R Programming Language: Advantages and Disadvantages

The R programming language is used for data processing and analysis. It is extremely useful in statistical research and building various models and therefore is in demand among mathematicians, economists, geneticists, and many other scientists. Often irreplaceable in business.

This language has a simple syntax and a free ecosystem for most operating systems. In addition, special graphical interfaces and various interactive tools have been developed for R, which greatly facilitate working with it.

General information about the programming language R

R is a programming language developed to perform statistical analysis of information. Initially, it was created as a free alternative to the S language, for which you had to pay. R was created in the Department of Statistics at the University of Auckland.

This language is very different from others. It has a specific syntax, working principles, and functions. The R language is used for statistical computing, data analysis, and machine learning. However, it cannot be used for other tasks.

Among other things, R is a working environment. It integrates ready-made methods of statistical analysis and various visualization tools.

For a given language, functions and tables belong to a particular class (data type). In this case, the finished program is executed immediately. Compiling the code into an executable is unnecessary before running it.

The syntax of the R language is quite simple. It contains several primitive data types (character, numeric, boolean, and complex). These types are combined to form more complex structures. For example, the vector type lists several objects (numbers, strings, etc.). It should be noted that numeric variables can take on special values: NaN (not a number – not a number), Inf (infinity – infinity), and NA (not available – not available).

The statistical language R allows you to develop various programs (scripts) using control structures. In addition, special extensions (packages) can be created and applied using this formal sign system. Today there are more than 7,000 such extensions. A package is a set of R functions, files containing reference data, and examples combined into a single archive.

These packages are of great importance to the language. The fact is that they are used as additional extensions based on R. Each package belongs to a specific direction of development. For example, the ‘ggplot2’ package is used to create vector plots of a certain design, and the ‘qtl’ ​​package is used for genetic mapping. They can be easily found on the Internet. At the same time, each of the existing packages is checked for errors.

Main features of the R language

As a rule, with the help of this formal sign system, ordinary programs with a familiar interface, buttons, and text are not created. Execution of codes in the R language allows you to display one or another result or graphics. Below we list the main features of this language.

Loading data

Each analytics tool must work with multiple sources of information. Otherwise, it becomes useless. Tables of various formats act as modern data storage and are one of the most important resources. You can upload spreadsheets in various formats with Er, including plain text txt files and Oracle/Excel/Matlab variants. The data is imported from the Web if you specify a URL link to a file.

Data processing

After uploading the data, a person starts processing it. This stage is often the most difficult of the others. The convenience of work directly depends on the tool the programmer has chosen.

 We list the most common problems that arise during processing:

• The downloaded data has to be cleared.
• You need to fill in the gaps in the data.
• Raw data is not informative, so you should group it and add filters and new columns.

The Er language contains many useful packages. They allow you to process various data with ease. For example, dplyr, tidyr, tibble, etc. Thanks to such libraries, the programmer can quickly create easy-to-understand code, even for complex processing.

Information visualization

After pre-processing the data, you may need to plot the graphs. Using the R language, you can create various visualizations. In addition to basic graphics, there is the ggplot2 package and related libraries. The programmer can create line plots, charts, density plots, boxplot, violin plots, maps, etc.

The plots are combined with the help of additional packages (Patchwork, Plotly, and some others). In addition, you can build interactive dashboards. The above can be posted on the Web and shared with others.

Presentation of the results of the work done

The analysis results must most likely be sent to the manager or other team members. Sometimes, the work must be placed in the public domain (for example, discussing research projects). The R language allows you to share your results. In this case, you do not need any third-party tools.

R language packages greatly expand their functionality. You can use a dedicated online service to post your results online, create your web analytics applications (requires the Shiny package), write articles and books (including their web versions) with interactive code results, and format and color tables.

We list a few more features of the R language for solving various problems:

• Performing statistical tests. For example, you can calculate the average duration and see if there is a statistically significant difference between several parameters.
• Combining information from different tables. The programmer can process several tables of different formats at the same time.
• Analysis of regression models. The language allows you to find relationships between variables (say, the dependence of the profitability of an enterprise on certain conditions).
• Perform various mathematical operations. For example, combine multidimensional arrays, predict a value, and recognize text. At the same time, there are libraries for almost every task. Alternatively, you can create the code yourself.

This list only exhausts some of the functionality of this language. But this is already enough to make it obvious that R is a useful tool for performing various tasks.

Advantages and disadvantages of the R language

First, let’s look at the advantages of the language:

• Ability to work with different programming paradigms. In this case, the object-oriented environment is the most suitable. With R, you can develop complex distributed programs. They can use the same objects and functions repeatedly.
• Interpretability. As mentioned earlier, you do not need to compile the program into an executable file using a compiler. You can check its work in parts right during the creation of the code.
• Ease of syntax. The language has a fairly simple structure. It does not have complex structures and functions. In addition, R contains only four data types: character, numeric, boolean, and complex. The programmer can collect all this into more complex structures in this case. Thus, the language is a constructor – with the help of simple elements, multi-level objects are formed.
• Support for major OS. We are talking about Windows, macOS, FreeBSD, Solaris, and various versions of Unix and Linux.
• Availability. The components can be distributed free of charge under the GNU license.
• The presence of a huge number of libraries and extensions. There are ready-made functions for information visualization, fast statistical operations, text recognition, A / B testing, and certain scientific fields.
• Availability of convenient interactive tools.

Now let’s list the disadvantages of the R language:

• English Documentation and Sources. For some people, this can be a big problem. To find answers to some questions, you can use Stack Overflow.
• The need for knowledge of the basics of statistics and programming. However, you can still learn to work with the R language if you understand some fundamental concepts: mean, mode, median, sample, and normal distribution.
• Limited scope. You will not be able to develop programs in the R language. On the one hand, this is a disadvantage, and on the other hand, an advantage. The fact is that the R language is ideal for data analysis. Therefore, it can be used by scientists, journalists, data scientists, analysts, and other professionals who need to process information.

Interactive tools for the R language

In addition to the command line interface, interactive tools, and graphical user interfaces have been devised for R. With their help; work becomes easier and more convenient. However, they are completely free and subject to the free GNU GPL license. We list the most common interactive tools:

• RStudio. This development environment can be used on Windows, Linux, and MacOS. RStudio has R code highlighting, program text navigation that simplifies work, action history, sorting tabular data by columns, displaying graphs in a separate window, and many other features inherent in a good development environment.

• Jupyter Notebook. This web interface, designed as a notepad application, is used to develop and exchange programs in R and other languages, with which you can work with information. Opens in the browser.
• Anaconda. This distribution is a large collection of the most common libraries and programs. They make it easier to work with data. In addition, there is a version of Python. It can be easily installed from a single file. This version contains RStudio, the Jupyter Notebook web interface, and other applications.

Differences R from other programming languages

R shares similarities with codeless data analysis tools (Power BI, Excel, Google Sheets, Tableau) and programming languages ​​designed to work with data (Python and Julia). However, there are several nuances.

Data analysis tools are much easier to use. For example, students are taught how to work with Excel. These programs have a graphical interface. They allow you to perform simple data operations quickly. This is both an advantage and a disadvantage. The fact is that if some function is missing, then only the developer of the program can add it.

In other words, you cannot create a tool with your hands that will allow you to perform a specific task. Plus, not all of these programs can be used to analyze huge amounts of data. For example, if you need to process a table with more than a million rows.

If we compare R with Python, Julia, and other languages ​​used for data analysis, it becomes obvious that they are more universal. This is expressed in the ability to create full-fledged programs, graphical interfaces, and applications without direct connection with mathematics and statistics.

However, if you look at it from a different point of view, then such versatility can be called a disadvantage. The fact is that R, being a more highly specialized language, is better suited for the statistical analysis of information.

Other languages ​​do not have as many ready-made analytical tools. Using R is much more convenient for carrying out complex mathematical operations.

Areas of application of the R language

Where is the R language used? Today it is one of the most popular tools used in science. For example, it is used by mathematicians, biologists, geneticists, and other researchers who must perform statistical analysis and create various models. Thus, the main scope of the R language is science.

It should be noted that this formal sign system is popular not only among scientists. For example, statistical analysis should be carried out by specialists in the field of Data Science. In addition, they have to perform mathematical calculations using data samples. The R language does this very well. It is also used in machine learning.

Based on the preceding, it becomes obvious that R is useful not only for scientists and sociologists. Almost all large companies employ programmers who speak this language.

It is also used in the following areas: finance, banking, medicine, construction, and more. R is often used for risk analysis in fintech companies and optimizing workflows in various industrial corporations.

Features of learning the R language

People who plan to conduct scientific activities should start studying the R language at the university. If you develop in a different field of knowledge, then courses are suitable. You can find many individual programs dedicated to this language on the Internet. In addition, there are courses in data analysis or Data Science. In this case, you will learn R as one of the necessary tools.

Please note that this language is best studied in parallel with mathematics. The fact is that you will need knowledge of algorithms, functions, and statistical analysis anyway. If you enroll in a data analysis course, the development of mathematics will most likely be included in the curriculum. As a last resort, you can use textbooks for universities.

Let’s summarize. The R programming language is one of the most widely used tools for performing statistical analysis. Moreover, it is a separate infrastructure and environment for data processing. R includes many statistical and visualization methods. It is used in the scientific community and business.