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.

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.

Where: Meyerhofstrasse 1, 69117 Heidelberg. Get directions with OpenStreetMap or Google Maps.

When: 28-30 January 2020. 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:

• The room is wheelchair / scooter accessible.
• Accessible restrooms are available.

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 tbyhdgs@gmail.com 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

1. Data organization in spreadsheets

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.
• List of common formatting problems.
  • You can find an example tidy data here.

2. Introduction to R

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

• Exercises
• Summary of R basics

3. Data manipulation with dplyr

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

• Exercises
• Summary of dplyr functions and their equivalent in base R

Day 2

4. Data visualisation with ggplot2

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

• Exercises
• Data to Viz website helps you choose appropriate visualisations for your data (and also some caveats to think about when making a graph!).

Day 3

6. Exploratory RNAseq data analysis in R

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:

• Reshaping data
• Combining tables

Further reading:

• Conesa et al. (2016) A survey of best practices for RNA-seq data analysis, Genome Biology 17, 13
• Jake Lever, Martin Krzywinski & Naomi Altman (2017) Principal component analysis, Nature Methods 14, 641–642
• Naomi Altman & Martin Krzywinski (2017) Clustering, Nature Methods 14, 545–546

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

---

Further resources

• Cheatsheets for dplyr, ggplot2 and more

Extra materials/books:

• Exploratory Data Analysis with R by Roger D. Peng - covers principles of exploratory data analysis, including visualisation, clustering (hierarchical, k-means) and dimensionality reduction (PCA)
• R for Data Science by Garrett Grolemund & Hadley Wickham - learn more about using the "tidyverse" package for data analysis
• Modern Statistics for Modern Biology by Susan Holmes & Wolfgang Huber - covers many aspects of data analysis relevant for biology from statistical modelling to image analysis
• Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie & Robert Tibshirani - introduction to machine learning using R
  • Also see this course material for a practical introduction to this topic
• Statistical Rethinking by Richard McElreath (not freely available) - an introduction to statistical modelling using R
  • Lectures for this course are freely available here

---

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.

Please visit the setup instructions for each lesson that will be covered in your workshop:

• Spreadsheets
• OpenRefine
• SQL
• R and RStudio