Which Of The Following Is Recommended Package In R

R Programming

When it comes to working with data in R, there are numerous packages available to help us accomplish our tasks more efficiently and effectively. However, not all packages are created equal, and some are definitely more recommended than others. In this article, I will delve into the world of R packages and share my personal recommendations based on my experience and expertise.

The Tidyverse

If you’re familiar with R, you’ve most likely heard of the Tidyverse. Made up of a collection of packages, the Tidyverse is designed to make data manipulation and analysis easier and more intuitive. With packages such as dplyr, tidyr, and ggplot2, the Tidyverse provides a cohesive and consistent framework for working with data.

Personally, I highly recommend the Tidyverse for several reasons. First and foremost, its syntax and approach to data manipulation are incredibly clean and straightforward. This makes it easier for both beginners and seasoned R users to quickly understand and write code. Additionally, the Tidyverse encourages good coding practices and emphasizes the use of pipes (%>%) to create readable and efficient workflows.

Another reason why I recommend the Tidyverse is its strong community support. The Tidyverse has a large and active user community, which means that help and resources are readily available. Whether you’re looking for tutorials, documentation, or troubleshooting assistance, you can easily find answers and guidance from other Tidyverse users.


While the Tidyverse is my top recommendation, it’s worth mentioning an alternative package called data.table. If you’re working with large datasets and performance is a key concern, data.table is a great option. It provides a fast and efficient way to manipulate and analyze data, especially when dealing with millions or even billions of rows.

The syntax of data.table may be a bit more complex compared to the Tidyverse, but once you get the hang of it, you’ll appreciate its speed and power. With features like automatic indexing and optimized algorithms, data.table can handle big data challenges with ease. If you find yourself working on projects that require heavy data processing and performance is critical, give data.table a try.


In conclusion, when it comes to R packages, the Tidyverse is my top recommended choice for its simplicity, versatility, and strong community support. However, if you’re working with big data and need maximum performance, data.table is a solid alternative. Ultimately, the choice of package depends on your specific needs and preferences.