29-31 Jan 2019
9:00 - 17:30
Instructors: Florian Huber, Hugo Tavares, Thea Van Rossum, Georg Zeller
Helpers: Toby Hodges, Malvika Sharan
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.
On the first two days of this workshop we will cover basic skills in data organisation and analysis, using the statistical programming language R. On the third day we will introduce other advanced analysis methods in R using a transcriptomic dataset (although any researcher with an interest in multi-dimensional data will also benefit from the materials covered on this day).
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.
When: 29-31 Jan 2019. Add to your Google Calendar.
Requirements: All of the software necessary for this workshop is available on our training computers. However, participants are welcome to bring their own laptop. If you bring your own laptop, please see the setup instructions below). Participants 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 email@example.com for more information.
Please be sure to complete these surveys before and after the workshop.
We will use this collaborative document for chatting, taking notes, and sharing URLs and bits of code.
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.
This lesson will cover the very basics of using R with RStudio.
This lesson will cover some functions to effectively manipulate and summarise
tabular data using the
This lesson teaches you how to use the
ggplot2 R package to make
a wide range of plot types.
This lesson will briefly cover how to use the
dbplyr package to
connect and query databases.
In this session we will apply the concepts learned so far to a worked example of an exploratory data analysis of transcriptomic data.
During the lesson, we will also learn a few more tricks in R, including:
To participate in this 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.
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.
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, RMariaDB