Are you looking for the right information on how to use R programming for data science? If YES, then this post is for you…

The use of data science and statistics to solve problems is ever increasing. For this reason, R is a valuable language to learn, especially for those interested in analyzing data and solving real-world problems. Altogether, what is R programming?

5 Ways to use R Programming for Data Science

R is a universal programming language with various uses, particularly in data science. It offers statistical methods to programmers and non-experts through an open-source environment. These provide access to scientific computing capabilities for those with limited computing background or experience with other statistical software.

R Programming for Data Science – Its 5 Main Uses

R has five main uses in data science as mentioned below:

1. Data analysis and visualization

R helps programmers read data, perform statistical analysis, and create informative graphs or charts. The ggplot2 package is beneficial for creating attractive and informative graphs. Additionally, you can use the Shiny package to create web applications that provide interactive data exploration.

2. Statistical modeling

You can use R to fit various statistical models, including linear and nonlinear models. Programmers use the lm () function to fit linear models, while the nls() function fits nonlinear models. Additionally, you can perform a wide range of classical statistical tests using the stats package.

3. Machine learning

The caret package is a set of functions that implement a wide range of supervised and unsupervised machine learning methods. Moreover, the randomForest package help creates random forests for regression and classification tasks. These algorithms get implemented using a bagging approach to reduce variance and improve performance on complex problems.

4. Reproducible research

Since it is possible to extend R through packages, you can extend the capabilities of R beyond the base language. Using packages allows for code written in R to accomplish more than the base R. Additionally, results and graphs produced with R software are also reproducible since other users with the same package can recreate it.

5. Developing applications

R is an interpreted language where you can perform calculations on the fly without first creating a program. Also, it allows additional optimizations on computationally expensive tasks.  RStudio, an integrated development environment (IDE) for R, provides capabilities such as code completion and visualization of data.

What are Special Features of R in Data Science?

In data science, R programming can boast of the following features:

    • It is easy to learn for beginners.
    • It is a platform that enables interactive data analysis and visualization. 
    • Statistically, it is a powerful tool to analyze data and models.
    • There are many machine learning algorithms at our disposal.
    • It is a powerful programming language for the web and building statistical models in finance, genomics, etc.
    • Many commercial organizations have added R to their list of data science tools that offer much business value in terms of cost-effectiveness and efficiency.
    • R has a vast and active community always willing to help.
    • The syntax of the language is simple and easily understood by programmers from other languages.
    • The language also allows for creating reusable code in the form of packages, which is an added advantage.
    • Finally, R is a platform that is not controlled by any single corporation, which means it is less likely to become obsolete in the future. 

R Versus Other Popular Programming Languages

R is specifically designed to perform data analysis compared to other programming languages. Therefore, the language relies on numerical computation, data structures, graphics, object manipulation, and statistical functionality. In addition, the platform boasts of lexical scoping and vector orientation that enables functional programming. 

The only major drawback is that R is too complex for novices to learn. This is because the language has a non-standardized code that is very difficult for beginners to hack. 

Examples of R Programming for Data Science Use Cases

Data science has transformed nearly every industry worldwide. For instance, algorithms help medics forecast patient side effects in the health industry. Some of the most popular R utilization examples include Google, which has created a new tool known as LYNA. The platform helps detect tiny cancer cells where other measures fail to perceive. 

Other than that, the Uber Eats food delivery app works magic on ensuring that customers receive their orders real fast. Finally, we cannot forget how Instagram utilizes data science to target sponsored posts to the right audience.

Next Step for R Programming

Without a doubt, R programming has a promising future because of technological advancement. As a matter of fact, experts consider it as one of the most suitable tools when handling data successfully.

It is no surprise that successful tech firms like Uber and Google prioritize the R language to beat competition. Thanks to the rising demand for machine learning and data science trends, familiarizing yourself with the programming language is the wisest decision lately.

The silver lining is that you can get plenty of helpful learning resources and online tutorials on the best way forward. Good luck in your endeavor.

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