After being busy the last two weeks teaching and attending academic conferences, I finally found some time to do what I love, program data visualizations using R. After being interested in Shiny for a while, I finally decided to pull the trigger and build my first Shiny app! I wanted to make a proof of

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Yesterday, I was creating a knitr document based on a script, and was looking for a way to include content from an R help file. The script, which was a teaching document, had a help() command for when the author wanted to refer readers to R documentation. I wanted that text in my final document, though.

There’s no...

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library(raster)
library(rgdal)

alt gadm gadm_sub 
plot(alt)
plot(gadm_sub, add=T)

asp slo 
> extract(slo, gadm_sub, fun = mean, na.rm = T, small = T, df = T)
  ID     slope
1  1  9.959053
2  2  1.047443
3  3  7.456165
4  4  1.673786
5  5 11.946553

> extract(asp, gadm_sub, fun = mean, na.rm = T, small...
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I suppose most companies use the Microsoft Office suite of programs, and my office is no exception. It easy to import data from an API or a database into R, but importing data from an Excel workbook is a different story. There are a few R packages for reading Excel files, but I’ve had problems

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Here's a little r-script to convenientely download high quality digital elevation data, i.e. for the Alps, from HERE:

require(XML)

dir.create("D:/GIS_DataBase/DEM/")
setwd("D:/GIS_DataBase/DEM/")

doc urls names 
for (i in 1:length(urls)) download.file(urls, names) 

# unzip all files in dir and delete them afterwards
sapply(list.files(pattern = "*.zip"), unzip)
unlink(list.files(pattern = "*.zip"))

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I present you with my restructured project on options trading and scenario analysis. You are more than welcome to try it out. Firstly, I will give a small presentation that will reveal what you can do with it and whether you need to continue reading. T...

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Some days ago H. Wickham (Chief Scientist of the RStudio company) posted an article about the RStudio CRAN mirror with …

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Similar to the back propagation neural network, the general regression neural network (GRNN) is also a good tool for the function approximation in the modeling toolbox. Proposed by Specht in 1991, GRNN has advantages of instant training and easy tuning. A GRNN would be formed instantly with just a 1-pass training with the development data.

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Interval estimation of the population mean can be computed from the functions of the following R packages:stats - contains the t.testTeachingDemos - contains the z.testBSDA - contains the zsum.test and tsum.testThe t.test of the stats package is a stud...

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I have updated my package solaR. This package provides calculation methods of solar radiation and performance of photovoltaic systems from …

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