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One of the most popular languages in the world of data science to program in is R. It is the 12th most popular programming language, according to recent RedMonk ratings.
It helps users analyze structured and unstructured data, which has made it the standard language for carrying out statistical operations. It has features that set it apart from other languages used in data science.
It was initially developed in 1993 by Ross Ihaka and Robert Gentleman. Like the S language, R is a GNU project, but it is a different implementation of S. While there are significant differences, most code written for S runs unaltered under R.
The S language is used for research in statistical methodology while offering an open source way to participate in that activity.
As mentioned earlier, R excels at statistical computing. Data visualization is known to be easier using R than with Python. It also has built-in functionality and useful tools that make performing tasks simpler in areas such as visualization, reporting, and interactivity.
There are over 2,000 free open source libraries for finance, cluster analysis, high-performance computing (HPC), statistics, machine learning, and data science. R is open source, so users can freely install, use, update, clone, modify, and redistribute it. It is also cross-platform, so it runs on Windows, Mac OS X, and Linux. It can also import data from Microsoft Excel, Microsoft Access, MySQL, SQLite, Oracle, and many others.
Python is known for being friendly for beginners. However, when you understand R’s syntax, you learn it offers a big advantage in learning data science basics, as it was designed with data manipulation and analysis in mind.
Organizations worldwide use R with data manipulation and analysis in mind.
There are many online courses people can take to learn this powerful language. Below is a selection of the best.
Provider: Udemy
URL: https://www.udemy.com/course/r-programming/
Course length: 10.5 hours
This step-by-step course helps students learn to program in R. It teaches the core principles of programming and how to create variables. Parts of the course teach you how to use R Studio and customize it to your preferences. There is also practice in financial, statistical, and sports data in R. Students will also learn about Normal distribution and Law of Large Numbers.
Provider: Coursera
URL: https://www.coursera.org/learn/r-programming
Course Length: 57 hours
In this course, students learn how to program in R and use it for purposes such as data analysis. Students learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.
The course covers practical issues in statistical computing, including programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.
Provider: Udacity
URL: https://www.udacity.com/course/programming-for-data-science-nanodegree-with-R–nd118
Course Length: 3 months at 10 hours a week
This nanodegree program helps students learn the programming fundamentals required for a career in data science. By the end of the program, students will use R, SQL, Command Line, and Git.
The introductory program has three modules: Introduction to SQL, Introduction to R Programming, and Introduction to Version Control. The module on R Programming teaches fundamentals such as data structures, variables, loops, and functions. Students will learn to visualize data in the popular data visualization library ggplot2.
Provider: edX
URL: https://www.edx.org/course/data-science-r-basics
Course Length: 8 weeks at 1-2 hours per week
This is the first part of a multi-part course that leads to a Professional Certificate Program in Data Science. It introduces students to the basics of R programming and builds a foundation that paves the way for the more in-depth courses later in the series where concepts such as probability, inference, regression, and machine learning, are covered.
Students can develop a skill set that includes R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with UNIX/Linux, version control with git and GitHub, and reproducible document preparation with RStudio.
Provider: Coursera
URL: https://www.coursera.org/specializations/statistics
Course Length: 7 months at 3 hours per week
There are five modules in this course from Duke University. These are Introduction to Probability and Data, Inferential Statistics, Linear Regression and Modeling, Bayesian Statistics, and Statistics with R Capstone.
The capstone project will be an analysis using R that answers the course team’s specific scientific/business question. A large and complex dataset will be provided to learners and the analysis will require the application of various methods and techniques introduced in the previous courses.
Provider: Datacamp
URL: https://www.datacamp.com/tracks/r-programmer
Course Length: 44 hours
This course provides programming skills needed to successfully develop software, wrangle data, and perform advanced data analysis in R. Students need no prior coding experience and will learn how to manipulate data, write efficient R code, and work with challenging data, including date and time data, text data, and web data using APIs.
There are interactive exercises to gain experience working with R libraries, including devtools, testthat, and rvest, that will help students perform important programmer tasks, such as web development, data analysis, and task automation
Provider: Udemy
URL: https://www.udemy.com/course/data-science-and-machine-learning-bootcamp-with-r/
Course Length: 17 hours 45 minutes
This course is designed for beginners with no programming experience or experienced developers looking to jump to Data Science.
Students learn how to program with R, create data visualizations, and use machine learning with R. This includes linear regression, decision trees, random forests, neural nets and deep learning, and support vector machines.
Provider: LinkedIn Learning
URL: https://www.linkedin.com/learning/r-for-data-science-lunchbreak-lessons
Course Length: 13 hours 8 minutes
Lunch Break Lessons teaches R in short lessons that expand on what existing programmers already know. Students can review language basics, discover methods to improve existing R code, explore new and interesting features, and learn about useful development tools and libraries to make their time programming with R more productive.
Provider: Udemy
URL: https://www.udemy.com/course/r-analytics/
Course Length: 5 hours 58 minutes
This course is for anyone who has basic R knowledge and would like to take their skills to the next level to become proficient at data science and analytics with R.
Students are expected to have taken and completed the R Programming A-Z course. Students learn how to prepare data for R analysis, perform the median imputation method in R, work with date-times in R, what lists are and how to use them, and what the Apply family of functions is.
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