#### Overview of this book

This cookbook offers a range of data analysis samples in simple and straightforward R code, providing step-by-step resources and time-saving methods to help you solve data problems efficiently. The first section deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation is provided, illustrating how to use the “dplyr” and “data.table” packages to efficiently process larger data structures. We also focus on “ggplot2” and show you how to create advanced figures for data exploration. In addition, you will learn how to build an interactive report using the “ggvis” package. Later chapters offer insight into time series analysis on financial data, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction. By the end of this book, you will understand how to resolve issues and will be able to comfortably offer solutions to problems encountered while performing data analysis.
R for Data Science Cookbook
Credits
www.PacktPub.com
Preface
Free Chapter
Functions in R
Data Preprocessing and Preparation
Visualizing Data with ggplot2
Making Interactive Reports
Simulation from Probability Distributions
Statistical Inference in R
Time Series Mining with R
Index

## Sampling data with dplyr

As a single machine cannot efficiently process big data problems, a practical approach is to take samples that we can effectively use to draw conclusions. Here, we will show you how to use `dplyr` to sample from data.

Ensure that you installed and loaded `data.table` in your R session. You also need to complete the Enhancing a data.frame with a data.table recipe to load `purchase_view.tab` and `purchase_order.tab` as both `data.frame` and `data.table` into your R environment.

### How to do it…

Perform the following steps to sample data with `dplyr`:

1. First, we can sample six rows from the data:

```> set.seed(123)
> sample_n(order.dt, 6, replace = TRUE)
Time Action       User     Product Quantity Price
1: 2015-07-10 09:22:37  order  U46651253 P0004306934        1   750
2: 2015-07-25 21:42:34  order U232322558 P0014273055        1  3688
3: 2015-07-13 22:55:33  order  U14804834 P0013147260        1 32900
4: 2015-07-29 08:48:18  order U364096419 P0003425855...```