Data Science with R

by May 23, 2016 5 comments

R is the numero uno choice of data scientists globally. It is an open source programming language widely used in the manipulation and visualization of data.  R has been religiously embraced in solving complex data science problems by statisticians and business analysts. Big industry names including Google and Facebook use R to analyze user behavior and develop strategies. Let us dive deep into R to know why it is the darling of the data science community.

 Why use R?

It is an open source language and can be installed, and used by anyone. It is available or could be integrated in various platforms. R is maintained by top-notch experts in statistics and business analytics. The R community develops and contributes packages specifically designed to address the needs of users. Currently it features about 8452 packages scattered alphabetically. One of the recent packages developed by R Studio, a member of the R community is flexdashboard, a package which enables you to create flexible, attractive, interactive dashboards.

Moreover, the romance of using R scripts is that you can exchange the analysis with others. It also crosses out the daunting task of repetitions.  One can easily import data from Excel, CSV, Minitab and SPSS into R.

Processing Data in R

Once you import your data into R, you can use basic expressions for numbers, strings, and true/false for calculations. We can store these values in variables and pass them to use the R functions. New column additions, counting and making subsets of the data would be easier if you learn the basic steps in the beginning. You may use this tutorial in order to make your base strong.

Once you are well-versed with the basic stuff, you can install packages that will enable you to perform statistical analysis revolving around probability distribution, correlation and regression. Some of these can be performed without installing a package as well.

Visualization of Data

Visualization helps decision makers understand the length and breadth of data without delving deep into it.  R is one of the best programming languages used to visualize data through static and interactive graphs, and understand relationship between variables. The most commonly used visualization techniques for beginners are dot chart, histogram, box and whisker plot, bar or line chart, and scatter plot.  For advanced visualization, one can use 3D graphs, heat map and Mosaic Map. Some of the useful packages in R for visualization include ggplot2, ggvis, rCharts and plotly.

With the burgeoning penetration of data analytics in different industries globally, R’s multi-functional analyisis techniques are gaining a lot of prominence. Besides industry usage, R has been a part of many statistics and analytics courses offered by prestigious institutes across the globe.  According to a survey, R is among the top four languages used extensively and faces tiff competition with Python, a well-known programming language graced by data scientists. A lot has been written about R and you could easily find good books to boost knowledge. Please see my earlier post on books here.

The surge in the use of analytics in a bid to gain useful insights from a large pool of data is providing a fillip to programming languages. With increase in research expenses and cost of sophisticated tools, R will surely lead the race with its arms open to everyone. Additionally, one doesn’t have to shell out a hefty amount of money to purchase large software packages.  As R gains more popularity in the data science arena, it will attract more users who want to rev up the turnaround time of a business problem, and the prognosis would be really insightful.

R

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