October 1st - 4th, 2018
9 AM - noon
Instructors: Jose Hernandez, Valentina Staneva, Cecilia Noecker
Helpers: Aji John, Dan McCloy, HJ Kim
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: WRF Data Science Studio, 6th floor Physics/Astronomy Tower, University of Washington, 3910 15th Ave NE, Seattle, WA, 98105. Get directions with OpenStreetMap or Google Maps.
When: October 1st - 4th, 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 arokem@uw.edu for more information.
Please be sure to complete these surveys before and after the workshop.
| Before starting | Pre-workshop survey |
| 09:00 | Introduction to R |
| 10:00 | Coffee break |
| 10:15 | R (continued) |
| 09:00 | Starting with data |
| 10:00 | Coffee break |
| 10:15 | Data (continued) |
| 09:00 | Aggregating and analyzing data with dplyr |
| 10:00 | Coffee break |
| 10:15 | dplyr (continued) |
| 09:00 | Data visualization with ggplot2 (continued) |
| 10:00 | Coffee break |
| 10:15 | Visualization (continued) |
| Before leaving | Post-workshop survey |
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.
Bash is a commonly-used shell that gives you the power to do simple tasks more quickly.
cmd and press [Enter])setx HOME "%USERPROFILE%"
SUCCESS: Specified value was saved.exit then pressing [Enter]This will provide you with both Git and Bash in the Git Bash program.
The default shell in all versions of macOS is Bash, so no
need to install anything. You access Bash from the Terminal
(found in
/Applications/Utilities).
See the Git installation video tutorial
for an example on how to open the Terminal.
You may want to keep
Terminal in your dock for this workshop.
The default shell is usually Bash, but if your
machine is set up differently you can run it by opening a
terminal and typing bash. There is no need to
install anything.
Git is a version control system that lets you track who made changes to what when and has options for easily updating a shared or public version of your code on github.com. You will need a supported web browser (current versions of Chrome, Firefox or Safari, or Internet Explorer version 9 or above).
You will need an account at github.com for parts of the Git lesson. Basic GitHub accounts are free. We encourage you to create a GitHub account if you don't have one already. Please consider what personal information you'd like to reveal. For example, you may want to review these instructions for keeping your email address private provided at GitHub.
Git should be installed on your computer as part of your Bash install (described above).
For OS X 10.9 and higher, install Git for Mac
by downloading and running the most recent "mavericks" installer from
this list.
After installing Git, there will not be anything in your /Applications folder,
as Git is a command line program.
For older versions of OS X (10.5-10.8) use the
most recent available installer labelled "snow-leopard"
available here.
If Git is not already available on your machine you can try to
install it via your distro's package manager. For Debian/Ubuntu run
sudo apt-get install git and for Fedora run
sudo dnf install git.
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. To install it, download the Data Carpentry Windows installer and double click on the file to run it. This installer requires an active internet connection.
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.
Python is a popular language for research computing, and great for general-purpose programming as well. Installing all of its research packages individually can be a bit difficult, so we recommend Anaconda, an all-in-one installer.
Regardless of how you choose to install it, please make sure you install Python version 3.x (e.g., 3.6 is fine).
We will teach Python using the Jupyter notebook, a programming environment that runs in a web browser. For this to work you will need a reasonably up-to-date browser. The current versions of the Chrome, Safari and Firefox browsers are all supported (some older browsers, including Internet Explorer version 9 and below, are not).
bash Anaconda3-and then press tab. The name of the file you just downloaded should appear. If it does not, navigate to the folder where you downloaded the file, for example with:
cd DownloadsThen, try again.
yes and
press enter to approve the license. Press enter to approve the
default location for the files. Type yes and
press enter to prepend Anaconda to your PATH
(this makes the Anaconda distribution the default Python).
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.
The Data Carpentry Windows Installer installs SQLite for Windows. If you used the installer to configure nano, you don't need to run it again.
SQLite comes pre-installed on macOS.
SQLite comes pre-installed on Linux.
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 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.