In recent times, the data science field is growing at a very fast pace. Businesses need to embrace the same before getting left behind at a distance that will increase with time. Other than programming, the R language is also a software for statistical graphics and computing. If you are confused with which language to choose for a data science project, you must be confused between R and Python programming language. Many advantages make the R language a good choice for data science. Here you will see R’s advantages in data science and why it proves to be an excellent choice in this space. Below we have discussed reasons to do Data Science with R. Students who extra information regarding R programming or why it is highly demanding for data science can take help with an R programming assignment.
Excellent Features Of R Programming Language
R programming language is an open-source tool that enables you to download and change the code and develop your libraries. It is also free to use.
With R programming language, you can do analytical operations within its broad range of libraries. You can clarify, design, examine, reflect your data, and can also produce imminent models.
A complete language
While R programming language is usually recognized to be a language for statistics, it also includes some Object-Oriented Programming features.
Facilitates interaction with databases
It has several add-on packages that connect R programming language with databases like the RODBC package, ODBC, and ROracle package that enables interaction with Oracle databases. The R also gives extensions of MySQL as R MySQL.
Huge community support
It has an existing community supported by R programming language is an open-source language. This makes this programming language a perfect option for many. It gives several extensive boot camps and workshops.
Easy to understand
Why is R Important in Data Science?
Below we have listed some of the important R programming features for data science application are:
- R programming provides several important data-wrangling packages like purrr, dplyr, readxl, google sheets, jsonlite, datapasta, tidy, tidyquant, etc.
- This language gives great statistical modeling support.
- R is an excellent tool for performing multiple statistical operations on it.
- Because it is an engaging tool for different data science applications, it gives appreciative visualization tools like scatterplot3D, ggplot2, high charter lattice, etc.
- R programming extensively used in data science applications for Extract, Transform, and Load.
- It gives an interface for different databases like SQL.
- Another powerful advantage of this language is its interface with NoSQL databases and examining data that is not structured. This is very helpful in the application of Data Science, where a huge number of data has to be examined.
- With the help of R, data scientists can implement machine learning algorithms to get details about later events. There are many packages like rpart, randomForest, CARET, and net.
- R is the perfect choice when it comes to data wrangling. It supports the usage of different preprocessed packages that makes data-wrangling simpler.
R programming language used by data scientists, programmers, researchers, students, and statisticians worldwide. From the past few years, the use of R is increasing rapidly because of improvements made in data science and data analytics. Now you know why R programming is so popular among programmers and why it is the wise choice for data science. We have also discussed some of the excellent features of R.
With the most advanced changes added to its arsenal and a continuously growing community, this is the perfect time for learning the R programming language. You can also take help from online R programming assignment experts.