Better Understand Your Data in R Using Descriptive Statistics

Last Updated on August 22, 2019

You must become intimate with your data.

Any machine learning models that you build are only as good as the data that you provide them. The first step in understanding your data is to actually look at some raw values and calculate some basic statistics.

In this post, you will discover how you can quickly get a handle on your dataset with descriptive statistics examples and recipes in R.

These recipes are perfect for you if you are a developer just getting started using R for machine learning.

Kick-start your project with my new book Machine Learning Mastery With R, including step-by-step tutorials and the R source code files for all examples.

Let’s get started.

• Update Nov/2016: As a helpful update, this tutorial assumes you have the mlbench and e1071 R packages installed. They can be installed by typing: install.packages(“e1071”, “mlbench”)

You Must Understand Your Data

Understanding the data that you have
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