Maximizing code efficiency with base r + dplyr

Data is messy. In order to glean insight from the millions of datapoints that carousel across our screens daily, we need to understand how to manipulate them. In my experience as a data scientist and analyst, tidyverse’s dplyr package provides some of the best tools on the market to wrangle, transform, and simplify large datasets into ‘tidy’ format. When combined with the simplicity and speed of R’s base functions, the opportunities are endless. Why tidy? Data is ‘tidy’ when each observation is represented by 1 row, each variable represented by 1 column, and each value held in one cell. It is the standard structure for mapping meaning onto data and gives us a consistent framework onto which we can build more complex mapping techniques. The dplyr() package sets the standard for tidy data wrangling in R and provides an arsenal of tools to work with even the most gnarly datasets out there. In this article, we will walk through some of the basic dplyr functions, including: <a href="https://medium.com/@cougco18/maximizing-code-efficiency-with-base-r-dplyr-4f9b288624cd">Learn More</a>