# Blog Archives

## plotting raster data in R: adjusting the labels and colors of a classified raster

August 2, 2012
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Thank’s to Andrej who wrote this comment: “Is it possible to to color the resulting 12 clusters within your original image to get a feel for visual separation?” You can do so: But how to get values at a location? You will need these values to determine whether the defined class is representing a water

## unsupervised classification of a Landsat image in R: the whole story or part two

August 1, 2012
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The main question when using remote sensed raster data, as we do, is the question of NaN-treatment. Many R functions are able to use an option like rm.NaN=TRUE to treat these missing values. In our case the kmeans function in R is not capable to use such a parameter. After reading the tif-files and creating

## unsupervised classification of a raster in R: the layer-stack or part one.

July 29, 2012
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In my last post I was explaining the usage of QGis to do a layerstack of a Landsat-scene. Due to the fact that further research and trying out resulted in frustration I decided to stick with a software I know well: R. So download the needed layers here and open up your flavoured version of

## introduction to R: learning by doing (part 2: plots)

July 10, 2012
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Lets go one with the second part of learning R by doing R (you will find the first part here. As we have used vectors, matrices and loops in the first part, we will concentrate on graphics in this one. but first we will need data to plot: Sometimes you will need several plots in

## introduction to R: learning by doing (part 1)

July 9, 2012
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Geography is often about statistics as it is the basis for fast exchange of information: providing a mean and standard deviation to the audience is often much easier then showing raw data: Learning a script language for this purpose can be a hard-ass work. But I think it is more often a need of practice.

## MatLab, SAS, STATA, SPSS, Excel users: Try R, damn it!

July 2, 2012
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Due to my work with a multitude of statistical packages in my career I may be able to evaluate a lot of them. I’ve first used Excel for my calculations as most of the normal users do. I like the idea behind a spreadsheet and the combination of data and click-to-do functions. Nevertheless I’ve often

## reproducible documents/analytics in R: the knitr package

June 26, 2012
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When I am working in new institutions and I am asking: “Do you have a document management system?” I often get the answer:”Yap, we are using folders” … OKAY. Making analysis, developing applications and keeping an eye on code, data and applications make this even harder as it has to be. Of course not many

## reading shape files in R

June 24, 2012
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If I would like to adjust a shape file I normally used the way over an excel file or a text file to get a table and to join this with an existing shape file. Due to the sp and rgdal packages in R you can manipulate shapefiles directly in R: now lets map the

## setting your working directory permanently in R

June 24, 2012
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Most of us R users are using a special working directory for the daily work in R. But I was bothered in typing everytime in my command line prior using R. Also using this line at the first position in scripts was not pleasent enough. So how to get around this? There is a special

## useR! 2012 Conference

April 25, 2012
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“Spatial data is, quite literally, everwhere” (Barry Rowlingson) this is so true! And because of that you guys will have the chance to take part in a great tutroial on using R for managing geospatial data, transforming, making maps and working with OGC standards. So visit this years useR! conference at Vanderbilt University; Nashville, Tennessee,