Summary and Schedule

This is a new lesson built with The Carpentries Workbench.

	Setup Instructions	Download files required for the lesson
Duration: 00h 00m	1. Course introduction	What is reproducible research? Why is it important?
Duration: 00h 30m	2. Good Practices	What good practices can to help us develop reproducible, reusable and computationally correct R code?
Duration: 02h 00m	3. Code readability	Why does code readability matter? How can I organise my code to be more readable? What types of documentation can I include to improve the readability of my code?
Duration: 03h 30m	4. Code structure	How can we best structure our R project? What are conventional places to store data, code, results and tests within our research project? :::
Duration: 05h 00m	5. Code documentation	How should we document our R code? Why is documentation important? What are the minimum elements of documentation needed to support reproducible research?
Duration: 06h 30m	6. Code correctness	How can we verify that our code is correct? How can we automate testing in R? What makes a “good” test? Which parts of our code should we prioritise for testing?
Duration: 08h 00m	7. Reproducible development environment	How can we manage R dependencies in our analysis projects?
Duration: 08h 30m	8. Wrap-up	What reproducible research practices have we covered in this course? What tools and practices should you learn next?
Duration: 08h 45m	Finish

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.

Code Setup

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We will be using an example project in the lesson. Download the file using the link below and extract it to your computer: - zip file of example project.

Software Setup

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Discussion

This lesson assumes you have R and RStudio installed on your computer. Please follow the setup instructions below.

Personal Laptop

To install R and RStudio on a personal laptop

Windows/Mac

• R can be downloaded here. Select the precompiled binary distribution for your operating system.

• RStudio is an environment for code development using R. It can be downloaded here. You will need the Desktop version for your computer; scroll to the bottom of the page for links.

University of Birmingham Managed Laptop

Windows

To install R and RStudio on a University managed Windows machine:

• Connect your computer to the university network and open the Software Centre from the Start Menu

• Search for and install “RStudio”.

• This will also automatically install R for Windows 4.3.1 and Rtools.

Mac

To install R and RStudio on a University managed Mac machine:

• Connect your computer to the university network and open the Software Centre from the Start Menu

• Search for and install “R Language”.

• Search for and install “RStudio”.

Packages

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The course teaches the tidyverse, which is a collection of R packages that are designed to make many common data analysis tasks easier. Please install this before the course. You can do this by starting Rstudio, and typing:

R

install.packages("tidyverse")

At the > prompt in the left hand window of RStudio. You may be prompted to select a mirror to use; either select one in the UK, or the “cloud” option at the start of the list.

R will download the packages that constitute the tidyverse, and then install them. This can take some time. You may get a prompt There are binary versions available but the source versions are later and asking if you want to install from sources packages which require compilation. You should answer no to this.

If you are using a mac you may be prompted whether you wish to install binary or source versions of the packages; you should select binary.

On Linux, several of the packages will be compiled from source. This can take several minutes. You may find that you need to install additional development libraries to allow this to happen.

There will be a number of messages displayed during installation. After the installation has completed you should see a message containing:

OUTPUT

** testing if installed package can be loaded
* DONE (tidyverse)

Or:

OUTPUT

package ‘tidyverse’ successfully unpacked and MD5 sums checked

Check Your Installation

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Type the following commands at the > prompt:

R

library(tidyverse)
ggplot(cars, aes(x=speed, y=dist)) + geom_point()

(any message about conflicts can be safely ignored) This should produce a plot in the lower right hand window of RStudio.