Washington State University

October 25-26, 2018

9:00 am - 4:30 pm

Instructors: Michael Meyer, Sarah Murphy, Nicholas Potter, Jordan Munson, Devi Gurung

Helpers: Matt Brousil, Shima Bahramvash Shams, Brett Vanderwerff, Alli Cramer, Yuanhong Song, Tung Nguyen, Menqi Zhao, Fidel Maureira Sotomayor, Joe Patten

Registration

Registration is now full.

General Information

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: Room 202, PACCAR building (2001 Grimes Way), Pullman, WA. Get directions with OpenStreetMap or Google Maps.

When: October 25-26, 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 matthew.brousil@wsu.edu for more information.


Surveys

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

Pre-workshop Survey

Post-workshop Survey


Schedule

Day 1: Thursday, October 25

9:00Introduction
9:15 Data organization in spreadsheets
10:45Coffee break
11:00Data Cleaning with OpenRefine
12:00Lunch break
1:00Introduction to R or Introduction to Python
2:30Coffee break
2:45Starting with data in R or Python
4:15Wrap-Up

Day 2: Friday, October 26

9:00Go over practice problems
9:15 Manipulating data with R or Python
10:45Coffee break
11:00Data visualization with R or or Python
12:00Lunch break
1:00Data Management with SQL
2:30Coffee break
2:45Data Management with SQL
4:15Wrap-Up

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


Syllabus

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...

Programming in R

  • Working with vectors and data frames
  • Reading and plotting data
  • Creating and using functions
  • Loops and conditionals
  • Using R from the command line
  • Reference...

Managing Data with SQL

  • Reading and sorting data
  • Filtering with where
  • Calculating new values on the fly
  • Handling missing values
  • Combining values using aggregation
  • Combining information from multiple tables using join
  • Creating, modifying, and deleting data
  • Programming with databases
  • Reference...

Open Refine

  • Introduction to OpenRefine
  • Importing data
  • Basic functions
  • Advanced Functions
  • Reference...

Setup

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 and a spreadsheet program such as Microsoft Excel or LibreOffice.

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.

Python

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).

Windows

Video Tutorial
  1. Open https://www.anaconda.com/download/#windows 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.

macOS

Video Tutorial
  1. Open https://www.anaconda.com/download/#macos with your web browser.
  2. Download the Python 3 installer for OS X.
  3. Install Python 3 using all of the defaults for installation.

Linux

  1. Open https://www.anaconda.com/download/#linux 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

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

Windows

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.

macOS

Video Tutorial

Install R by downloading and running this .pkg file from CRAN. Also, please install the RStudio IDE.

Linux

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.

SQLite

SQL is a specialized programming language used with databases. We use a simple database manager called SQLite in our lessons.

Windows

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.

macOS

SQLite comes pre-installed on macOS.

Linux

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.

OpenRefine

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.

Windows

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.

Mac

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.

Linux

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.