R is an open source language released in 2001 that’s ideal for data wrangling and data science. It has connectors to pretty every much every data source under the sun, allows you wrangle data like nobody’s business, build pretty much every type of model ever thought up, and visualise it in all the niftiest ways. These superlatives are not disingenuous, R really is that broad and amazing.
R is a vibrant ecosystem that enables people to extend, enhance, and replace any part of it. There are many paradigms in R to facilitate object-oriented programming, functional programming, and more. If you can write something in C++, FORTRAN, Python, or JavaScript–and of course, R!–you can write extensions for R.
There are currently more than eleven thousand extensions (referred to as packages) to R in the core ecosystem (which is a fancy word for the collected bits and pieces of R!) and two and a half thousand packages in the genomics ecosystem.
We’re also seeing emerging ecosystems and paradigms within CRAN. The tidyverse is one such ecosystem, focussed primarily on analysing tabular data, and it will be used in future works extensively.
The core ecosystem is CRAN, the Comprehensive R Archive Network. CRAN is maintained by some great people who put in place a large number of quality gates that an R package must adhere to in order to be made widely available. They then host these packages and do great things like daily re-runs of all package tests to ensure packages are still working. CRAN is the default source of packages for most R users.
If you use RStudio, you’ll use a mirror of CRAN hosted by RStudio. There are a number of these mirrors scattered over the globe to help reduce the load on the central servers. You can use another one of these mirrors, or even set-up your own internal CRAN.
R is an great language for doing data analysis, data science, and more. It has it’s quirks but the community around it is huge and is making R easier to adopt every day.
R as a programming language is brilliant at its core competencies – statistics and data visualisation. It’s also a great “glue” language, by which I mean that you can use it to perform computations in many different languages and combine the results smoothly. As a result, R enables you to be an effective data wrangler, data scientist, and/or data visualisation practitioner.
So, now we’ve done the hard sell, here’s some fun stuff… and you can’t even acuse us of being on commision…because R and RStudio are totally free (yes free) to download and use what with them being opensource and whatnot.
RStudio is a coding interface to R that makes it easier for you to be productive.
The interface will be split up into a top menu and then four panes, although only three may be visible when you first start RStudio.
The (bottom) left hand section is the console. This is where you can execute R code directly.
To use the console you type some code alongside the > and hit Enter for the code to be executed. The result will then appear underneath your line of code.
If the code you entered wasn’t a complete statement e.g. 1 + 2 +, when you hit Enter , you’ll get a new line only the > will now be a +. This indicates the code you’re writing is a continuation of the previous line. R will allow you to continue building up a complete chunk of code this way. It’ll run all the lines you entered as one block once it’s been completed.
If you want to clean your console and start afresh, hit Ctrl + L to remove whatever has been executed in the console this session.
You can use your up and down arrow keys to navigate through previous code you’ve written and executed.
RStudio allows you to create and work with files containing code. These files give you a way to store and manage your code.
The most common file types you might use are R files (.R) and rmarkdown files (.Rmd). You can create one of these files by going to File > New > R Script, the New File button, or with the hotkey combo of Ctrl + Shift + N .
In an R script you can type code and execute it by hitting Ctrl + Enter, or selecting the code to run and hitting the Run button.
You can execute all the code in a script by hitting Ctrl + Shift + Enter or hitting the Source button.
Whilst writing scripts or typing in our console, we can get help and be more productive by using code completion. Code completion will pick up from what we’ve typed so far and provide a navigable list of suggestions.
As we navigate through the list, it’ll provide help text where possible and then it will complete the code we were typing.
So far you’ve seen R as a scratch-pad (via the console) and for making an isolated script, but a lot of the time we have to put data, multiple scripts, documentation and more into a project.
An RStudio project is a folder with an extra file. This file can be used to open RStudio, with everything laid out like it was before you closed the project. It can store preferences to allow projects to vary from the way you normally do things.
You can, and should, create a new R project when embarking on a new area of work. To create a project go to File > New Project.
This will popup a dialogue that gives you the option to create a brand new project directory, create one from some existing directory you might already have, or create one with the content of a project in your source control system (we’ll talk about source control in a later book.)
Most commonly, you’ll want to create a new directory project. Once selected it’ll then give you the option to create an empty project, a Shiny project (a feature for creating amazing dynamic reports,) or an R package. You’ll normally select the empty projects. Once an option is selected, provide a name and where the project should go.
RStudio is a fantastic environment for learning R and writing R code going forward.
Using code completion can be a great help in finding what to write and how to write it.
Other areas of the RStudio interface will be introduced as we go forward but taking the time to get to know the environment now will help you be more productive in future.
Still not had enough? Here’s an optional extension task you could do if you need to look busy at work in the awkward five minutes before you can go and wash your mug to kill another five minutes before you can go home.