Too often I found myself googling simple R code I had learned and quickly forgotten. While googling code examples and tutorial is a daily pursuit for 99% of coders, I needed more consistancy. So I have made my own Data Wrangling & Visualization page! This markdown contains all wrangling and visual codes and examples that I find useful and necessary. All code will NOT be hid but printed for example. Enjoy.
“Data wrangling, sometimes referred to as data munging, is the process of transforming and mapping data from one”raw" data form into another format with the intent of making it more appropriate and valuable for a variety of downstream purposes such as analytics". - Wikipedia (Yes, wikipedia.).
Essentially, it is the imperative preprocessing step of cleaning your data. It is the basics and already assumed skillset of researchers, data scientists, and data visualization. You need to know this stuff.
Good data visualizations come in all shapes and sizes, but all of them have certain traits that help ensure you produce something with important insights. The following table, sourced from Mico Yuk and Stephanie Diamond from their book “Data Visualization for Dummies”, shows these key items:
library(pander)
Trait <- c("Useful", "Desirable", "Usable")
Description <- c("People use it on a regular basis and can make relevant decisions by viewing all the information they need in one place.", "It's not only easy to use, but also pleasurable to use.", "People who use it can accomplish their goals quickly and easily.")
key_items <- cbind(Trait, Description)
pander(key_items)| Trait | Description |
|---|---|
| Useful | People use it on a regular basis and can make relevant decisions by viewing all the information they need in one place. |
| Desirable | It’s not only easy to use, but also pleasurable to use. |
| Usable | People who use it can accomplish their goals quickly and easily. |
Simply put, you want your visuals to be appealing, simple yet critical, and only using information the consumer needs. Sometimes less is more and sometimes more is just right. All visualizations should be tailor made to your audiance.
The following packages will prove helpful: