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  • Book Overview & Buying R Data Science Essentials
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R Data Science Essentials

R Data Science Essentials

By : Koushik, Kumar Ravindran
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R Data Science Essentials

R Data Science Essentials

3 (3)
By: Koushik, Kumar Ravindran

Overview of this book

With organizations increasingly embedding data science across their enterprise and with management becoming more data-driven it is an urgent requirement for analysts and managers to understand the key concept of data science. The data science concepts discussed in this book will help you make key decisions and solve the complex problems you will inevitably face in this new world. R Data Science Essentials will introduce you to various important concepts in the field of data science using R. We start by reading data from multiple sources, then move on to processing the data, extracting hidden patterns, building predictive and forecasting models, building a recommendation engine, and communicating to the user through stunning visualizations and dashboards. By the end of this book, you will have an understanding of some very important techniques in data science, be able to implement them using R, understand and interpret the outcomes, and know how they helps businesses make a decision.
Table of Contents (10 chapters)
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9
Index

Bivariate analysis

In this section, we will cover bivariate analysis to understand the combined effect of two variables as well as the effect of one variable on the other variable. In any real-life example, there will be multiple variables dependent on each other. Hence, this analysis will be useful in getting an understanding about these cases.

The best method to get a quick understanding about two variables is the scatter plot. This visual representation gives us a clear idea about the impact of one variable on the other variable. We can use the same ggplot function to plot the scatter plot. We will plot the scatter chart to get the relationship between the Age and Fare variables:

ggplot(tdata, aes(x=Fare, y=Age)) +
geom_point(shape=1) +    
geom_smooth(method=lm)
ggsave(file="scatter-plot.png", dpi=500)

In the preceding case, we are plotting the relationship between these two variables along with the scatter plot, using the geom_smooth parameter, which plots an additional linear...

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