Software Carpentry: University of Washington

University of Washington

Jan 15-16, 2015

9:00 am - 4:30 pm

Instructors: Russell Alleen-Willems, Becca Blakewood, Tracy Fuentes, Emilia Gan, Chungho Kim, Ana Malagon, Maria Mckinley, Dominik Moritz, Ben Marwick, Marina Oganyan, Jaclyn Saunders, Peter Schmiedeskamp, Thomas Sibley, Rachael Tatman, Tiffany Timbers, Sam White, Earle Wilson

Helpers: Esther Le Grezause, Tania Melo

General Information

Software Carpentry's mission is to help researchers become more productive by teaching them basic lab skills for computing. This two-day hands-on workshop will cover basic concepts and tools; participants will be encouraged to help one another and to apply what they have learned to their own research problems. The goal of the workshop is for participants to acquire skills to:

enable automation of repetitive tasks
work with structured data
improve the reproducibility of your research
version control code for reuse and collaboration.

This workshop is supported by the UW eScience Institute. Priority will thus be given to UW-affiliated students, staff and faculty.

Please use the Etherpads (R room pad, Python room pad) and the #swcuw hashtag on Twitter during class. We have a UW Software Carpentry email list that you can join to keep up with local plans, to get advice, ask questions, etc. after the workshop is over.

Who: The course is aimed at graduate students, staff, faculty and other researchers at UW. No previous experience with programming is required. If you do have experience in the topics in the syllabus and want to help, send us an email.

Where: WRF Data Science Studio, Physics/Astronomy Tower (6th Floor), University of Washington, Seattle, WA. The workshop will be held in two rooms in the WRF Data Science Studio on the 6th floor of the Physics/Astronomy Tower. Get directions with OpenStreetMap or Google Maps.

Requirements: Participants must bring a laptop with a few specific software packages installed in advance (listed below). They are also required to abide by Software Carpentry's Code of Conduct.

We find that workshops go a lot better if people come in groups, e.g., 4-5 people from one lab, half a dozen from another department or institute, etc., so that they are less inhibited about asking questions, and can support each other afterwards. So while individual sign-ups are welcome, we encourage you to sign-up with a friend.

No previous programming skills or experience is required. The lesson contents and exercises are aimed at novices.

Contact: Please mail bmarwick@uw.edu for more information.

Schedule

We are running two full concurrent sessions, one in each room. There are two key differences between the sessions. First is that one session will teach programming with Python and the other session will teach programming with R, all the other class content will be the same.

As a rough guide to choosing which language to learn, Python might be best for you if you're working in the natural or physical sciences, and if you're in the social sciences and humanities then R might be more valuable.

The second difference between the two sessions is that the instructors in the Python session mostly come from the natural and physical sciences, while the instructors in the R session mostly come from the social sciences and humanities. These is simply a convenient way to organise the lessons, and of course you're welcome to join whichever session you think will benefit you the most. The choice is completely up to you.

Day 1

09:00	Automating tasks with the Unix shell
10:30	Coffee break
12:00	Lunch break
13:00	Building programs with Python or R
14:30	Coffee break
16:00	Wrap-up

Day 2

09:00	Version control with Git
10:30	Coffee break
12:00	Lunch break
13:00	Managing data with SQL
14:30	Coffee break
16:00	Wrap-up

Syllabus

The Unix Shell

Files and directories: pwd, cd, ls, mkdir, ...
History and tab completion
Pipes and redirection
Looping over files
Creating and running shell scripts
Finding things: grep, find, ...
Reference...

Programming in Python

Using libraries
Working with arrays
Reading and plotting data
Creating and using functions
Loops and conditionals: for, if, else, ...
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: for, if, else
Using R 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...

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...

Setup

To participate in a Software Carpentry workshop, you will need working copies of the software described below. Please make sure to install everything (or at least to download the installers) before the start of your workshop. Limited time will be available before the start of the workshop to assist with installation.

If you haven't already, please register for a free account at GitHub. If you have an edu email, you can register for a free educational account which has some features usually only found in paid accounts. We will use this service as part of the lesson on version control.

The datasets used in the lessons can be downloaded from here: shell lesson data, R, Python and SQL lesson data

Overview

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 Mac OS X 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.

The Bash Shell

Bash is a commonly-used shell. Using a shell gives you more power to do more tasks more quickly with your computer.

Git

Git is a state-of-the-art version control system. It 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.

Python

Python is becoming very popular in scientific computing, and it's a great language for teaching general programming concepts due to its easy-to-read syntax. We teach with Python version 2.7, since it is still the most widely used. Installing all the scientific packages for Python individually can be a bit difficult, so we recommend an all-in-one installer.

R

R is a programming language that is especially powerful for data exploration, visualization, and statistical analysis. To interact with R, we will use RStudio, an interactive development environment (IDE).

SQL

SQL is a specialized programming language used with databases. We use a simple database manager called SQLite, either directly or through a browser plugin.

Windows

Python

Download and install Anaconda.
Use all of the defaults for installation except make sure to check Make Anaconda the default Python.

Git Bash

Install Git for Windows by download and running the installer. This will provide you with both Git and Bash in the Git Bash program.

Software Carpentry Installer

This installer requires an active internet connection

After installing Python and Git Bash:

Download the installer.
If the file opens directly in the browser select File→Save Page As to download it to your computer.
Double click on the file to run it.

Editor

nano is the editor installed by the Software Carpentry Installer, it is a basic editor integrated into the lesson material.

Notepad++ is a popular free code editor for Windows. Be aware that you must add its installation directory to your system path in order to launch it from the command line (or have other tools like Git launch it for you). Please ask your instructor to help you do this.

R

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

SQLite

Download the sqlite3 program and put it in the directory where you are running examples. Alternatively, you may install the Firefox SQLite browser plugin described below.

Mac OS X

Bash

The default shell in all versions of Mac OS X is bash, so no need to install anything. You access bash from the Terminal (found in /Applications/Utilities). You may want to keep Terminal in your dock for this workshop.

Editor

We recommend Text Wrangler or Sublime Text. In a pinch, you can use nano, which should be pre-installed.

Git

For OS X 10.8 and higher, install Git for Mac by downloading and running the installer. For older versions of OS X (10.5-10.7) use the most recent available installer for your OS available here. Use the Leopard installer for 10.5 and the Snow Leopard installer for 10.6-10.7.

Python

Download and install Anaconda.
Use all of the defaults for installation except make sure to check Make Anaconda the default Python.
Troubleshooting: If you get the following error message "ValueError unknown locale: UTF-8", see instructions on how to fix it here.

R

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

SQLite

sqlite3 comes pre-installed on Mac OS X. Alternatively, you may install the Firefox SQLite browser plugin described below.

Linux

Bash

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

If Git is not already available on your machine you can try to install it via your distro's package manager (e.g. apt-get or yum).

Editor

Kate is one option for Linux users. In a pinch, you can use nano, which should be pre-installed.

R

You can download the binary files for your distribution from CRAN. Or you can use your package manager, e.g. for Debian/Ubuntu run apt-get install r-base or yum install R. Also, please install the RStudio IDE.

SQLite

sqlite3 comes pre-installed on Linux. Alternatively, you may install the Firefox SQLite browser plugin described below.

Python

We recommend the all-in-one scientific Python installer Anaconda. (Installation requires using the shell and if you aren't comfortable doing the installation yourself just download the installer and we'll help you at the boot camp.)

Download the installer that matches your operating system and save it in your home folder.
Open a terminal window.
Type
```
bash Anaconda-
```
and then press tab. The name of the file you just downloaded should appear.
Press enter. You will follow the text-only prompts. When there is a colon at the bottom of the screen press the down arrow to move down through the text. 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).

All operating systems

Firefox SQLite Plugin

Instead of using sqlite3 from the command line, you may use this plugin for Firefox instead. To install it:

Start Firefox.
Go to the plugin homepage.
Click the "Add Now" button.
Click "Install Now" on the dialog that appears after the download completes.
Restart Firefox when prompted.
To check that the plugin installed correctly, select "SQLite Manager" from the "Tools" menu.

In newer versions of Firefox, the menu bar isn't always displayed. To make it appear, use the Alt key next to the space bar on your keyboard, or consult the support page from Firefox for additional help.