Advantages and disadvantages of the R programming language over Excel

Based on the fact that we all know Excel, the R programming language is, perhaps, something more unknown. R is a programming environment and language with a focus on statistical analysis . Free software and one of the languages most commonly used in statistical research. Has great relevance in areas of Big Data, Data Mining , Artificial Intelligence, the bioinformatics , logistics, predictions maintenance of industrial machinery, financial mathematics… There is the possibility of using different libraries with calculation functions and different graphs, since R is a powerful tool for data visualization. Our R Programming Training Institute in Bangalore was specially designed for those who are keen to learn the python course from Scratch to Advanced level. From my point of view, in order to understand which modules or functions of the R programming language provide real value with respect to the use of Excel, you must first understand everything that R can provide and in which areas R really helps us to collect , data processing and visualization:rn1. Speed in calculation: although Excel has great solvency in performing mathematical operations, R provides greater automation in many more operations.rn2. It is open source and free: changing code as you wish and managing the interface without obstacles is something that Excel cannot do. In R you can write functions to perform tasks and provide as a package for other users to use and optimize. The number of packages increases day by day.rn3. More accurate than Excel: Studies indicate that, in terms of linear regression analysis, Excel shows certain inaccuracies. The R programming language was created specifically for statistical analysis, so it is a winner at this point.rn4. Advanced statistical analysis: R has greater statistical capacity than Excel. This is largely because many users have contributed, thanks to open source, to creating specialized functions.rn5. Data processing: R allows the processing and manipulation of data with great speed. Generally, most of the time spent on an analysis project with R is spent on data preparation, so R is much more responsive and effective in data preparation than Excel.rn6. Automation and reading of data: the scripting language that uses R instead of a graphical user interface makes it easier to automate tasks. It is also capable of reading practically any type of data (.txt, .dat, .cvs ...). With various extra packages you could read JSON, SPSS, SAS ...rn7. Organization of the projects: as well as in Excel the projects are structured in sheets of the same book in R the different tasks or sub-projects can be saved in different files, grouped in the same folder and connected to each other in the same project with RStudio.rn8. Work with large volumes of data: As many tabs, formulas and references are added, Excel can go slow with large volumes of data. R far exceeds you at this point, being able to absorb large volumes of data from sources like Hadoop.rn9. Much easier error correction: R uses scripts instead of "clicks" as in Excel, allowing comments and version control. In this way, an "action history" can be accessed. This adds greater ease of amending errors. In Excel we can err in a cell and drag a difficult fault to detect if it does not take a long time.rnAnd finally, the graphics: if R is also famous for something, it is for his incredible graphics. Three-dimensional, stem, Boxplot, dot, circular, histograms, scatter, independence graphics.