Data Science with R Data Preparation—A Template - One Page R

Hands-On Data Science with R Data Preparation [email protected] 24th August 2014 Visit http://HandsOnDataScience.com/ for more Chapters.

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We now begin with the task of preparing our data for building models using R. This chapter takes us through loading a dataset into R, observing the data, and transforming the data. The following chapter deals with building predictive models. R provides the foundation and we then introduce rattle (Williams, 2014) as a graphical interface built on this foundation, but as a tool to facilitate programming with data in R.

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All of the steps for preparing data are worked through one page at a time and then collected together into a single sequence in the final pages of the chapter. The aim is to provide the code here as the starting point for any new data science project. The required packages for this chapter include: library(rattle) library(randomForest) library(tidyr) library(ggplot2) library(dplyr) library(lubridate) library(FSelector)

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The weather dataset and normVarNames(). Impute missing values using na.roughfix(). Tidy the dataset. Visualise data. Data preparation and pipes %>%. Handle dates. Feature selection.

As we work through this chapter, new R commands will be introduced. Be sure to review the command’s documentation and understand what the command does. You can ask for help using the ? command as in: ?read.csv

We can obtain documentation on a particular package using the help= option of library(): library(help=rattle) This chapter is intended to be hands on. To learn effectively, you are encouraged to have R running (e.g., RStudio) and to run all the commands as they appear here. Check that you get the same output, and you understand the output. Try some variations. Explore. Copyright © 2013-2014 Graham Williams. You can freely copy, distribute, or adapt this material, as long as the attribution is retained and derivative work is provided under the same license.

Data Science with R

1

Hands-On

Data Preparation

Step 1: Load—Dataset

We use the weather dataset from rattle (Williams, 2014) to illustrate our data preparation. Often though we will be loading the dataset from a CSV file and so we illustrate that step first. We begin by noting the path to the CSV file we wish to load—in this case we load it from the Internet: dspath