| # Author: Joona Lehtomäki <[email protected]> | |
| # Updated: 13.11.2011 | |
| # Version: 0.0.1 | |
| if (!require("rgdal")) { | |
| install.packages("rgdal") | |
| } | |
| if (!require("raster")) { | |
| install.packages("raster") |
Notes:
I've tried to break up in to separate pieces, but it's not always possible: e.g. knowledge of data structures and subsetting are tidy intertwined.
Level of Bloom's taxonomy listed in square brackets, e.g. http://bit.ly/15gqPEx. Few categories currently assess components higher in the taxonomy.
| #Title: An example of the correlation of x and y for various distributions of (x,y) pairs | |
| #Tags: Mathematics; Statistics; Correlation | |
| #Author: Denis Boigelot | |
| #Packets needed : mvtnorm (rmvnorm), #RSVGTipsDevice (devSVGTips) | |
| #How to use: output() | |
| # | |
| #This is an translated version in R of an Matematica 6 code by Imagecreator. | |
| # from http://en.wikipedia.org/wiki/File:Correlation_examples2.svg | |
| library(mvtnorm) |
| Which documents belong to each topic? | |
| Documents don't belong to a single topic, there is a distribution of topics | |
| over each document. | |
| But we can Find the topic with the highest proportion for each document. | |
| That top-ranking topic might be called the 'topic' for the document, but note | |
| that all docs have all topics to varying proportions | |
| Assume that we start with `topic_docs` from the output of the mallet package |
| library('devtools') | |
| library('utils') | |
| library('httr') | |
| update_github <- | |
| function(ask = TRUE, ...){ | |
| installed <- installed.packages() | |
| oldVersion <- installed[,'Version'] | |
| urls <- sapply(names(oldVersion), function(x){ | |
| d <- packageDescription(x) |
| --- | |
| title: "A few methods for making tables in rmarkdown" | |
| output: html_document | |
| --- | |
| Updates: | |
| Packages that have appeared since my original look into this, and seem great: | |
| https://github.com/yihui/printr |
| # Function to rarefy point data in any number of dimensions. The goal here is to | |
| # take a large data set and reduce it in size in such a way as to approximately maximize the | |
| # difference between points. For instance, if you have 2000 points but suspect a lot of | |
| # spatial autocorrelation between them, you can pass in your data frame, the names (or indices) | |
| # of the lat/lon columns, and the number 200, and you get back 200 points from your original data | |
| # set that are chosen to be as different from each other as possible given a randomly chosen | |
| # starting point | |
| # Input is: | |
| # |
| diverge0 <- function(p, ramp) { | |
| # p: a trellis object resulting from rasterVis::levelplot | |
| # ramp: the name of an RColorBrewer palette (as character), a character | |
| # vector of colour names to interpolate, or a colorRampPalette. | |
| require(RColorBrewer) | |
| require(rasterVis) | |
| if(length(ramp)==1 && is.character(ramp) && ramp %in% | |
| row.names(brewer.pal.info)) { | |
| ramp <- suppressWarnings(colorRampPalette(brewer.pal(11, ramp))) | |
| } else if(length(ramp) > 1 && is.character(ramp) && all(ramp %in% colors())) { |
| belief | y2015 | y2014 | |
|---|---|---|---|
| Improves the security posture of my organization | 0.75 | 0.71 | |
| Improves the security posture of the nations critical infrastructure | 0.63 | 0.64 | |
| Reduces the cost of detecting and preventing cyber attacks | 0.22 | 0.21 | |
| Improves situational awareness | 0.60 | 0.54 | |
| Fosters collaboration among peers and industry groups | 0.48 | 0.51 | |
| Enhances the timeliness of threat data | 0.11 | 0.16 | |
| Makes threat data more actionable | 0.21 | 0.24 |