R is a program which is constructed from libraries, you need certain libraries for certain types of analysis.
To add a library type
library(rcmdr) Launches R Commander GUI commander() relaunches R Commander GUI
See also the Digging numbers section for an introduction to R.
Using R with GRASS GIS
Start GRASS GIS Open a mapset To start R type:
Firstly you need the library with allows R to access the GRASS data:
library(spgrass6) to check it has worked use
This will list various information about your current mapset Even though you are working in R you can access GRASS commands using the system command
This will bring up the g.list GUI
Reading Vector Data
To access data from GRASS you need to get R to create a SpatialGridDataFrame class object, in this case we call the DataFrame data
data <- readVECT6(c("mapA"),plugin=F)
use summary() to see check it has imported correctly
This will give you:
- 1st Quartile
- 3rd Quartile
for each of your columns in the attribute table You can also plot the data using the plot() function
to get a nicer plot of your data use
Reading Raster Data
Raster data is imported in a similar way as vector data just by using the readRAST6 function
There are many ways to visualise data
Bars, lines and pies
This section will demonstrate how to create nice looking graphs from your data.
Also these come with many options to change the defaults:
- graph layout
- main the title of the graph …main=”Main Title”…
- xlim the x axis limits …xlim=c(1,10)…
- ylim the y axis limits …ylim=c(1,100)…
- xlab the label for the x axis …xlab=”x axis label”…
- ylab the label for the y axis …ylab=”y axis label”…
- xaxt x axis type …xaxt=”n”… other options:
- yaxt y axis type …yaxt=”n”… other options:
- colours and symbols
More than one graph at a time
Some times you want to see a selection of graphs at the same time, R is able to do this through the par command
par(mfrow=c(1,1)) #this will set the environment to display one graph par(mfrow=c(1,2)) #this will set 1 row 2 columns par(mfrow=c(2,2)) #this will give a 2 by 2 appearance
you only need to do this once then the next graph you create will be in the first position and any following graphs will follow after.
Sometimes there is data which you do not want to display.
newdata <- data[data>=1]
This will remove Zeros and minus numbers, useful if you want to present numbers of artefacts ignoring when there are none found in an area.
length(data) this gives the amount of items in your dataset data selects the 2nd row data[-2] all except the 2nd row data[1:5] the first 5 rows data[(length(x)-5):length(x)] last 5 rows data[c(1,3,5)] exact rows data[x>3] values greater than 3 data[ x< -2 | x > 2] greater or less than some values which(data == max(data)) identifies which is the maximum value
Getis and Ord’s Gi*
Developed in 1992, this analysis helps identify clusters. It is part of a group of analyses collectivly called LISA (Local Indicators of Spatial Association). It is known as Local G, Gi* or GiStar, it can be found in the spdep library.
localG(x, listw, zero.policy=NULL, spChk=NULL)
Hierarchical Cluster Analysis (HCA)
Here we look at the Agnes method (Agglomerative Nesting ), this treats very observation as a single cluster (one artefact clusters at a distance of zero). The two closest clusters are then combined, then the next two closest are combined until only a single cluster (the dataset) remains.
Other types are:
Hierarchical methods: Agnes, Diana, and Mona.
Partitioning methods: Pam, Clara, and Fanny.
library(cluster) mydata <- read.table(file="mydata.txt", header=TRUE,row.names=1, sep=",") mydata.agnes <- agnes(mydata) mydata.agnes.euc <- agnes(mydata, metric="euclidean",method="ward", stand=TRUE) plot(mydata.agnes.euc)
The text file you read into the mydata dataframe must be a matrix in the following form:
A B C D E A 2 5 4 2 B 2 7 5 1 C 5 7 9 8 D 4 5 9 1 E 2 1 8 1