790: Open-Source Libraries for Data Science at the New York R Conference — with @JonKrohnLearns

Janitor Package

The janitor package is highlighted for its essential role in data cleaning, particularly in removing empty values and standardizing column names. Jared Lander emphasizes its utility, noting that he loads it every time he starts a new R project. He appreciates how it simplifies the process of cleaning datasets, making it a staple in his data analysis toolkit 1.

Every time I read in a dataset to do an analysis, I run my remove empty and my clean names at minimum to get those couple of things exactly where I know I need them.

--- Jared Lander

This package is particularly useful for handling complex data names, ensuring consistency and clarity in data preparation tasks.

   

dplyr Library

The dplyr library stands out for its intuitive syntax, making it accessible even to those without programming experience. Emily Zabor shares her experience working with business professionals who, despite not coding, can understand and engage with the dplyr code she writes 1. This accessibility is a testament to the library's design, allowing non-programmers to grasp data manipulations effectively.

The thing I like about it is I work with business folks who don't code at all, just excel. They can look at the player code that I write and talk to me about it.

--- Emily Zabor

Such features make dplyr a powerful tool for bridging the gap between technical and non-technical team members.