Simple Exploratory Data Analysis
Marco Cremonini · 2024
Having read a dataset, the first activity usually made afterward is to figure out the main characteristics of those data and make sense of them. This activity is often called simple exploratory data analysis, where the adjective “simple” distinguishes this basic and quick analysis performed to grasp the main features of a dataset with respect to thorough exploratory data analyses performed with more sophisticated statistical tools and methods. R and Python offer common functionalities to obtain descriptive statistics together with other utility functions, which allow getting information on a dataset, from its size to the unique values of a column/variable, names, indexes, and so forth. Missing values are very common in real datasets to the point that it is a much safer assumption to expect their presence than the opposite. The chapter lists some of the main R utility functions to gather descriptive statistics and other general information on data frames.