Book Image

R for Data Science Cookbook (n)

By : Yu-Wei, Chiu (David Chiu)
Book Image

R for Data Science Cookbook (n)

By: Yu-Wei, Chiu (David Chiu)

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.
Table of Contents (19 chapters)
R for Data Science Cookbook
About the Author
About the Reviewer

Creating maps

One can use a map to visualize the geographical relationship of spatial data. Here, we introduce how to create a map from a shapefile with ggplot2. Moreover, we introduce how to use ggmap to download map data from an online mapping service.

Getting ready

Ensure you have installed and loaded ggplot2 into your R session. Please download all files from the following GitHub link folder:

How to do it…

Perform the following steps to create a map with ggmap:

  1. First, load the ggmap and maptools libraries into an R session:

    > install.packages("ggmap")
    > install.packages("maptools")
    > library(ggmap)
    > library(maptools)
  2. We can now read the .shp file with the readShapeSpatial function:

    > nyc.shp <- readShapeSpatial("nycc.shp")
    > class(nyc.shp)
    [1] "SpatialPolygonsDataFrame"
    [1] "sp"
  3. At this point, we can plot the map with the geom_polygon function:

    > ggplot() +  geom_polygon(data = nyc.shp, aes(x...