Advantages of Using R for Data Science
Advantages of Using R for Data Science, In modern times, the field of data science is evolving at a very fast pace. Hence, businesses need to embrace the same before getting left behind at a distance that will just keep on increasing over time. R is a powerful tool that has excellent statistical and visualisation capabilities, making it very attractive to data scientists.
R is the most powerful tool to execute algorithms related to data science and has the capability of working with abundant data. It provides a wide variety of linear and non-linear models, classical statistical tests, time series analysis and machine learning capabilities (i.e., classification, clustering, regression and reinforcement learning) and excellent visualisation techniques.
5 Advantages of Using R for Data Science
1) Free and Open Source
An open-source language is a language on which we can work without any need for a license or a fee. R is an open-source language. We can contribute to the development of R by optimizing our packages, developing new ones, and resolving issues.
2) Extensive support for statistical modelling
Statistical modelling is essential to determine how one variable is related to others. R provides powerful capabilities to deal with statistical modelling. It has excellent functions for central tendency, the measure of variability, probability, hypothesis testing, ANOVA and regression analysis.
3) Extremely easy data wrangling
R has several packages that hugely simplify the process of preparing your data for analysis. You may have your data stored in the .csv or .txt file, in Excel spreadsheets, in relational databases, or as a SAS or Stata file. R can load these various types of files with just one line of code.
The process of data cleaning and transforming is also straightforward. One line of code — and you create a separate dataset without any missing values, another line — and you impose multiple filters on your data. With such powerful capabilities, the time you spend preparing your data for analysis can decrease significantly, giving you more time to spend it on the analysis itself.
4) The connection with NoSQL databases
The majority of data science projects deal with unstructured data. R can provide interfaces with NoSQL databases and analyse unstructured data in effective ways.
5) Advanced visualizations
Even the basic functionality of R allows you to create histograms, scatterplots, or line plots with only a tiny bit of code. These are very convenient functions for visualizing your data before even starting any analysis. In a few seconds, you can see your data and get insights that are not visible from the tabulated data alone.
However, if you spend some time learning more advanced visualization packages, such as ggplot2, for example, you’ll be able to build some very impressive graphs. R provides seemingly countless ways to visualize your data. These graphs will look very professional. And you’ll get access to a whole host of extra options, such as adding maps to your visualizations or making them animated.
Originally published at https://pyoflife.com on September 10, 2022.
