R for Spatial Analysis

This tutorial is to help R beginners to have an idea of using R for spatial analysis.It assumes that beginners have no prior knowledge of R, but some experience should help. You will experience using R for loading data, visualizing both vector and raster data and manipulating those data. You will learn a great details of R for spatial mapping as we progress.

R is a programming language, where people have developed many packages to make our life easier. So We will make use of those packages to benefit our work

Everytime, you want to use a R package, you have to load it in R environmet by writing library(package_name)

library(ggplot2)

library(ggmap)

## Warning: package 'ggmap' was built under R version 3.4.2

library(rgeos)

## Warning: package 'rgeos' was built under R version 3.4.2

## rgeos version: 0.3-25, (SVN revision 555)
##  GEOS runtime version: 3.6.1-CAPI-1.10.1 r0 
##  Linking to sp version: 1.2-5 
##  Polygon checking: TRUE

library(maptools)

## Loading required package: sp

## Checking rgeos availability: TRUE

library(tmap)

## Warning: package 'tmap' was built under R version 3.4.2

library(tidyverse)

## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr

## Conflicts with tidy packages ----------------------------------------------

## filter(): dplyr, stats
## lag():    dplyr, stats

x=1:500

y=sin(x/10)*exp(x*-0.0001)

plot(x,y,col=rainbow(100))

Read spatial data shp file in R

library(raster)

## 
## Attaching package: 'raster'

## The following object is masked from 'package:dplyr':
## 
##     select

## The following object is masked from 'package:tidyr':
## 
##     extract

# Reading VN map

VN<-getData("GADM",country="Vietnam",level=1)

HN<-VN[VN@data$VARNAME_1=="Ha Noi City|Hanoi",]

plot(HN)

# Extracting VN at level 3

VN3<-getData("GADM",country="Vietnam",level=3)

## Clipping Hanoi communes from HN (Hanoi boundary)

HN3<-crop(VN3,HN)

plot(HN3,col=rainbow(100),axes=T,main="Ha Noi Map")

Creating a new random variable

set.seed(123)

HN3$Distribution<-rnorm(nrow(HN3),10,9) # Create a variable named `Distribution`

myclass<-c("A","B","C","D","F")

HN3$Class1<-sample(myclass,nrow(HN3),replace = T) # Creating a new randonly categorical variable 

head(HN3@data)

plot(HN3,axes=T,col=2, main="Distribution>20/Blue")

HN4<-HN3[HN3$Distribution>20,]

plot(HN4,col=4,axes=T,add=T)

Removing and reprojecting coordinate system

library(sp)

proj4string(HN3)<-NA_character_ # Remove coordinate system

proj4string(HN3)<-CRS("+proj=utm +zone=48 +data=WGS84") # reprojecting a new coordinate system utm

plot(HN3,col=3,axes=T,main="Hanoi Map")

Using tmap package to visualize map

library(tmap)

qtm(HN3,"Class1") # Using a categorical variable

qtm(HN3,"Distribution")

qtm(shp = HN3, fill = c("Distribution", "ID_3"), fill.palette = "Blues", ncol = 2)