R is a programming language designed for use in statistics and the analysis of large amounts of data. With R, it is very easy to handle vast amounts of data and visualise them as required using different types of graphs. As it is a programming language created by statisticians, it lends itself naturally to the purposes of data science, a discipline that is currently booming.
R is therefore made up of two sides: one is its aspect as programming language, and the other is a own computing environment in which users employ functions within a command-line interface to programme in R.
Unlike languages such as Python and Java, which are general-purpose languages, R is a domain-specific language, and is therefore used exclusively for data analysis in a statistical context.
It is therefore equipped with a very wide range of functions for analysing large volumes of data, modelling them and creating charts that are both aesthetically pleasing and functional.
There are many companies that rate R very highly and use it in their workplaces. Some well-known examples of companies that use R These include Amazon, Facebook, Google, Accenture, Uber, IBM, LinkedIn, and many more! That’s why professionals who learn R significantly improve their career prospects.
The origins of R as a programming language specialising in data can be traced back to the 1970s, when John Chambers developed a language called S, which was also designed for statistical purposes. Twenty years later, R emerged as a dialect of this primordial language. Since then, R has been constantly evolving and becoming more accessible to all users and programmers through regular updates.
One particularly noteworthy aspect of R is its community, a group of data enthusiasts who use R and bring a great deal of value to this niche. The R community is very active, and there are many people on the forums who write about data analysis techniques and answer questions about R’s most obscure features.
Furthermore, it is within this community that thousands of R packages have been developed, offering specific functionalities that are far more intuitive than those of the standard R.
What is R used for?
As we have said, R is a the leading programming language for data analysis. It can be used to clean data, filter information, create different types of graphs and manipulate the data of interest to carry out a comprehensive analysis of vast amounts of data stored in databases. This is why many professionals find R to be a very powerful tool. These are the fields in which R is used:
Research
Just as English is the lingua franca in the West, R is the programming language of choice at many academic institutions. It is not only used in the departments of mathematics and statistics not only in universities, but it is also required in much research involving the analysis of quantitative data, such as biology, economics, physics or medicine.
Data science
Python and R are the most widely used languages in the field of data science. Thanks to R, these professionals can data modelling structured and unstructured, as well as creating tools for machine learning that automate certain tasks.
Once you have learnt how to use R, working with data from various sources becomes a more manageable challenge. Furthermore, both the core R language and many of its packages offer a wide range of options for visualising data according to our needs and the type of data we are working with. In this way, data scientists present the results of their research and analyses in a visual and easily understandable way for everyone.
Finance
Unsurprisingly, R has also found direct application in the analysis of business and accounting metrics data. Many companies and banks use R to analyse investment risks and produce a wide variety of financial reports. In fact, there are R packages specifically designed for business intelligence, such as jrvFinance and Rmetrics, which enable finance professionals to work with R without needing to be highly proficient in programming.
R packages
One of the key advantages of R is the wide range of packages available, covering a variety of areas, from statistics and data analysis to visualisation and machine learning. When we talk about an R package, we are referring to a ca set of functions, data and code which are organised in a structured manner and distributed as an importable unit within the R environment.
Packages are a standout feature in R. In fact, the suite of packages in tidyverse which is expected to surpass R base in terms of frequency of use and functionality. Similarly, all these packages are geared towards different fields and are designed to be more intuitive and practical for their respective areas of application. These are the most useful R packages:
dplyr
The dplyr package is based on a concise and intuitive syntax for data manipulation tasks. Some of its functions, such as `filter`, `select`, `mutate` and `summarise`, allow us to filter, transform and summarise large amounts of data. It is one of the essential packages for both beginners getting started with R and experienced data scientists.
ggplot2
This pack is well known for its practicality and versatility when it comes to to visualise data in an attractive and highly customisable way. The possibilities that ggplot2 offers us when creating graphs are endless, ranging from scatter plots to highly complex multivariate plots.
shiny
With this innovative R package, we can create interactive web applications directly within the R environment. Thanks to Shiny, we can create interactive dashboards, cutting-edge data visualisations and bespoke tools without having to undergo extensive training in web development. This enables data scientists to share their findings with other professionals in a more accessible and immersive way.
stringr
Stringr is a package that provides a highly intuitive interface for the average user, with the aim of manage text string tasks with features such as pattern recognition, string extraction, substitution and much more. It is a particularly useful tool when working with unstructured data and analysing large volumes of text.
How do I get started with learning R?
It may sound like a bit of a cliché, but it’s the truth: R You learn by doing. There are plenty of online resources to help you learn the basics of R. That said, we should warn you that learning a programming language – especially if you have no previous experience – requires perseverance and discipline. It may take you a couple of months to get to grips with the basics of R.
However, Programming from memory is something that comes with time. The key thing is that you understand the full potential that R can offer you in data analysis, that you have the necessary resources, and that you know how to get the results you want from your data using R.
That said, getting started with R can be a bit daunting and confusing, as it’s a vast world that can be tricky to get to grips with. In that case, we recommend you take a look at our catalogue of courses and master’s programmes in analytics and data science, in which we’ll welcome you to R with a highly practical approach that’s applicable to the day-to-day work of a professional data scientist.
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