R Programming for Statistical Data Processing and Graphics

R Programming for Statistical Data Processing and Graphics

Those who have ever been involved in data processing know how sometimes the standard tools of the Microsoft Excel format are sorely lacking. Therefore, programming languages are used to analyze a large amount of data. The R language is the main competitor of Python in statistics, data collection, and analysis. Here is more about it.

The basics of R programming language

Programming involves creating reliable software by writing, testing, debugging, and compiling a computer program. The R language is a powerful tool for statistics, graphics, and statistical programming. It is used daily by tens of thousands of people for serious statistical analysis. It is a free and open-source system, the implementation of which is the collective achievement of many intelligent, hardworking people. There are over 10,000 add-on packs available for this language. R is a serious competitor to all commercial packages for static data processing.

R is a language that allows users to use loop statements to analyze multiple data sets sequentially. Combining various statistical functions into a separate program for more complex analysis is also possible. At first, R may seem too complicated for a non-specialist (like a biologist). Actually, it is not. The main feature of R is its flexibility. For example, analysis can be done without displaying the result. Indeed, sometimes only part of the results of interest is needed.

R’s capabilities

Since the programming language was developed by statisticians for the relevant discipline, the scope of use will be somewhat limited:

  • Processing, cleaning, and transformation of data for research. For example, if you need to see how many users downloaded the application in one spring and one winter month. R allows you to exclude the statistics for the summer and autumn.
  • Conducting statistical tests. Suppose you want to find out how the average life expectancy of men and women differs. To do this, you can run a t-test. The results can show if there are differences between the data entered.
  • Perform exploratory analysis. Many data need to be tested for “adequacy” because most statistical methods require a normal distribution in the source.
  • Work with tables of various formats. Its capability is required for analytics. For example, to combine data from several tables, “.csv” and “.xlsx,” and then combine them into one file.
  • Drawing an interactive chart and developing an interactive application.
  • Analysis of regression models.

Advantages of the R language

The strength of R is its flexibility. The language makes life easier for the programmer, allowing you to forget about Excel forever. The main advantages of the R language are:

  • Intuitive and user-friendly tool. Especially for beginners. To write programs, following a clear structure is unnecessary – you can enter data sequentially.
  • The language was created specifically for analyzing large amounts of data, so its structure and syntax are clear to analysts and statisticians by default.
  • The presence of several packages for visualization. You can build 2D graphics and 3D models.
  • Convenient and understandable language constructs. It is a significant parameter for beginner programmers.
  • Basic statistical tools are implemented as standard functionality. It dramatically simplifies and speeds up the development process.
  • A good range of additional packages for every taste. R developers try to release libraries and packages as often as possible to optimize the work of the PL.
  • It is enough to use the console. But it is better to use special development environments. One of the most practical uses is R Studio (which can be downloaded from the official website).