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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
3 (3)
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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

Implementation using K-means

After getting to know the ideal number of clusters, we can construct the required number of clusters in the dataset using the K-means method. We use the kmeans function to construct the clusters; this function takes the dataset as well as the number of clusters to be formed as an input. In the following case, we are just passing the number of clusters we want as an output from the pamk function:

fit<- kmeans(wdata, n)
table(fit$cluster)
1  2  3  4  5 
3  3 27  7 22

As per the preceding output, there are five clusters with varying number of elements in each of the clusters. Even if an isolated element is not found similar to any of the existing clusters, it will be made to form a new cluster. We can see the mean of the elements in the clusters using the aggregate function. As we have to choose the mean to be the aggregation factor for all the attributes in the dataset, we get the mean for each of the clusters formed. If the clusters are mutually exclusive,...

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