Top R Libraries for Data Science

Top R Libraries for Data Science

An Overview Of Popular R Libraries for Data Science. When we talk about the top programming language for Data Science, we often find Python to be the best fit for the topic. Sure, Python is undoubtedly an excellent choice for a vast majority of Data Science-centric tasks, but there’s another programming language that was built specifically to provide superior number-crunching capabilities for Data Science, and that is R.

When we talk about the top programming language for Data Science, we often find Python to be the best fit for the topic. Sure, Python is undoubtedly an excellent choice for a vast majority of Data Science-centric tasks, but there’s another programming language that was built specifically to provide superior number-crunching capabilities for Data Science, and that is R.

In addition to providing robust statistical computing, R offers a huge collection, over 16 thousand *to be exact, of *highly resourceful libraries, catering to the needs of Data Scientists, Data Miners, and Statisticians alike. Further, in this article, we will shed some light on a handful of top R libraries for Data Science.

Best R Libraries for Data Science

R is extremely popular among Data Miners and Statisticians, and part of the reason is the extensive range of libraries that comes with R. These tools and functions can simplify statistical tasks to a great extent, making tasks such as** data manipulation, visualization, web crawling, Machine Learning** and more, a breeze. Some of the libraries have been briefly explained below:

1. dplyr

The dplyr package, also known as the grammar of data manipulation, essentially provides frequently used tools and functions for data manipulation, that includes the following functions:

  • filter(): for filtering your data based on the criteria
  • *mutate(): *to add new variables which will act as functions of existing variables
  • select(): for selecting variables based on the names
  • summarise(): helps summarise the data from multiple values
  • arrange(): for rearranging the ordering of the rows
  • Additionally, you can use the group_by() function, *which can return the results grouped according to the requirements. If you’re keen on checking out the dplyr package, you can either get it from the[ *tidyverse](https://dplyr.tidyverse.org/)or install the package directly with the** command “install.packages(“dplyr”).**

2. tidyr

tidyr is one of the core packages in the Tidyverse** ecosystem, and as the name suggests, it is **used to tidy up messy data. Now, if you’re wondering what tidy data is, let me clear it for you. A tidy data indicates that every column is variable, each row is an observation, and each cell is a singular value.

According to tidyr,  tidy data is a way of storing the data that is to be used throughout the tidyverse and can help you save time and be more productive with your analysis. You can get the package from tidyverse  or by the following command “install.packages(“tidyr”)”.

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