September 11, 18, 25, October 2 & 9, 2019
8:30 a.m. - 12:00 p.m.
Instructors: Trisha Adamus, Srikanth Aravamuthan, Michael Liou, Clare Michaud
Helpers: Trisha Adamus, Michael Liou, Tobin Magle, Lauren Michael, Clare Michaud
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: BioCommons 110A/B, Steenbock Memorial Library, 550 Babcock Drive, Madison, WI 53715. Get directions with OpenStreetMap or Google Maps.
When: September 11, 18, 25, October 2 & 9, 2019. 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).
Code of Conduct: Everyone who participates in Carpentries activities is required to conform to the Code of Conduct. This document also outlines how to report an incident if needed.
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 contact@datascience.wisc.edu for more information.
Please be sure to complete these surveys before and after the workshop.
| Before | Pre-workshop survey |
| September 11 | Data Organization in Spreadsheets |
| Most research projects start with collecting data in a spreadsheet, and we tend to organize data in spreadsheets based on how we want to work with the data. In this lesson, you will first learn best practices for entering data and formatting data tables in spreadsheets. | |
| September 11 | OpenRefine for Data Cleaning |
| After an introduction to organizing your data in spreadsheets, you’ll learn how to clean data for easier and improved analysis using the tool OpenRefine. | |
| September 18 | Data Management with SQL |
| This lesson will go over using a relational database for the effective storage and use of your data. Using databases for your data storage ensures that your stored data isn’t affected when you run analyses on it. SQL (Structured Query Language) allows you to create databases as well as run queries on it. Learning SQL and the concepts behind relational databases are at the core of understanding how to do similar tasks using programming languages such as R or Python. | |
| September 25 | Introduction to R |
| In this lesson, you’ll first be introduced to how to use R and R Studio to analyze and organize your data. | |
| September 25 | Data Manipulation with R (dplyr) |
| In the second half of the lesson, you will learn how to use the popular ‘tidyverse’ package ‘dplyr’ to manipulate and begin to analyze your data. | |
| October 2 | Data Visualization with R (ggplot) |
| During the first portion of this lesson, you’ll learn how to generate visualizations from your data using the R package ggplot2. | |
| October 2 | Producing Reports with R (knitr) |
| The second portion of the lesson will focus on generating reports using R. Reports are a way to make your research more reproducible, whether that’s for you and your research group or for peer researchers whom you’ve made the data available to. | |
| October 9 | Open Coding Lab (optional) |
| Do you have questions about any of the tools that you’ve learned about over the last four weeks or how to apply them to your research? This week’s session will allow you to come to work on applying what you learned to your own data. The instructors from the previous weeks’ lessons will be available to help answer your questions and work with you on any issues. This week’s session is optional. | |
| END | 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.
For the spreadsheets lessons you will need to have a spreadsheet program, such as Microsoft Excel or the Calc program in OpenOffice . If you do not already have one of these programs installed, please follow the links above to install.
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.
Visit SqliteBrowser and download and install it. Version 3.11.0 or greater.
Visit SqliteBrowser and download and install it. Version 3.11.0 or greater.
Visit SqliteBrowser and download and install it. Version 3.11.0 or greater.
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 openrefine.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.
.Rmd file for Introduction to R material - and the generated html file.Rmd source here.zip file, which includes the data that we had used for the R portion of the workshop.