Introduction
If you wish to build an impeccable predictive model, trust me, neither any programming language nor any machine learning algorithm can award it to you unless you perform data exploration.
Just like a baby learns to walk before running, every data scientist should learn to explore data prior to getting accustomed to algorithms. Data Exploration has paramount importance in predictive modeling.
Data Exploration not only uncovers the hidden trends and insights, but also allows you to take the first steps towards building a highly accurate model. Considering the popularity of R Programming and its fervid use in data science, I’ve created a cheat sheet of data exploration stages in R. This cheat sheet is highly recommended for beginners who can perform data exploration faster using these handy codes. All you need to do is, customize the codes according your need.
Note: This Cheat Sheet is also available for Download in PDF version below.
table() doesn't serve the purpose for continuous random variables, hence of limited use!!!
Awesome information.
In "how to generate frequency tables", there is no need to subset the iris data set with "iris$..." if you attached it.