“Big data is at the foundation of all the megatrends that are happening.”
-Chris Lynch, Vertica Systems
Everyday the number of programmers that are learning R programming language to become a Data Scientist is increasing. It is one of the hottest and high paying tech jobs on the planet currently. R is sometimes called the “golden child” of data science
If you want to try or learn R programming for Data Science and machine learning purpose and looking for some awesome courses to start your journey then you are on the right track since this article will be covering the best resources to learn R programming language in 2019.
Contents
However, for those, who are not familiar with R, let’s get into discussing that first and then move forward.
What Is R?
R is a programming language which was developed by Ross Ihaka and Robert Gentleman in 1993. R offers an extensive catalog of statistical and graphical methods. It includes machine learning algorithm, linear regression, time series, statistical inference to name a few. Most of the R libraries are written in R, but for heavy computational task, C, C++ and FORTRAN codes are preferred.
R is entrusted by many large companies including Uber, Google, Airbnb, Facebook and so on.
Data analysis with R is done involving a series of steps which are: programming, transforming, discovering, modeling and communicate the results
- Program: R is a clear and accessible programming tool
- Transform: R is made up of a collection of libraries designed specifically for data science
- Discover: Investigate the data, refine your hypothesis and analyze them
- Model: R provides a wide array of tools to capture the right model for your data
- Communicate: Integrate codes, graphs, and outputs to a report with R Markdown or build Shiny apps to share with the world
R burst into the scene with its powerful support for stats (linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering…) and graphical techniques, but in recent year, it has gained a lot of popularity among Data Scientist and Data minors.
Along with Python, R is the second most popular language to learn the new world of Data Science, Machine Learning, Deep Learning, and Artificial intelligence.
Difference Between R and Python:
In the battle of “best” data science programming language, python and R both have their pros and cons. Selecting one over the other depends on the use-cases, the cost of learning, and other common tools required.
R is mainly used when the data analysis task requires standalone computing or analysis on individual servers. It’s great for exploratory work, and it’s handy for almost any type of data analysis because of the huge number of packages and readily usable tests that often provide you with the necessary tools to get up and running quickly. R can even be part of a big data solution whereas Python can be used when your data analysis tasks need to be integrated with web apps or if statistics code needs to be incorporated into a production database. Being a fully fledged programming language, it’s a great tool to implement algorithms for production use.
Best Online Courses and Websites To Learn R foe Data Science in 2019:
1. Econometrics in R:
The first of the many to come is Econometrics in R. It is an e-book of about 50 pages written by Grant Farnsworth. It covers a few topics like Time Series analysis and linear regression and it also covers basic utility aspects like IO, but Farnsworth does that by explaining often advanced techniques, such as input of multiple-gigabyte-sized datasets. Hence, it is one of the finest resources for you to go through if you are planning on learning R.
2. Statistics with R:
This is another ebook and its author is a guy named Vincent Zoonekynd. Statistics with R is a work of art which suggests that is some sort of mad genius who stumbled onto CRAN one day, downloaded R, and began coding with it, and Statistics with R is just the cumulative archive of those spontaneous meditations with the R console. Once you reading it, you feel like you are learning it right along with him, just at a pace that exceeds ordinary human IO bandwidth by an order of magnitude or so. Occasionally, he gets stuck but instead of relying on the gigantic and accessible R documentation, he just looks at the R source code and carries on.
3. Introduction to R Programming by Dataflair:
Introduction to R Programming by Dataflair is one of the best free online tutorials website to learn R from. DataFlair team comprises of trainers who are experts in their relevant technologies and are selected after many rounds of interviews, content team that continuously works hard to provide quality content to the readers, Marketing team that work hand in hand with other teams to make the content reaches the right audience and HR team that work to extend the team and to provide a healthy work environment.
I would recommend you to go through the tutorials they offer and see for yourself.
4. Udemy Free Courses on R:
Udemy offers many free courses on R starting from the basic level and moving on to advanced level. These courses are free and very helpful for anyone looking to make his/her career in the field of Data Science using R. These courses are not only limited to Data Science but are also useful for Machine Learning, Statistics and Graphics purposes. These courses are:
- Quick Dive Into R
- R Basics — R Programming Language Introduction
- Learn Data Science With R
- Learn R for Business Analytics from Basics
- R, ggplot, and Simple Linear Regression
5. Edureka Youtube Channel for Beginners:
You can go through this tutorial videos playlist and understand R and its different components in a simple way. Also it is free.
This Data analytics with R Tutorial video playlist takes you through R Programming, Data Manipulation, Exploratory Data Analysis, Data Visualization, Data Mining, Regression, Sentiment Analysis and using R Studio.
6. Learn R with R tutorials and coding challenges | DataCamp:
If you’ve got no or little programming background, you don’t need to worry. I’d highly recommend to get used to it by learning the following.
1. simple to complex data types – number, string, logical, vector, data.frame, matrix, list (optional)
2. reading/writing text (csv) files
3. (optional) conditional expression (mainly ‘if’) and looping construct (mainly ‘for’)
4. (optional) function (eg to create your own sqrt() function)
5. do you report
It doesn’t need to be exhaustive as your R can be improved a lot by doing. There must be enough R tutorials on the web including the following.
7. Hands on programming with R:
This is a free book which takes a project-based approach to learning the R language. It teaches how to load data, assemble and disassemble data objects, navigate R’s environment system, write your own functions, and use all of R’s programming tools. Throughout the book, you’ll use your newfound skills to solve practical data science problems.
8. Read Blogs:
R-bloggers website is among one of the best R blogging websites which curates content from some of the best blogs about R that can be found on the web today. Since there are more than 750 blogs that are curated on R-bloggers alone, one shouldn’t have a problem finding an article on the exact topic or use case you’re interested in.
A few other notable R blogs:
- Revolutions (Microsoft’s R blog)
- Civil Statistician
- Flowing Data
- Datazar Blog
There are hundreds of websites, video tutorials and free books available that can help you learn R but if you never really try it out by yourself, you’ll never learn. Datazar and Datacamp are great places to experiment whatever you have learned. You can immediately start by opening the R console and can even consult with other users if you get stuck.
I hope you found this article useful and I wish all the readers a best of luck if are considering to learn R to make your way into the world of Data Science.
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