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

R Data Science Essentials

By : Raja B. Koushik, Sharan Kumar Ravindran
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R Data Science Essentials

R Data Science Essentials

3 (3)
By: Raja B. Koushik, Sharan 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

Generating filtering rules

These rules are generated using the apriori function of the arules package. Let's generate the rules for the other dataset this time:

rules2<- apriori(sampdata,parameter = list(sup = 0.45, conf = 0.9, target="rules"));

The following is the output of the preceding command:

Generating filtering rules

We set the threshold for support as 0.45 and the threshold for confidence as 0.9. From the preceding output, we can see that there are about 40 rules generated. We can print them in descending order of their lift ratio using the following code:

inspect(head(sort(rules2, by="lift"),10))

The output is as follows:

Generating filtering rules

From the output, it is clear that whenever the i128 and i141 items co-occur, it is most likely that the i139 item will occur. Additionally, as the lift value is more than two, it further reiterates that the combination is most likely to occur. We can also get the top rules based on the combination of support, confidence, and lift using the quality function:

head...
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R Data Science Essentials
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