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Is R Really that Awesome for Machine Learning?

Machine learning is gaining momentum every single day. International Data Corporation (IDC) published a report where it predicts that the investment in machine learning and AI will rise from $12 billion to $57 billion by 2021. That amount will keep on growing in the time to come.

Here is one more interesting fact. According to The New York Times, Tech Giants are paying a considerable sum of money, anywhere from $300-500k, to AI specialists. Investing your time in machine learning is worth it.

The primary question when it comes to learning machine learning is the selection of programming language. Among many programming languages in the world today, Python stands out as the number 1 programming language for machine learning. Does this mean that you should go with Python without giving R a second thought?

Popularity of Machine Learning Programming Languages in graph

The R programming language has now risen in popularity, and it is one of the preferred programming languages now for machine learning.

Should you consider Machine Learning with R? It entirely depends on you. Just go through this article to decide if R is right for you or not.

Why Should Anyone Consider R For Machine Learning?

R is an open-source programming language that has been around for 25 years now. Robert Gentleman and Ross lhaka in 1990 introduced R as a statistical platform with an objective to use it for data modeling and analysis.

R was not famous back then, but today this programming language has gained momentum. The one primary reason for this momentum is due to the rise of the popularity of machine learning.

Without further ado, let’s quickly dive into the top reasons to consider R for machine learning:

1. Full Documentation and Great Online Support

There is no shortage of well-documented online resources when it comes to R programming language (this includes message board as well). You will find active professionals online in case you need help in your learning journey.

Plenty of community packages are available online from JSON to XML files for helping developers create Random-Effects regression model. The excellent documentation and participation of R professionals will help you shorten your learning curve.

2. Rise in Big Data and Demand for Data Scientists

Rise in Big Data and Demand for Data Scientists

According to the report, we will exceed 50 billion smart connected devices sharing information by 2020. It has led to a rise in the demand of data scientists. The increment in the market of data scientists has also led to a considerable boost in their salary. Big data has been a helpful input to tackle some of the complex problems.

R is a great programming language for data management, as this language was designed to analyze and manage data in specific. Many enterprises would love to get in touch with professionals who are familiar with the R programming language.

3. Great for Data Wrangling

Data Wrangling is a method of simplifying complex data sets, making it possible to understand for more in-depth analysis. There is an extensive list of tools that you can use in R language that is great for database manipulation and wrangling.

Here are some of the popular packages that are available for R to carry out data wrangling.

  • Readr Package: Allows developers to read numerous types of data without the conversion of characters, which makes it process the information at 10X faster speed.
  • Dplyr Package: It is famous for its data exploration, fast adapting chaining syntax, and its fantastic transformation features.
  • Data.Table Package: Its amazing power to quickly manipulate data with a least coding help in simplification of data at an incredibly faster rate.

4. Excellent Data Visualization Capabilities

The data visualization capabilities of R is mind-boggling. R consists of many tools that allow developers to visualize data for analysis and its representation. Ggplot 2 and ggedit are two standard plotting packages in R.

Ggplot 2 is for data visualization, whereas ggedit build the bridge for developers to make a plot and get all sorts of plot aesthetics correct with high precision. Below is an example of data visualization output by making use of the available packages.

Data visualization capabilities of R language
Graph showing data visualization capabilities of R


5. Greater Availability

R language is an open-source programming language with a community of a considerable number of developers. Thanks to its large number of active developers, the developments in R happens rapidly.

Due to its greater availability, there is always a significant number of new programmers jumping in to learn this programming language. Furthermore, it is also quite cost-effective to outsource tasks to R developers.

6. Simple to Learn and Easy to Pace your Learning

R programming language was never meant to be a language for computer scientists. It was created, keeping statisticians and other mathematicians in mind. It could be more comfortable for non-programmers to learn it. Not trying to imply that it will be a piece of cake, though.

Moreover, there is a large number of active developers online to help you out as said before, which will make things easier for you. There are also numerous top-notch online courses on the internet to help you get started. Reputed companies and professionals mostly release these online classes.

Online courses to learn R language for free

In the first few search results, we can see R Programming courses in several reputed websites. The great thing about these online courses is that they are affordable. There is no need for you to spend tens of thousands of dollars to learn this language. Here are two significant points that indicate that learning this language will not be a difficult task for you.

  • R Programming Language is more comfortable to learn
  • There are great resources available online along with a large number of active professionals.

To give you a hint of the simplicity of R, here is an example of a simple R program.

ydata <- sample(c(1:100),10000,replace = T)
xdata <- sample(c("Eric","Copper","Stewart"),10000,replace = T)
zdata <- sample(c("Red","Blue","Green","Yellow"."Orange"),10000,replace = T,prob=c(0.4,0.2,0.25,0.1,0.05))
df <- data.frame(Marbles=ydata,Person=xdata,Color=zdata)
	scale_fill_manual(values=c("light blue","light green","#ff8000",#ff3232",#ffff66"))+
	labs(fill=Ball Color")

Final Thoughts

I hope you have found value in this article. Now that you know all the unique features of R programming language, it is up to you to decide whether R is for you or not.

The recent report published by Kaggle revealed that only around 4.5% of data scientists and researchers are working as machine learning engineers. The in-depth knowledge of machine learning, R programming language, in particular, will mean that you will most probably be an on-demand professional.

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