is a basic editor and the default that instructors use in the workshop. See the Git installation video tutorial for an example on how to open nano. It should be pre-installed.
Others editors that you can use are Text Wrangler or Sublime Text.
nano is a basic editor and the default that instructors use in the workshop. It should be pre-installed.
Others editors that you can use are Gedit, Kate or Sublime Text.
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.