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R in data science

R is a very popular language in academia. Many researchers and scholars use R for experimenting with data science. Many popular books and learning resources on data science use R for statistical analysis as well. Since it is a language preferred by academicians, this creates a large pool of people who have a good working knowledge of R programming. Putting it differently, if many people study R programming in their academic years than this will create a large pool of skilled statisticians who can use this knowledge when the move to the industry. Thus, leading increased traction towards this language.

  1. Data wrangling:

Data wrangling is the process of cleaning messy and complex data sets to enable convenient consumption and further analysis. This is a very important and time taking process in data science. R has an extensive library of tools for database manipulation and wrangling. Some of the popular packages for data manipulation in R include:

  1. Data visualization:

Data visualization is the visual representation of data in graphical form. This allows analyzing data from angles which are not clear in unorganized or tabulated data. R has many tools that can help in data visualization, analysis, and representation. The R packages ggplot2 and ggedit for have become the standard plotting packages. While the ggplot2 package is focused on visualizing data, ggedit helps users bridge the gap between making a plot and getting all of those pesky plot aesthetics precisely correct.

  1. Specificity:

R is a language designed especially for statistical analysis and data reconfiguration. All the R libraries focus on making one thing certain – to make data analysis easier, more approachable and detailed. Any new statistical method is first enabled through R libraries. This makes R a perfect choice for data analysis and projection. Members of the R community are very active and supporting and they have a great knowledge of statistics as well as programming. This all gives R a special edge, making it a perfect choice for data science projects.

  1. Machine learning:

At some point in data science, a programmer may need to train the algorithm and bring in automation and learning capabilities to make predictions possible. R provides ample tools to developers to train and evaluate an algorithm and predict future events. Thus, R makes machine learning (a branch of data science) lot more easy and approachable. The list of R packages for machine learning is really extensive. R machine learning packages include MICE (to take care of missing values), rpart & PARTY (for creating data partitions), CARET (for classification and regression training), randomFOREST (for creating decision trees) and much more.

  1. Availability:

R programming language is open source. This makes it highly cost effective for a project of any size. Since it is open source, developments in R happen at a rapid scale and the community of developers is huge. All of this, along with a tremendous amount of learning resources makes R programming a perfect choice to begin learning R programming for data science. Because there are many new developers exploring the landscape of R programming it is easier and cost-effective to recruit or outsource to R developers.

Thus, we have seen that R is worth its popularity and it is going to scale further. R allows practicing a wide variety of statistical and graphical techniques like linear and nonlinear modeling, time-series analysis, classification, classical statistical tests, clustering, etc. R is a highly extensible and easy to learn language. All of this makes R an ideal choice for data science, big data analysis, and machine learning.

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