Download - R crash course

R crashcourse

T. Hengl &R.S. Bivand

Overview

Course programme

Why R?

Software installation

Scripting in R

Do’s and don’ts

R code editors

Working withspatial data

Spatial classes

Spatial methods

R+SAGA

R+FWTools

Export to GoogleEarth

Literature

R crash course(for people who pick up new methods/tools quickly

and have a high motivation to learn)

T. Hengl1 & R.S. Bivand2

1Instituut voor Biodiversiteit en Ecosysteem DynamicaUniversiteit van Amsterdam

2Department of EconomicsNorwegian School of Economics and Business administration

June 19, 2010

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

Outline

1 OverviewCourse programmeWhy R?Software installation

2 Scripting in RDo’s and don’tsR code editors

3 Working with spatial dataSpatial classesSpatial methodsR+SAGAR+FWToolsExport to Google Earth

4 Literature

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

Programme

Installation and first (baby) steps (intro.R).

Building, running and editing models in R.

Working with spatial data.

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

Crash course

A “crash course” is a compressed version of a training course forpeople that already have full agendas. It is really intended for

people who pick up new methods/tools quickly and have a highmotivation to learn (PhD students?). This means no longquestions, no going back, no long discussion, no complains

about the speed/programme of the course. . . and of course: nopromises that you will manage to learn R in such a short time.

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

The course programme

Basic concepts (self-study)

R basic and add-on packages;

R syntax; R objects and ’methods’;

R FAQs; getting help and the most important literature;

Scripting in R

data management; creating and debugging scripts(scripting editors: Tinn-R);

automating analysis — making functions and packages;

publication quality outputs (using R+Sweave);

Advanced topics

GDAL and R; spatial classes and packages;

calling external applications from R (SAGA GIS, FWTools,Python, Matlab;

export of maps to Google Earth;

http://cran.r-project.org/doc/FAQ/R-FAQ.html

http://www.r-project.org/doc/bib/R-publications.html

http://www.statmethods.net/management/

http://www.sciviews.org/Tinn-R/

http://www.stat.uni-muenchen.de/~leisch/Sweave/

http://rgdal.sourceforge.net/

http://fwtools.maptools.org/

http://www.omegahat.org/RSPython/

http://cran.r-project.org/doc/contrib/Hiebeler-matlabR.pdf

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

Outline




4 Literature

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

Getting the right motivation (inspiration)

What is R, and why should you invest time to learn it?

What can it do? (and what it can’t do?)

How does the R community works (what are its sharedprinciples)?

Is R suited for spatio-temporal data analysis?

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

Some important assumptions

R is (increasingly) sexy.

R is hell.

You are all beginners (who need a gentle intro).

You are non-statisticians / computer scientists.

I learned R myself. If I can do it, you can do it too.

http://bits.blogs.nytimes.com/2009/01/08/r-you-ready-for-r/

http://www.burns-stat.com/pages/Tutor/R_inferno.pdf

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

What is R?

the open source implementation of the S language forstatistical computing created by created by Ross Ihakaand Robert Gentleman (now maintained by the RDevelopment Core Team);

why R?: the name “R” was selected for two reasons: (1)precedence —“R”is a letter before“S”, and (2) coincidence— both of the creators’ names start with a letter “R”;

it is a computer language developed to simplify statisticalcomputing/programming;

it offers many contributed packages, technical documents,demos and examples;

widely recognized as one of the fastest growing and mostcomprehensive statistical computing tools;

it is a movement;

http://www.r-project.org/about.html

http://www.r-project.org/contributors.html

http://www.r-project.org/contributors.html

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

R is “sexy”

http://bits.blogs.nytimes.com/2009/01/08/r-you-ready-for-r/

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

Quote

“R has really become the second language for peoplecoming out of grad school now, and there’s anamazing amount of code being written for it.”

Max Kuhn

http://www.nytimes.com/2009/01/07/technology/business-computing/07program.html

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

What it is NOT

it’s not a software with GUI and point-and-click control;

there are practically no data analysis wizards;

it’s not an interactive system;

it comes with no warranties (but so does the commercialsoftware too!!);

there is practically no formal support;

however. . .

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

Why make scripts?

It’s easy to use: “Because S (and its implementation R) isa well-developed, simple and effective programminglanguage which includes conditionals, loops, user-definedrecursive functions and input and output facilities, existingfunctions can be modified.” In R we all becomeprogrammers (but much faster than with C++ or Java).

The basic approach to using R is to generate scripts thatdefine the data processing steps (workflows?).

Documenting the analysis process is a “good thing”, soprogramming scripts are not just a burden, certainly forusers doing original research and repetitive work, arguablyfor student classes too.

Point-and-click operations are for amateurs.

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

Do you speak R?

After some time you basically discovered that most of the thingsyou want to do, you can do in R, the only question is how.

Well, first, you have to learn to how to speak’n’write R.

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

Why NOT Matlab?

R and Matlab are in fact very similar, except. . . :

(almost) everything you do in Matlab, you can do in R;

R can be used to write homework (for Matlab you need acomputer lab with software licences);

“R gradually pushes people towards better programminghabits” (Tamas K. Papp);

Matlab has a limited support for processing spatial data (Ris better);

Contributed packages in R are growing faster (R is better);

We are in academia — we shouldn’t be developingtoolboxes for some private company but for the people(and for ourselves);

http://anyall.org/blog/2009/02/comparison-of-data-analysis-packages-r-matlab-scipy-excel-sas-spss-stata/

http://tolstoy.newcastle.edu.au/R/help/04/04/1284.html

http://www.r-bloggers.com

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

A bit of history

R was first time released in 1997;

majority of the development is (still) done by prof. BrianD. Ripley;

a the moment (juni 19 2010 10:01), there are 2387contributed packages!

according to Google trends, R-project.org has acommunity of about 200–350k active users;

in 2003, a group of researchers (International Workshop onDistributed Statistical Computing) decided to add Spatialfunctionality to R;

. . . now is time to use it more broadly (MSc level and PhDlevel modules, projects, reports and scientific documents);

http://www.google.com/adplanner/

http://cran.r-project.org/web/views/Spatial.html

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

Quote

“Once methodological problems start being perceivedor even defined in terms of what one’s favoritesoftware does well, then the software has stoppedbeing a tool, and has become a crutch, and at worse ashackle.”

Simon Jackman

http://ackman.stanford.edu/papers/download.php?i=34

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

The ASDA(R)-book.org

http://www.asdar-book.org

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

The ASDA(R) team

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

A Practical Guide to Geostatistical Mapping

http://spatial-analyst.net/book/

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

Quote

“I created Quick-R for one simple reason. I wanted tolearn R and I am a teacher at heart. The easiest wayfor me to learn something is to teach it”

Robert I. Kabacoff

http://www.statmethods.net/about/author.html

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

Outline




4 Literature

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

Software

Not all software is required to follow the exercises

R v2.11 (Windows OS) including a list of packages;

Tinn-R v2.3 (code editor);

Optional: FWTools v2.4 — a list of utilities to handlespatial data; SAGA GIS v2.0.4 — a light GIS excellent foreducational purposes.

http://cran.r-project.org/bin/windows/base/

http://www.sciviews.org/Tinn-R/

http://fwtools.maptools.org

http://www.saga-gis.org

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

Installing the add-on packages

> install.packages("ctv")

> library(ctv)

> install.views("Spatial")

This will install all connected packages listed at R views Spatial.

http://cran.r-project.org/web/views/Spatial.html

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

Check your installation

> Sys.getenv(c("OS", "COMPUTERNAME", "R_HOME", "R_LIBS_USER",

+ "PROCESSOR_IDENTIFIER"))

OS

"Windows_NT"

COMPUTERNAME

"PC-IBED193"

R_HOME

"C:\\PROGRA~1\\R\\R-210~1.1"

R_LIBS_USER

"n:/R/win-library/2.10"

PROCESSOR_IDENTIFIER

"x86 Family 6 Model 15 Stepping 6, GenuineIntel"

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

Outline




4 Literature

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

Making friends with R

1 Use script editors such as as TINN-R or JGR (syntaxhighlighting, wrapping, search/replace etc.).

2 The best way to learn R is to look at the existing scripts,then extend:

Graphical Manual/Addicted to R — if you prefer to exploreexamples graphically;Teach-your-self books e.g. “Statistics with R” or “AnIntroduction to R”; “Introduction to R for ITC students”Quick-R and/or R by example;

3 If your R script does not work, do not break your head, tryto get help:

local installation: > help.search("kriging");R-project: > RSiteSearch("krige dimensions do notmatch")www: Rseek.orgR mailing lists;

http://spatial-analyst.net/wiki/index.php?title=Scripting_in_R

http://bm2.genes.nig.ac.jp/RGM2/

http://addictedtor.free.fr/graphiques

http://zoonek2.free.fr/UNIX/48_R/all.html

http://www.csiro.au/resources/Rcoursenotes.html

http://www.csiro.au/resources/Rcoursenotes.html

http://www.itc.nl/personal/rossiter/teach/R/RIntro_ITC.pdf

http://www.statmethods.net/

http://www.mayin.org/ajayshah/KB/R/index.html

http://rseek.org

http://www.r-project.org/mail.html

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

Good practice

Put comments in script (after #).

Add some meta-information at the beginning of yourscript.

Once you tested your script and saw that it works, tidy-upthe code.

Place the input data on-line (database) so you cancommunicate only the script.

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

R mailing lists: Do’s and Don’ts!

Do’s:

If you have not done so already, read the R posting guide.Use the existing pre-installed data sets (come together witha certain package) to describe your problem; link yourproblem to some existing problems.Acknowledge the work (time spent) other people do to helpyou.You can submit not only the problems you discover but alsothe information that you think is interesting for thecommunity.

Don’ts:

Do not send poorly formulated questions.Do not send too much.Complaining to a mailing list about what frustrates youabout R makes no sense.You are requesting that somebody does a work for you thatyou could do yourself!

http://spatial-analyst.net/wiki/index.php?title=Scripting_in_R

http://www.r-project.org/posting-guide.html

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

Outline




4 Literature

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

Tinn-R

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

JaGuaR

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

Outline




4 Literature

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

Spatial objects

An advantage of R (as compared to e.g. Matlab) is that youcan create your own formats and structures for data. But ifthere are too many formats you can easily get lots. In addition,we want to have smooth links to external formats (R is open!).

To reduce this problem, Bivand et al. (2008) developednew-style classes to represent spatial data.

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

Spatial class

The foundation object is the Spatial class, with just two basicslots (new-style S classes have pre-defined components calledslots):

a bounding box — mostly used for setting up plots;

a CRS class object — defining the coordinate referencesystem, and may be set to CRS(as.character(NA));

Operations on Spatial* objects should update or copy thesevalues to the new Spatial* objects being created. The mostbasic spatial data object is a point, which may have 2 or 3dimensions.

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

Spatial classes

for point features: SpatialPoints;SpatialPointsDataFrame;

for line features: SpatialLines,SpatialLinesDataFrame;

polygons: SpatialPolygons,SpatialPolygonsDataFrame;

rasters: SpatialPixels, SpatialPixelsDataFrame,SpatialGrid, SpatialGridDataFrame;

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

SpatialPoints

> library(sp)

> data(meuse)

> coords <- SpatialPoints(meuse[, c("x", "y")])

> summary(coords)

Object of class SpatialPoints

Coordinates:

min max

x 178605 181390

y 329714 333611

Is projected: NA

proj4string : [NA]

Number of points: 155

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

SpatialPointsDataFrame

We can add the tabular data to make aSpatialPointsDataFrame object:

> meuse1 <- SpatialPointsDataFrame(coords, meuse)

> str(meuse1, max.level = 2)

Formal class 'SpatialPointsDataFrame' [package "sp"] with 5 slots

..@ data :'data.frame': 155 obs. of 14 variables:

..@ coords.nrs : num(0)

..@ coords : num [1:155, 1:2] 181072 181025 181165 ...

.. ..- attr(*, "dimnames")=List of 2

..@ bbox : num [1:2, 1:2] 178605 329714 181390 333611

.. ..- attr(*, "dimnames")=List of 2

..@ proj4string:Formal class 'CRS' [package "sp"] with 1 slots

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

Point features

Spatial points

Using the Meuse bank data set of soil samples and measurementsof heavy metal pollution provided with sp, we’ll make aSpatialPoints object.> library(sp)

> data(meuse)

> coords <- SpatialPoints(meuse[, c("x", "y")])

> summary(coords)

Object of class SpatialPoints

Coordinates:

min max

x 178605 181390

y 329714 333611

Is projected: NA

proj4string : [NA]

Number of points: 155

Spatial points

Now we’ll add the original data frame to make aSpatialPointsDataFrame object. Many methods for standard dataframes just work with SpatialPointsDataFrame objects.> meuse1 <- SpatialPointsDataFrame(coords, meuse)

> names(meuse1)

[1] "x" "y" "cadmium" "copper" "lead" "zinc"

[7] "elev" "dist" "om" "ffreq" "soil" "lime"

[13] "landuse" "dist.m"

> summary(meuse1$zinc)

Min. 1st Qu. Median Mean 3rd Qu. Max.

113.0 198.0 326.0 469.7 674.5 1839.0

> stem(meuse1$zinc, scale = 1/2)

The decimal point is 2 digit(s) to the right of the |

0 | 12223333344444455666677778888899999999

2 | 000000011111112222233444555666678880022334455788

4 | 00012235677001455556789

6 | 01144678890012455678889

8 | 0133113

10 | 235604469

12 | 8

14 | 5357

16 | 7

18 | 4

Spatial points classes and their slots

coordsSpatial

coords.nrsdata

SpatialPoints bboxproj4string


data.frame

Spatial

SpatialPoints

Spatial lines and polygons

I A Line object is just a spaghetti collection of 2D coordinates;a Polygon object is a Line object with equal first and lastcoordinates

I A Lines object is a list of Line objects, such as all thecontours at a single elevation; the same relationship holdsbetween a Polygons object and a list of Polygon objects, suchas islands belonging to the same county

I SpatialLines and SpatialPolygons objects are made usinglists of Lines or Polygons objects respectively

I SpatialLinesDataFrame and SpatialPolygonsDataFrame

objects are defined using SpatialLines and SpatialPolygons

objects and standard data frames, and the ID fields are hererequired to match the data frame row names

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

Line/polygon featuresSpatial polygons

The Meuse bank data set also includes the coordinates of the edgeof the river, linked together at the edge of the study area to form apolygon. We can make these coordinates into a SpatialPolygons

object:> data(meuse.riv)

> str(meuse.riv)

num [1:176, 1:2] 182004 182137 182252 182314 182332 ...

> river_polygon <- Polygons(list(Polygon(meuse.riv)), ID = "meuse")

> rivers <- SpatialPolygons(list(river_polygon))

> summary(rivers)

Object of class SpatialPolygons

Coordinates:

min max

r1 178304.0 182331.5

r2 325698.5 337684.8

Is projected: NA

proj4string : [NA]

Spatial linesThere is a helper function contourLines2SLDF to convert the list ofcontours returned by contourLines into a SpatialLinesDataFrame

object. This example shows how the data slot row names matchthe ID slot values of the set of Lines objects making up theSpatialLinesDataFrame, note that some Lines objects includemultiple Line objects:> library(maptools)

> volcano_sl <- ContourLines2SLDF(contourLines(volcano))

> row.names(slot(volcano_sl, "data"))

[1] "C_1" "C_2" "C_3" "C_4" "C_5" "C_6" "C_7" "C_8" "C_9"

[10] "C_10"

> sapply(slot(volcano_sl, "lines"), function(x) slot(x,

+ "ID"))

[1] "C_1" "C_2" "C_3" "C_4" "C_5" "C_6" "C_7" "C_8" "C_9"

[10] "C_10"

> sapply(slot(volcano_sl, "lines"), function(x) length(slot(x,

+ "Lines")))

[1] 3 4 1 1 1 2 2 3 2 1

> volcano_sl$level

[1] 100 110 120 130 140 150 160 170 180 190

Levels: 100 110 120 130 140 150 160 170 180 190

Spatial Polygons classes and slots

coordsSpatiallines

plotOrderSpatial

polygons

bboxproj4string

Polygon

coords

labptareaholeringDir

LineLines

ID

Polygons

plotOrderlabptIDarea

SpatialLines

Spatial

SpatialPolygons

Lines

Polygons

Spatial grids and pixels

I There are two representations for data on regular rectangulargrids (oriented N-S, E-W): SpatialPixels and SpatialGrid

I SpatialPixels are like SpatialPoints objects, but thecoordinates have to be regularly spaced; the coordinates arestored, as are grid indices

I SpatialPixelsDataFrame objects only store attribute datawhere it is present, but need to store the coordinates and gridindices of those grid cells

I SpatialGridDataFrame objects do not need to storecoordinates, because they fill the entire defined grid, but theyneed to store NA values where attribute values are missing

R crashcourse


Overview

Course programme

Why R?


Scripting in R

Do’s and don’ts

R code editors


Spatial classes

Spatial methods

R+SAGA

R+FWTools


Literature

Grids

Spatial pixels

Let’s make a SpatialPixelsDataFrame object for the Meuse bankgrid data provided, with regular points at a 40m spacing. The datainclude soil types, flood frequency classes and distance from theriver bank:> data(meuse.grid)

> coords <- SpatialPixels(SpatialPoints(meuse.grid[, c("x",

+ "y")]))

> meuseg1 <- SpatialPixelsDataFrame(coords, meuse.grid)

> names(meuseg1)

[1] "x" "y" "part.a" "part.b" "dist" "soil" "ffreq"

> slot(meuseg1, "grid")

x y

cellcentre.offset 178460 329620

cellsize 40 40

cells.dim 78 104

> object.size(meuseg1)

[1] 339036

> dim(slot(meuseg1, "data"))

[1] 3103 7

Spatial grids

In this case we convert the SpatialPixelsDataFrame object to aSpatialGridDataFrame by making a change in-place. In othercontexts, it is much more usual to create the GridTopology objectin the grid slot directly, and populate the grid from there, as we’llsee later:> meuseg2 <- meuseg1

> fullgrid(meuseg2) <- TRUE

> slot(meuseg2, "grid")

x y

cellcentre.offset 178460 329620

cellsize 40 40

cells.dim 78 104

> class(slot(meuseg2, "grid"))

[1] "GridTopology"

attr(,"package")

[1] "sp"

> object.size(meuseg2)

[1] 425684

> dim(slot(meuseg2, "data"))

[1] 8112 7

Spatial grid and pixels classes and their slots

dataSpatialPixels

SpatialGriddata grid

grid.indexSpatialPoints

gridgrid.indexSpatialPoints

cellcentre.offsetcellsizecells.dim

coordsSpatial

bboxproj4string

data.frame

Spatial

GridTopology

SpatialPoints

SpatialGridDataFrameSpatialGrid

SpatialPixelsSpatialPixelsDataFrame

Spatial classes provided by sp

This table summarises the classes provided by sp, and shows howthey build up to the objects of most practical use, theSpatial*DataFrame family objects:

data type class attributes extendspoints SpatialPoints none Spatial

points SpatialPointsDataFrame data.frame SpatialPoints

pixels SpatialPixels none SpatialPoints

pixels SpatialPixelsDataFrame data.frame SpatialPixels


full grid SpatialGrid none SpatialPixels

full grid SpatialGridDataFrame data.frame SpatialGrid

line Line nonelines Lines none Line listlines SpatialLines none Spatial, Lines listlines SpatialLinesDataFrame data.frame SpatialLines

polygon Polygon none Line

polygons Polygons none Polygon listpolygons SpatialPolygons none Spatial, Polygons listpolygons SpatialPolygonsDataFrame data.frame SpatialPolygons

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Basic methods

spplot — plotting of spatial objects (maps);

spsample — sample points from a set of polygons, on aset of lines or from a gridded area;

bbox — get the bounding box;

proj4string — get or set the projection (coordinatereference system);

coordinates — set or retrieve coordinates;

spTransform — transform coordinates from one CRS toanother;

overlay — combine two different spatial objects;

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Plotting a SpatialPoints object

> plot(as(meuse1, "Spatial"), axes = TRUE)

> plot(meuse1, add = TRUE)

> plot(meuse1[meuse1$ffreq == 1, ], col = "green", add = TRUE)

178000 179000 180000 181000 182000

3300

0033

1000

3320

0033

3000

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Combining statistical and GIS operations

Because the Spatial*DataFrame family objects behave inmost cases like data frames, most of what we are used todoing with standard data frames just works (but no merge,etc., yet).

These objects are very similar to typical representations ofthe same kinds of objects in geographical informationsystems, so they do not suit spatial data that is notgeographical (like medical imaging) as such.

Because now sp classes for GIS data exits, this opens thedoor for fusing GIS and statistical operations (this has notbeen possible in e.g. 2002).

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Spatial packages

R now offers a range of contributed packages in spatialstatistics and increasing awareness of the importance of spatialdata analysis in the broader community. Current contributedpackages with spatial applications:

point patterns: spatstat, VR:spatial, splancs;

geostatistics: gstat, geoR, geoRglm, fields, spBayes,RandomFields, VR:spatial, sgeostat, vardiag;

lattice/area data: spdep, DCluster, spgwr, ade4;

links to GIS: rgdal, spgrass, RPy, RSAGA;

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Let’s create spatial objects!

We can create spatial objects from scratch! For example aDEM:

> dem <- expand.grid(x = seq(100, 600, 100), y = seq(100,

+ 600, 100))

> dem$Z <- as.vector(c(23, 24, 34, 38, 45, 51, 24, 20,

+ 20, 28, 18, 49, 22, 20, 19, 14, 38, 45, 19, 15, 13,

+ 21, 23, 25, 14, 11, 18, 11, 18, 19, 10, 16, 23, 16,

+ 9, 6))

> gridded(dem) <- ~x + y

> dem <- as(dem, "SpatialGridDataFrame")

> str(dem)

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A small DEM

> dem.plt <- spplot(dem[1], main = "DEM", scales = list(draw = FALSE),

+ col.regions = topo.colors(25))

> print(dem.plt)

> writeGDAL(dem, "dem6.sdat", "SAGA")

DEM

10

20

30

40

50

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Controlling SAGA from R

> library(RSAGA)

> rsaga.env()

$workspace

[1] "."

$cmd

[1] "saga_cmd.exe"

$path

[1] "C:/PROGRA~1/R/R-210~1.1/library/RSAGA/saga_vc"

$modules

[1] "C:/PROGRA~1/R/R-210~1.1/library/RSAGA/saga_vc/modules"

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Getting list of modules

> rsaga.get.modules("ta_channels")

$ta_channels

code name interactive

1 0 Channel Network FALSE

2 1 Watershed Basins FALSE

3 2 Watershed Basins (extended) FALSE

4 3 Vertical Distance to Channel Network FALSE

5 4 Overland Flow Distance to Channel Network FALSE

6 5 D8 Flow Analysis FALSE

7 6 Strahler Order FALSE

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Run stream extraction

> rsaga.geoprocessor(lib = "ta_channels", module = 5, param = list(DEM = "dem6.sgrd",

+ DIRECTION = "channels.sgrd", CONNECTION = "route.sgrd",

+ NETWORK = "channels.shp"))

SAGA CMD 2.0.4

library path: C:/PROGRA~1/R/R-210~1.1/library/RSAGA/...

library name: ta_channels

module name : D8 Flow Analysis

author : (c) 2003 by O.Conrad

Load grid: dem6.sgrd...

ready

Parameters

Grid system: 100; 6x 6y; 100x 100y

DEM: dem6

Flow Direction: Flow Direction

Flow Connectivity: Flow Connectivity

Flow Network: Flow Network

Minimum Connectivity: 0

...

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Read back to R

> dem$route <- readGDAL("route.sdat")$band1

route.sdat has GDAL driver SAGA

and has 6 rows and 6 columns

> channels <- readOGR("channels.shp", "channels")

OGR data source with driver: ESRI Shapefile

Source: "channels.shp", layer: "channels"

with 32 features and 2 fields

Feature type: wkbLineString with 2 dimensions

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Plot the final result

> dem.plt <- spplot(dem[1], main = "DEM", col.regions = topo.colors(25))

> channels.plt <- spplot(dem[2], col.regions = rev(gray(0:20/20)),

+ main = "Flow connectivity", sp.layout = list("sp.lines",

+ channels, col = "red"))

> print(dem.plt, split = c(1, 1, 2, 1), more = T)

> print(channels.plt, split = c(2, 1, 2, 1), more = F)

DEM

10

20

30

40

50

Flow connectivity

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

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Preparing FWTools

There is still no package to control FWTools from R, but we cansimply send command lines using the system command. Beforewe can use FWTools from R, we need to locate it on our PC:

> gdalwarp <- gsub("/", "\\\\", dir(path="C:/PROGRA~2/FWTOOL~1.7",

+ pattern="gdalwarp.exe", recursive=TRUE, full.names=TRUE))

> gdalwarp

[1] "C:\\PROGRA~2\\FWTOOL~1.7\\bin\\gdalwarp.exe"

> workd <- paste(gsub("/", "\\\\", getwd()), "\\", sep="")

http://fwtools.maptools.org

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MODIS data

Now we can download some GIS data from web:

> MOD12Q1 <- "ftp://anonymous:[email protected]/

+ MOLT/MOD12Q1.004/2004.01.01/"

> download.file(paste(MOD12Q1,

+ "MOD12Q1.A2004001.h18v03.004.2006117173748.hdf", sep=""),

+ destfile=paste(getwd(),

+ "MOD12Q1.A2004001.h18v03.004.2006117173748.hdf", sep="/"),

+ mode='wb', method='wget')

Resolving e4ftl01u.ecs.nasa.gov... 152.61.4.83

Connecting to e4ftl01u.ecs.nasa.gov|152.61.4.83|:21... connected.

Logging in as anonymous ... Logged in!

==> SYST ... done. ==> PWD ... done.

==> TYPE I ... done. ==> CWD /MOLT/MOD12Q1.004/2004.01.01 ... done.

==> SIZE MOD12Q1.A2004001.h18v03.004.2006117173748.hdf ... 23165983

==> PASV ... done. ==> RETR MOD12Q1.A2004... done.

Length: 23165983 (22M)

0K .......... .......... 0% 64.9K 5m48s

...

22550K .......... .......... 99% 501K 0s

22600K .......... 100% 503K=65s

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Reprojecting grids

We can reproject/resample the map to our local coordinatesystem using the gdalwarp functionality (this combines severalprocessing steps in one function):

> NL.prj <- "+proj=sterea +lat_0=52.15616055555555

+ +lon_0=5.38763888888889 +k=0.999908 +x_0=155000

+ +y_0=463000 +ellps=bessel +units=m +no_defs

+ +towgs84=565.237,50.0087,465.658,

+ -0.406857,0.350733,-1.87035,4.0812"

> system(paste(gdalwarp, " HDF4_EOS:EOS_GRID:\"", workd,

+ "\\MOD12Q1.A2004001.h18v03.004.2006117173748.hdf\"

+ :MOD12Q1:Land_Cover_Type_1 -t_srs \"", NL.prj, "\"

+ IGBP2004NL.tif -r near -te 0 300000 280000 625000

+ -tr 500 500", sep=""))

Creating output file that is 560P x 650L.

Processing input file HDF4_EOS:EOS_GRID:\\MOD12Q1.A2004001...

Using internal nodata values (eg. 255) for image HDF4_EOS:EOS_...

0...10...20...30...40...50...60...70...80...90...100 - done.

http://www.gdal.org/gdalwarp.html

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Plot the final result

In this case we have produced a MODIS-based land cover mapfor the whole Netherlands in resolution of 500 m (in localcoordinate system).

https://lpdaac.usgs.gov/lpdaac/products/modis_products_table/land_cover/yearly_l3_global_0_05deg_cmg/mod12c1

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Writing spatial data to KML

There are two possibilities to export maps to KML: (a)using existing packages, and (b) by writing KML files“by-hand”.

To export point or line features to KML, use the writeOGRmethod that is available in R package rgdal.

More flexible way to writing KML files is by using loops.

http://code.google.com/apis/kml/documentation/kmlreference.html

http://spatial-analyst.net/wiki/index.php?title=Export_maps_to_GE

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Literature

Bivand, R., Pebesma, E., Rubio, V., 2008. Applied SpatialData Analysis with R. Use R Series, Springer, Heidelberg,378 p.

Hengl, T., 2009. A Practical Guide to GeostatisticalMapping, 2nd edition. University of Amsterdam, 291 p.ISBN 978-90-9024981-0.

Kabacoff, R.I., 2009. Data Analysis and Graphics with R.Manning publications, 375 p.

Zuur, A. F., Ieno, E. N., Meesters, E. H. W. G., 2009. ABeginner’s Guide to R. Springer, Use R series, 228 p.

http://asdar-book.org

http://asdar-book.org



http://www.manning.com/kabacoff/

http://www.springer.com/statistics/computational/book/978-0-387-93836-3

http://www.springer.com/statistics/computational/book/978-0-387-93836-3