Figure 0.1: Pipe Operator Instead of introducing tidyr and dplyr packages-two most essential R packages for data wrangling, I would like to insert a side topic that I think it’s worth to mention for R programming efficiency as my 2nd Tidyverse blog 1. To me, this important programming command completely changes my view of programming and reshapes my programming habit since I used it. This magic command is %>%, a.
In an ideal world, a data analysis process is as simple as-read in data, select a suitable model to fit in data, obtain statistical estimates, and finally, interpret the analysis results. Sounds simple and straight forward, isn’t it?
But, in reality, it’s often not that simple!Data is always messy and often times we need to clean our data before we can make any sense of it. Moreover, some researchers found that more than 80% of data analysis is actually spent on data preparation or data manipulation (Dasu & Johnson, 2003), so that the data is transformed into a usable format before you even think about analysis.
Yihui Xie Keith McNulty David Robinson Useful links This is the first blog I created after I put my personal website online. This is the second time I’ve built my personal website. Last time, when I built my first one, I was still in graduate school fighting for my dissertation.
It seems like a Déjà vu, but two websites serve as two different purposes. My first website was mainly used for advertising and job hunting.