grass_r:start

R is a program which is constructed from libraries, you need certain libraries for certain types of analysis To add a library type

library(library name)

for example

library(rcmdr) Launches R Commander GUI

commander() relaunches R Commander GUI

Start GRASS GIS

Open a mapset

To start R type:

R

Firstly you need the library with allows R to access the GRASS data: spgrass6

library(spgrass6)

to check it has worked use

str(gmeta6())

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

system('g.list')

This will bring up the g.list GUI

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

summary(data)

This will give you:

- Minimum
- 1st Quartile
- Median
- Mean
- 3rd Quartile
- Maximum

for each of your columns in the attribute table

You can also plot the data using the plot() function

plot(data)

to get a nicer plot of your data use

library(maptools) spplot(data)

Raster data is imported in a similar way as vector data just by using the readRAST6 function

data<-readRAST6(c("mapA"),plugin=F)

There are many ways to visualise data

This section will demonstrate how to create nice looking graphs from your data.

plot(data)

hist(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, usefull 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[2] 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

Developed in 1992, this analysis helps identify clusters. It is part of a group of analyses collectivly called LISA (**L**ocal **I**ndicators of **S**patial **A**ssociation). 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)
see LocalG http://cran.r-project.org/web/packages/spdep/spdep.pdf

Here we look at the Agnes method (**Ag**glomerative **Nes**ting
), 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 |

To do this in GRASS…

Alternatively ArcGIS with Hawths tools and the sampling tools for a NxN matrix, this saves as a .csv but just change it to a .txt

Methods
“average” ([unweighted pair-]group average method, UPGMA)

“single” (single linkage)

“complete” (complete linkage)

“ward” (Ward's method)

“weighted” (weighted average linkage) and its generalization

“flexible” which uses (a constant version of) the Lance-Williams formula and the par.method argument

Default is “average”

grass_r/start.txt · Last modified: 2018/08/04 00:01 (external edit)