Hands-On Exploratory Data Analysis with R

Examining, cleaning, and filtering data

The next steps after importing the data are to examine it and check for missing or erroneous data. We then need to clean the data and apply filters and selections. Different kinds of datasets need different approaches to carry out these steps. R has powerful packages to handle this and some of them are as follows:

• dplyr: dplyr is a powerful R package that provides methods to make examining, cleaning, and filtering data fast and easy.
• tidyr: The tidyr package helps to organize messy data for easier data analysis.
• stringr: The stringr package provides methods and techniques of working with string data efficiently.
• forcats: Factors are widely used while doing data analysis in R. The forcats package makes ...