Best of DZone: R Tutorials for Big Data

R is one of the most popular languages among data scientists today for all sorts of operations. From data cleaning to data streaming to data visualization, R can be a powerful tool. In this post, we take a look at the best-performing R posts in the history of DZone, mixed in with some of the most exciting new tutorials of 2018. 

R Tutorials on DZone

1. Learn R: How to Create Data Frames Using Existing Data Frames Ajitesh Kumar. Short and sweet, this post demonstrates six different pieces of R code (5 of which are only one line) that allow data scientists and developers to extract data from previously developed data frames.

2. Learn R: How to Extract Rows and Columns From Data Frame Ajitesh Kumar. A review of several quick and easy to use R commands that allow developers and data scientists using the R language to extract data from rows, columns, and elements from data frames. If you're looking to get started on with data cleaning or need to refresh your skills, this is a great resource!

3. How to Write R Script Explained with an Awesome Example by Srini Pesala. This tutorial will walk you through a tutorial aimed at newcomers to the R language. It shows how to write scripts using the R language, which can prove beneficial when working with longer analyses. 

4. Exploring College Major and Income: A Live Data Analysis in R [Video] by David Robinson. A case study on how to dive into a dataset and learn from it, including steps where the author thinks aloud and decides what route to take. It's the live coding of data science!

5. Automatically Combining Factor Levels in R by Arthur Charpentier. A tutorial on how to perform analyses on big data sets, such as regression analyses, and visualize the results of those analyses using the R programming language. 

DZone Publications on R and Big Data

1. DZone's Guide to Big Data: Stream Processing, Statistics, and Scalability featuring articles by Jones Bonér, Arjuna Chala, Wolf Ruzicka, Liz Bennett, Sunil Kappal, and Tom Smith. Big Data is the new competitive advantage and it is necessary for businesses. With Blockchain tech, Cloud, and IoT adding new dimensions to Big Data, we see the creation and growth of new Big Data Storage and Analytics applications to pull value from the data. The 2018 Guide to Big Data will explore the evolution of Big Data, provide case studies on Big Data reference architectures, and leave you with the knowledge to scale your Big Data architecture.

2. R Essentials: The Language for Data Manipulation and Statistical Analysis by G. Ryan Spain. R is a highly extensible, open-source programming language used mainly for statistical analysis and graphics. R has become a widely popular language because of its varying data structures, which can be more intuitive than data storage in other languages; its built-in statistical and graphical functions; and its large collection of useful plugins that can enhance the language’s abilities in many different ways.

R Documentation

If you're interested in getting started with R, you can check out r-project.org for the official docs on the lanauge, and rdocumentation.org to search for the best of the what the R community has to offer.