What is R Programming Language? Introduction & Basics of R
A programming language and free software developed by Ross Ihaka and Robert Gentleman in 1993, R is an open-source programming language and free software. R contains a large number of statistical and graphical methods that are easy to learn and use. Machine learning algorithms, linear regression, time series, and statistical inference are only a few examples of what is covered. Although the majority of the R libraries are written in R, C, C++, and Fortran codes are favoured for computationally intensive jobs.
Many significant corporations, such as Uber, Google, Airbnb, Facebook, and other similar organisations rely on the R programming language in addition to academic institutions.
Using R, data analysis is accomplished in a sequence of phases, including programming, changing data, detecting patterns, modelling data, and communicating the results.
R is a programming language that is simple and easy to learn.
Transform: R is comprised of a group of libraries that are specifically suited for data science applications.
Discover: Examine the data, revise your hypotheses, then analyze them.
Model: R provides a comprehensive set of tools for capturing the most appropriate model for your data.
Communicate: Use R Markdown to integrate scripts, graphs, and outputs into a report, or create Shiny apps to share with the rest of the world.
In this introduction course, you will learn how to use the R programming language.
What is R used for?
When we look at the use of R in industry, we can observe that academics are the first to use it. R is a statistical programming language. In the healthcare industry, R is the most frequently used letter, followed by government and consulting.
R by Industry
The principal applications of R have been and will continue to be statistical analysis, visualisation, and machine learning. The image below depicts which R package received the greatest number of questions on Stack Overflow. A data scientist’s workflow is represented by the top ten questions, with the majority of them relating to data preparation and communicating the results.
CRAN contains all of the R libraries, which number about 12,000. CRAN is a free and open-source software package. You can use the multiple libraries available for Machine Learning and time series analysis, which you can download and use.
Communicating with R R has a variety of ways to show and share work, whether it’s through a markdown document or a shiny application. Everything can be hosted in Rpub, GitHub, or on the company’s website if necessary.
An example of a presentation hosted on Rpub can be found below.
When writing a document, Rstudio allows markdown as a format. You can export the papers in a variety of formats, including:
Document: HTML PDF/Latex Word Presentation HTML PDF beamer HTML PDF beamer
Rstudio provides a fantastic tool for quickly creating an App. The following is an example of an app that uses data from the World Bank.
The use of data science is changing the way firms conduct their operations. Without a doubt, avoiding Artificial Intelligence and Machine Learning will lead to the company’s demise shortly. The big question is: which tool or programming language should you employ.
On the market, numerous tools may be used for performing data analysis. Learning a new language necessitates a significant time commitment. The learning curve for a language is depicted in the diagram below concerning the business capacity it provides. Because of the negative association, there is no such thing as a free lunch. If you want to provide the most accurate insight from your data, you must devote some time to mastering the appropriate tool, which is the R programming language.
Excel and PowerBI can be found at the very top of the graph, to the left. These two technologies are straightforward to learn, but they do not have particularly strong business capabilities, particularly in terms of modelling. Python and SAS are visible in the centre of the screen. SAS is a specialised tool for doing statistical analyses in the corporate world, however, it is not free to download. SAS is software that may be launched with a single click. In contrast, Python is a language with a monotonous learning curve that is difficult to master. Python is a terrific technology for deploying Machine Learning and Artificial Intelligence, but it lacks communication capabilities. R is a good trade-off between implementation and data analysis, and it has an identical learning curve to Python.
The name Tableau is certainly familiar to you if you’re interested in data visualisation (DataViz). Tableau is, without a doubt, a fantastic tool for discovering patterns in graphs and charts, and it is free to use. Furthermore, learning Tableau is not a time-consuming endeavour. One major problem with data visualisation is that you may wind up never discovering a pattern or simply creating a large number of pointless charts. If you need to visualise data quickly or for Business Intelligence, a Tableau is an excellent tool. When it comes to statistics and decision-making tools, R is a more appropriate choice than Python or Java.
Stack Overflow is a large programming language community with a large number of members. We’re here to help you with your coding problems or to comprehend a model you’re trying to understand better. When compared to the other languages, the percentage of question views for R has climbed significantly over the year. This development is, of course, significantly associated with the burgeoning era of data science, but it also shows the increased demand for the R programming language in data science applications.
In the field of data science, two tools are in direct competition with one another. R and Python are most likely the programming languages that are most associated with data science.
Communicate with R
R and Python are two fantastic programming languages for data scientists to employ. It’s possible that you won’t have enough time to master them both, especially if you’re just getting started with data science. Learning statistical modelling and algorithms is significantly more important than learning a programming language, as is learning how to programme in general. A programming language is a tool that allows you to compute and report your discoveries in real-time. The way you deal with data is the most significant activity in data science: import, clean, prep, feature engineering, and feature selection are all crucial tasks. This is where your primary attention should be directed. If you are attempting to learn R and Python at the same time without first gaining a basic understanding of statistics, you are being quite foolish. Data scientists are not programmers in the traditional sense. Their responsibility is to comprehend the data, manipulate it, and expose the most effective strategy. Please consider the following if you are considering learning a foreign language: which language is best appropriate for you?
It is primarily business professionals who will be interested in data science. One of the most significant implications in business is communication. There are numerous other ways to communicate, including reports, web apps, and dashboards. You’ll need a tool that can handle everything at once.
Should you choose R?
R was once considered a difficult programming language to master. The language was difficult to understand and was not as well organised as the other programming tools. Hadley Wickham created a suite of packages known as tidyverse to address this significant problem. The rules of the game were altered positively. Data manipulation becomes simple and intuitive as a result of this. Creating a graph was not as tough as it had previously been.
R is a programming language that may be used to implement the best machine learning algorithms. Packages like Keras and TensorFlow make it possible to develop advanced machine learning techniques. R also includes a package for performing Xgboost, which is considered to be one of the top algorithms for the Kaggle competition.
R can communicate with those who speak another language. In R, it is possible to invoke Python, Java, and C++. R has access to a wide range of data sources, including big data. R can communicate with a variety of databases, including Spark and Hadoop.
Finally, R has progressed and now allows for the parallelization of operations to speed up the computation. R has been chastised for only utilising one CPU at the same time. The parallel package enables you to distribute tasks over multiple processor cores on a single system.
For the most part, R is a fantastic tool for exploring and investigating data sets. R is used for more complex analysis, such as clustering, correlation, and data reduction, among other things. A good feature engineering and model are critical components of the machine learning process; without them, the machine learning deployment would fail to produce meaningful results.