Data analysis in the modern-day computing industry is of great essence as the world tries to understand the data that has been accumulated in many systems across the globe. Extraction of useful information is a task being focused so much in most organizations as this is marking the lifetime for existence in the business world.
Coursera, hail to Coursera. Despite the uprising criticism on MOOCs and their footprint in the educational landscape at universities Coursera created an interesting R learning course. It is divided and scheduled for 4 weeks and has video-tutorials as well as written material. The guys over at RevolutionAnalytics packed it all together: Content: Setting working directory and getting help How to get help Data Types Subsetting Vectorized Operations Reading/Writing Data Control Structures in R Writing Functions Avoiding loops using xapply Plotting Regular expressions Regular expressions in R Classes and methods in R It is a free course and is very userfriendly. The…
SEXTANTE by Victor Olaya is a powerful plugin that bundles many methods and applications from QGIS in one place and provides a GUI for your processing work flow which is comparable with the ArcGIS ModelBuilder or the ERDAS Spatial Modeler. With this plugin it is very easy to use your GRASS, SAGA and GDAL tools, self-written R scripts and many more. This makes spatial analysis much easier and increases reproducibility. Especially the combination with R functions provides a completely new dimension of working with a GIS as nearly everything spatially can be converted to a data.frame and be consumed by…
As i was preparing myself for getting funding for the trip to the R user conference this year in Albacete, Spain I was coming across a highlightning talk by Josh Paulson about an interactive way of using the power of R without real struggling with R as a programming language: Shiny is a cool webapp which lets the user control the application via some drop-down menus and buttons and R computes the result in the background and displays them as well on the webpage:
Due to an upcoming presentation about “what is R” and “what can I do with R” in my company I was playing around with GUIs as they are a very important way to interact with users and R to present a simple calculator. This will lead hopefully to an understanding of syntax and concepts in R:
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:
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 of a layer stack we will go on with a work-around to solve the missing values problem of the non-covered areas of a Landsat picture.
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 R (in my case RStudio).
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:
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. And by practice I mean typing, reading and trying out.