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Step 0: Warming up

R is a fast growing open source contestant to commercial software packages like SAS, STATA and SPSS. The demand for R skills in the job marketing is rising rapidly, and recently companies such as Microsoft pledged their commitment to R as a lingua franca of Data Science.

Step 1: Setting up your machine

The easiest way to set-up R is by downloading a copy of it on your local computer from the Comprehensive R Archive Network (CRAN). You can choose between binaries for Linux, Mac and Windows.

Although you could consider working with the basic R console, we recommend you to install one of R’s integrated development environment (IDE). The most well known IDE is RStudio, which makes R coding much easier and faster as it allows you to type multiple lines of code, handle plots, install and maintain packages and navigate your programming environment much more productively. An alternative to RStudio is Architect, an eclipse-based workbench.

Step 2: Learn the basics of R language

You should start by understanding the basics of the language, libraries and data structure.

An intermediate R programming. An alternative learning tool is this online version of swirl where you can learn R in an environment similar to RStudio.

Next to these interactive learning environments, you can also choose to enroll in one of the Moocs available on Coursera or Edx.

In addition to these online resources, you can also consider the following excellent written resources:

The free introduction to R manual by CRAN
Jared Lander’s R for Everyone
Quick-R

Specifically learn: read.table, data frames, table, summary, describe, loading and installing packages, data visualization using plot command

Step 3: Understanding the R community

The major reason R is growing rapidly and is such a huge success, is because of its strong community. At the center of this is R’s package ecosystem. These packages can be downloaded from the Comprehensive R Archive Network, or from bioconductor, github and bitbucket. AtRdocumentation you can easily search packages from CRAN, github and bioconductor that will fit your needs for the task at hand.

Step 4: Importing and manipulating your data

Importing and manipulating your data are important steps in the data science workflow. R allows for the import of different data formats using specific packages that can make your job easier:

readr for importing flat files
The readxl package for getting excel files into R
The haven package lets you import SAS, STATA and SPSS data files into R.
Databases: connect via packages like RMySQL and RpostgreSQL, and access and manipulate via DBI
rvest for webscraping

Once your data is available in your working environment you are ready to start manipulating it using these packages:

The tidyr package for tidying your data.
The stringr package for string manipulation.
For data frame like objects learn the ins and outs of the dplyr package (try this course).
Need to perform heavy data wrangling tasks? Check out the data.table package
Performing time series analysis? Try out packages like like zoo, xts and quantmod.

Step 5: Effective Data Visualization

There is no greater satisfaction than creating your own data visualizations. However, visualizing data is as much of an art as it is a skill. A great read on this is Edward Tufte principles for visualizing quantitative data, or the pitfalls on dashboard design by Stephen Few. Also check out the blogFlowingData by Nathan Yau for inspiration on creating visualization using (mainly) R.

5.1: Plots everywhere

R offers multiple ways for creating graphs. The standard way is by making use of base graphics in R. However, there are way better tools (or packages) to create your graphs in a more simple way that will look on top of that way more beautiful:

Start with learning the grammar of graphics, a practical way to do data visualizations in R.
Probably the most important package to master if you want to become serious about data visualization in R is the ggplot2 package. ggplot2 is so popular that there are tons of resources available on the web for learning purposes such as this online ggplot2 tutorial, a handycheatsheet or this book by the creator of the package Hadley Wickham.

A package such as ggvis allows you create interactive web graphics using the grammar of graphics (seetutorial)
Know this ted talk by Hans Rosling? Learn how to re-create this yourself with googleVis (an interface with google charts).

5.2: Maps everywhere

Interested in visualizing data on spatial analysis? get started easily with these two packages:

Visualize spatial data and models on top of static maps from sources such as Google Maps and Open Street Maps with ggmap.
Ari Lamstein’s choroplethr
The tmap package.

5.3: HTML widgets

A very promising new tool for visualizations in R is the usage of HTML widgets. HTML widgets allow you to create interactive web visualizations in an easy way (see the tutorial by RStudio) and mastering this type of visualizations is very likely to become a must have R skill. Impress your friends and colleagues with these visualizations:

Dynamic maps with leaflet
Time-series data charting using dygraphs
Interactive tables (DataTables)
DiagrammeR for diagrams and flowcharts
D3 scatterplots, line charts, and histograms with MetricsGraphics

Step 6: Data Mining and Machine Learning

For those that are new to statistics we recommend these resources:

Andrew Conway’s Introduction to statistics with R (online)
Data Analysis and Statistical Inference by Duke University (online)
Practical Data Science With R (book)
Data Science Specialization by Johns Hopkins (online)
A Survival Guide to Data Science with R (book)

If you want to sharpen your machine learning skills, consider starting with these tutorials:

Step 7: Reporting Results

Communicating your results and sharing your insights with fellow data science enthusiast is equally important as the analysis itself. Luckily R has some very nifty tools to do this that can save you a lot of time.

The first is R Markdown , a great tool for reporting your data analysis in a reproducible manner based on knitr and pandoc. With R markdown, R generates a final document that replaces the R code with its results. This document can be in an html, word, pfd, ioslides, etc. format. You can learn more on it via this tutorialand use this cheat sheet a a reference.

Next to R Markdown there is also ReporteRs. ReporteRs is an R package for creating Microsoft (Word docx and Powerpoint pptx) and html documents and runs on Windows, Linux, Unix and Mac OS systems. Just like R Markdown it’s an ideal tool to automate reporting generation from R. See here how to get started.

Last but not least there is Shiny, one of the most exciting tools in R around at the moment. Shiny makes it incredibly easy to build interactive web applications with R. It allows you to turn your analysis into interactive web applications without needing to know HTML, CSS or Javascript. If you want to get started with Shiny (and believe us you should!), checkout the RStudio learning portal.

Step 8: Time Series Analysis

R has a dedicated task view for Time Series. If you ever want to do something with time series analysis in R, this is definitely the place the start. You will soon see that the scope & depth of tools is tremendous.

You will not run out of online resources for learning time series analysis with R easily. Good starting points are A little book of R for time series or check out Forecasting: principles and practice. In terms of packages, you need to make sure that you are familiar with the zoo package and the xts. Zoo provides you a common used format for saving time series objects, while xts gives you the tools to manipulate your time series data sets.