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Developed by Ross Ihaka and Robert Gentleman in the year 1993, R is a free programming language and software environment that comes with a wide variety of statistical as well as graphical methods. Staples of this programming language include linear regression, machine learning algorithms, statistical inference, time series, and many more. This is why it is used in the development of statistical software for data analysis. The most widespread usage of programming in R can be found in writing the R libraries; however, C, C++, and Fortran codes are preferred for heavy computational tasks.
R is majorly used in academics; however, it also has great practical applications, and corporate giants, including Google, Facebook, Uber, Airbnb, also utilize it. Data analysis with R follows a few steps described below:
- Import: R imports the data set from a variety of sources as specified by you
- Transform: R categorizes the data into a tabular format with variables and observations
- Discover: Examine the data, tune your hypothesis, and carry out an analysis in a graphical format
- Model: R comes up with a wide range of tools to determine an apt model for your data
- Communicate: Create impressive apps or use R Markdown for adding graphs, codes, and outputs to a report
Here are some of the common questions about the R programming language that can help us understand it a little better.
What are common applications of R?
R is commonly applied in Machine Learning Algorithms, Statistical Inferences, and Data Analysis.
In which industries is R widely used?
As a valuable tool of statistical analysis, the foremost industry that uses R programming language is academics. The other industries where R is widely used include healthcare, consulting, and government.
What makes R a valuable tool to learn?
Ever since data science has been introduced, it has changed the way companies run businesses. Artificial Intelligence and machines are a vital part now, without which businesses will fail.
Although there are several tools available in the market that can perform data analysis but learning a new language takes time. And as you are ready to invest time to learn a new tool, you can consider R, which is counted among one of the highly reliable tools. In fact, R is better described as the perfect tool for statistics as well as decision making.
Why should you choose R over other tools?
The two tools that are most used by data scientists are R and Python. Those who have just started learning data science may not be able to learn both because of time constraints. In fact, learning statistical modeling and algorithms is way more important than learning a programming language. A programming language is a tool that is used for analyzing as well as consolidating your findings. An important task in data science is dealing with the data, which include import, clean, prep, feature engineering, feature selection which are all available with R.
Is it difficult to learn R?
The learning curve of R is comparable to SAS and Python and stands out due to its excellent capabilities for business. Along with R coding, another tool that is studied as a good combination is Tableau which is an impressive data visualization tool and has a less steep learning curve. Learning both can be a rewarding career decision if you aspire to become a data scientist. With the introduction of packages called tidyverse by Hadley Wickham, the language has become a much more intuitive tool.
How does a business benefit from using R?
R meets the challenges of the modern business of data and statistical analysis, which is useful while developing new products, exploring new markets for products, or studying other sets of data. One of the key needs for that is not only data analysis but further communicating it in a meaningful manner. R achieves this with a set of dedicated tools such as a report, dashboard, or web app. Another important factor is that R is open source and free, along with being highly customizable. This makes it a valuable tool to meet the evolving business challenges and the dynamic environment.
From what all formats can I import and export data?
If you are wondering about the various formats from which R can import the data, then be assured that all major data formats and sources such as MS Excel, MS Access, SQLite, MySQL, Oracle, and others are covered. When it comes to exporting the data, you can do that in common formats such as CSV, HTML, XML, PDF, as well as interactive websites.
How does being open-source language help R?
The source code of R is freely available, and you can mould it for any purpose. There is also a big community of users that supports and provides help on various platforms so that you can find useful information easily. If you want to develop a web application using R, you can find the right package to develop and visualize the interactive data dashboards.
These are just a few factors that contribute to the popularity and applicability of programming in R. From being used in Social Network Analysis at Facebook to analyzing data in biology and genetics, R has widespread uses. This makes learning R a worthwhile pursuit and helps you along in this journey.
