General Information

Data Carpentry workshops are for any researcher who has data they want to analyze, and no prior computational experience is required. This hands-on workshop teaches basic concepts, skills and tools for working more effectively with data. We will cover Data organization in spreadsheets, and learn how to use the statistical program R. If time allows we will also talk about interacting with databases from R. 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 "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: Bioinformatics Training Room, Craik-Marshall Building, Downing Site. Get directions with OpenStreetMap or Google Maps.

When: Feb 19-20, 2019. Add to your Google Calendar.

Requirements: All the software needed for this workshop is available on the training room's computers. However, participants can bring their own 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. Please get in touch (using contact details below) if you have any special requirements.

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 and we will attempt to provide them.

Contact: Please email Gabriella Rustici <gr231@cam.ac.uk> for more information.

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Surveys

Please be sure to complete this survey after the workshop: Cambridge training survey

We would also appreciate if you filled in these surveys for the Data Carpentry community:

Data carpentry pre-workshop Survey

Data carpentry post-workshop Survey

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

Schedule

Day 1

Data organization in spreadsheets (Hugo)

Digital data recording often starts with a spreadsheet software (e.g. Excel). For an effective data analysis, it's crucial to start with a well structured and formatted dataset. Because of this, before diving into R, we will start by having a discussion about common issues that should be considered when recording data in spreadsheets.

• Download data for this lesson here.
• Find detailed materials here. In particular, look at the list of common formatting problems.
  • You can find an example tidy data here.

Introduction to R (James)

This lesson will cover the very basics of using R with RStudio.

• Overview of R and Rstudio
• Introduction to R
• Working with tabular data in R

Exercises

Data manipulation in R (Martin)

This lesson will cover some functions to effectively manipulate and summarise tabular data using the dplyr package.

• Data manipulation using the R package dplyr

Exercises

Day 2

Data visualisation in R (Crina)

This lesson teaches you how to use the ggplot2 R package to make a wide range of plot types.

• Data visualisation with ggplot2

Exercises

Optional: interacting with databases in R (Hugo)

This lesson will briefly cover how to use the dbplyr package to connect and query databases.

• Using the dbplyr package to access databases
• Example of accessing ENSEMBL database

Further resources

• Summary of R basics
• Summary of dplyr functions and their equivalent in base R
• Cheatsheets for dplyr, ggplot2 and more
• Data to Viz website helps you choose appropriate visualisations for your data (and also some caveats to think about when making a graph!).

Extra materials/books:

• R for Data Science - a nice follow-up from this course focusing on "tidyverse" packages
• Introduction to Statistical Learning - an introductory book about machine learning using R
  • Also see this course material for a practical introduction to this topic
• Statistical Rethinking (not freely available) - an introduction book about statistical modelling using R
R packages that might be of interest:
1. naniar - R package to help summarise, visualise and manipulate missing data.
2. visdat - R package to get a first quick "visual" look at the data.
3. Bioconductor - repository for packages related to Bioinformatics. These packages are installed differently from the normal packages. Instructions on how to install them is available on each package's page. (see for example the DESeq2 package page).

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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.

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.

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.

R packages

We will use several R packages in the course, which you can install before the course. To install the packages open RStudio and on the upper menu go to "Tools > Install Packages". In the box that opens type tidyverse, gridExtra, dbplyr, RSQLite, RMySQL

If you have problems, we can help you during the course.