The University of Washington eScience Institute

August 28th - 31st

9 AM - noon

Instructors: Vikas Pejaver, Dave Williams, Eurika Kaiser, Meredith Rawls, Callin Switzer, Aji John

Helpers: Ariel Rokem, Meredith Rawls, Jimmy O'Donnell, Tiernan Martin, Sam White, Cecilia Noecker, Ritvik Vasan, Callin Switzer, Chad Curtis, Shwetha Canchi Murali, Stephen Kaluzny

General Information

Software Carpentry and Data Carpentry aim to help researchers get their work done in less time and with less pain by teaching them basic research computing skills. This hands-on workshop will cover basic concepts and tools, including program design, version control, data management, and task automation. Participants will be encouraged to help one another and to apply what they have learned to their own research problems. The workshop will have two parallel tracks: a Software Carpentry track that will focus on tools for software development (with Python), and a Data Carpentry track that will focus on tools for data processing and data visualization (with R).

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. 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: August 28th - 31st. 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 Software 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 to-be-announced for more information.



Please be sure to complete these surveys before and after the workshop.

Pre-workshop Survey

Post-workshop Survey

Software Carpentry (Python)

Day 1

09:00 Automating tasks with the Unix shell
10:00 Coffee break
10:15 Unix shell (continued)

Day 2

09:00 Version control with git
10:00 Coffee break
10:15 Version control with git (continued)

Day 3

09:00 Programming with Python
10:00 Coffee break
10:15 Python (continued)

Day 4

09:00 Programming with Python (continued)
10:00 Coffee break
10:15 Python (continued)

Data Carpentry (R)

Day 1

09:00 Introduction to R
10:00 Coffee break
10:15 R (continued)

Day 2

09:00 Starting with data
10:00 Coffee break
10:15 Data (continued)

Day 3

09:00 Aggregating and analyzing data with dplyr
10:00 Coffee break
10:15 dplyr (continued)

Day 4

09:00 Data visualization with ggplot2 (continued)
10:00 Coffee break
10:15 Visualization (continued)

We will use this collaborative document for chatting, taking notes, and sharing URLs and bits of code.


Software Carpentry

The Unix Shell

  • Files and directories
  • History and tab completion
  • Pipes and redirection
  • Looping over files
  • Creating and running shell scripts
  • Finding things
  • Reference...

Programming in Python

  • Using libraries
  • Working with arrays
  • Reading and plotting data
  • Creating and using functions
  • Loops and conditionals
  • Defensive programming
  • Using Python from the command line
  • Reference...

Version Control with Git

  • Creating a repository
  • Recording changes to files: add, commit, ...
  • Viewing changes: status, diff, ...
  • Ignoring files
  • Working on the web: clone, pull, push, ...
  • Resolving conflicts
  • Open licenses
  • Where to host work, and why
  • Reference...

Data Carpentry

Introduction to R

  • Define the following terms as they relate to R: object, assign, call, function, arguments, options.
  • Assign values to objects in R.
  • Learn how to name objects
  • Use comments to inform script.
  • Solve simple arithmetic operations in R.
  • Call functions and use arguments to change their default options.
  • Inspect the content of vectors and manipulate their content.
  • Subset and extract values from vectors.
  • Analyze vectors with missing data.

Starting with data

  • Describe what a data frame is.
  • Load external data from a .csv file into a data frame in R.
  • Summarize the contents of a data frame in R.
  • Manipulate categorical data in R.
  • Change how character strings are handled in a data frame.
  • Format dates in R

Data aggregation with dplyr

  • Select certain columns in a data frame with the dplyr function select.
  • Select certain rows in a data frame according to filtering conditions with the dplyr function filter.
  • Link the output of one dplyr function to the input of another function with the ‘pipe’ operator %>%.
  • Add new columns to a data frame that are functions of existing columns with mutate.
  • Use the split-apply-combine concept for data analysis. Use summarize, group_by, and tally to split a data frame< into groups of observations, apply a summary statistics for each group, and then combine the results.
  • Reshape a data frame from long to wide format and back with the spread and gather commands from the tidyr package.
  • Export a data frame to a .csv file.

Data visualization with ggplot2

  • Produce scatter plots, boxplots, and time series plots using ggplot.
  • Set universal plot settings.
  • Describe what faceting is and apply faceting in ggplot.
  • Modify the aesthetics of an existing ggplot plot (including axis labels and color).
  • Build complex and customized plots from data in a data frame.


To participate in a Software 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.

The Bash Shell

Bash is a commonly-used shell that gives you the power to do simple tasks more quickly.


Video Tutorial
  1. Download the Git for Windows installer.
  2. Run the installer and follow the steps bellow:
    1. Click on "Next".
    2. Click on "Next".
    3. Keep "Use Git from the Windows Command Prompt" selected and click on "Next". If you forgot to do this programs that you need for the workshop will not work properly. If this happens rerun the installer and select the appropriate option.
    4. Click on "Next".
    5. Keep "Checkout Windows-style, commit Unix-style line endings" selected and click on "Next".
    6. Keep "Use Windows' default console window" selected and click on "Next".
    7. Click on "Install".
    8. Click on "Finish".
  3. If your "HOME" environment variable is not set (or you don't know what this is):
    1. Open command prompt (Open Start Menu then type cmd and press [Enter])
    2. Type the following line into the command prompt window exactly as shown:

      setx HOME "%USERPROFILE%"

    3. Press [Enter], you should see SUCCESS: Specified value was saved.
    4. Quit command prompt by typing 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 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 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).


Video Tutorial

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.

Text Editor

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.


Video Tutorial

nano is a basic editor and the default that instructors use in the workshop. To install it, download the Software 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).


Video Tutorial
  1. Open with your web browser.
  2. Download the Python 3 installer for Windows.
  3. Install Python 3 using all of the defaults for installation except make sure to check Make Anaconda the default Python.


Video Tutorial
  1. Open with your web browser.
  2. Download the Python 3 installer for OS X.
  3. Install Python 3 using all of the defaults for installation.


  1. Open with your web browser.
  2. Download the Python 3 installer for Linux.
    (The installation requires using the shell. If you aren't comfortable doing the installation yourself stop here and request help at the workshop.)
  3. Open a terminal window.
  4. Type
    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 Downloads
    Then, try again.
  5. Press enter. You will follow the text-only prompts. To move through the text, press the space key. Type 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).
  6. Close the terminal window.


R is a programming language that is especially powerful for data exploration, visualization, and statistical analysis. To interact with R, we use RStudio.


Video Tutorial

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.


Video Tutorial

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.