A recent article from the New York Times said “Data scientists, according to interviews and expert estimates, spend from 50 percent to 80 percent of their time mired in the mundane labor of collecting and preparing data, before it can be explored for useful information.”
Before an R program can look for answers, your data must be cleaned up and converted to a form that makes information accessible. In this webinar, you will learn how to use the `dplyr` and `tidyr` packages to optimise the data wrangling process. You’ll learn to:
- Spot the variables and observations within your data
- Quickly derive new variables and observations to explore
- Reshape your data into the layout that works best for R
- Join multiple data sets together
- Use group-wise summaries to explore hidden levels of information within your data