This workshop is a Data Carpentry-inspired lesson based on the ‘Data Carpentry: R for Data Analysis and Visualization of Ecological Data’ lesson, maintained by François Michonneau & Auriel Fournier. The most current version of this lesson can be found here.
Parts of this lesson are also derived from Data Science in the tidyverse by Charlotte Wickham, which is licensed under a Creative Commons Attribution 4.0 International License and based on a work at https://github.com/rstudio/master-the-tidyverse.
Data Carpentry’s aim is to teach researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain.
This is an introduction to R designed for participants with no programming experience. These lessons will cover an overview of the RStudio interface, basic R syntax, an introduction to the tidyverse package and ggplot, working with files, the structure of data frames, how to deal with factors, how to add/remove rows and columns, and how to calculate summary statistics from a data frame.
The term “R” is used to refer to both the programming language and the software that interprets the scripts written using it.
RStudio is currently a very popular way to not only write your R scripts but also to interact with the R software. To function correctly, RStudio needs R and therefore both need to be installed on your computer.
The learning curve might be steeper than with other software, but with R, anaylsis results do not rely on remembering a succession of pointing and clicking, but instead on a series of written commands, and that’s a good thing! So, if you want to redo your analysis because you collected more data, you don’t have to remember which button you clicked in which order to obtain your results, you just have to run your script again.
Working with scripts makes the steps you used in your analysis clear, and the code you write can be inspected by someone else who can give you feedback and spot mistakes.
Working with scripts forces you to have a deeper understanding of what you are doing, and facilitates your learning and comprehension of the methods you use.
Reproducibility is when someone else (including your future self) can obtain the same results from the same dataset when using the same analysis.
R integrates with other tools to generate manuscripts from your code. If you collect more data, or fix a mistake in your dataset, the figures and the statistical tests in your manuscript are updated automatically.
An increasing number of journals and funding agencies expect analyses to be reproducible, so knowing R will give you an edge with these requirements.
A package is the “fundamental unit of shareable code” and “bundles together code, data, documentation, and tests” Hadley Wickham, “R Packages”. With 10,000+ packages that can be installed to extend its capabilities, R provides a framework that allows you to combine statistical approaches from many scientific disciplines to best suit the analytical framework you need to analyze your data. For instance, R has packages for image analysis, GIS, time series, population genetics, and a lot more.
The skills you learn with R scale easily with the size of your dataset. Whether your dataset has hundreds or millions of lines, it won’t make much difference to you.
R is designed for data analysis. It comes with special data structures and data types that make handling of missing data and statistical factors convenient.
R can connect to spreadsheets, databases, and many other data formats, on your computer or on the web.
The plotting functionalities in R are endless, and allow you to adjust any aspect of your graph to convey most effectively the message from your data.
Thousands of people use R daily. Many of them are willing to help you through mailing lists and websites such as Stack Overflow.
Anyone can inspect the source code to see how R works. Because of this transparency, there is less chance for mistakes, and if you (or someone else) find some, you can report and fix bugs.
Data Carpentry’s teaching is hands-on, so participants are encouraged to use their own computers to ensure the proper setup of tools for an efficient workflow. These lessons assume no prior knowledge of the skills or tools, but working through this lesson requires working copies of the software described below. To most effectively use these materials, please make sure to download the data and install everything before working through this lesson.
Data files for the lesson are available and can be downloaded manually here: http://dx.doi.org/10.6084/m9.figshare.1314459
However, we will download them directly from R during the lessons when we need them.
R and RStudio are separate downloads and installations. R is the underlying statistical computing environment, but using R alone is no fun. RStudio is a graphical integrated development environment (IDE) that makes using R much easier and more interactive. You need to install R before you install RStudio. After installing both programs, you will need to install the tidyverse package from within RStudio. Follow the instructions below for your operating system, and then follow the instructions to install tidyverse.
sessionInfo(), which will also display which version of R you are running. Go on the CRAN website and check whether a more recent version is available. If so, please download and install it. You can check here for more information on how to remove old versions from your system if you wish to do so..exe file that was just downloadedsessionInfo(), which will also display which version of R you are running. Go on the CRAN website and check whether a more recent version is available. If so, please download and install it..pkg file for the latest R versionsudo apt-get install r-base, and for Fedora sudo yum install R), but we don’t recommend this approach as the versions provided by this are usually out of date. In any case, make sure you have at least R 3.3.1.sudo dpkg -i rstudio-x.yy.zzz-amd64.deb at the terminal).After installing R and RStudio, you need to install the tidyverse package.
install.packages(c("tidyverse"))The list of contributors to this lesson is available here.
Data Carpentry,
2017. License. Contributing.
Questions? Feedback?
Please file
an issue on GitHub.
On
Twitter: @datacarpentry