benmarwick

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

sckott

load alm

library(alm)

Define vector of DOIs, and search

Remember to get your api key, pass it in in the key parameter. Notice that we are passing the base url for the Crossref API, whereas the default is for the PLOS url http://alm.plos.org/api/v3/articles

benmarwick

#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)

AugustT

install.packages('zoon')

library(zoon)

vignette('Building_a_module')

#############
## Simples ##
#############

jhollist

---
title: "Untitled"
author: "Jeff W. Hollister"
date: "2/2/2016"
output: beamer_presentation
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)

jlehtoma

# Author: Joona Lehtomäki <joona.lehtomaki@gmail.com>
# Updated: 13.11.2011
# Version: 0.0.1

if (!require("rgdal")) {
	install.packages("rgdal")
}

if (!require("raster")) {
	install.packages("raster")

viciana

# require(devtools)
# install_github('SevillaR/Andaclima')

require(Andaclima)
rm(list = ls())

stations <- getAndalusia_ACS()

metainfo <- getMetaData(provincia       = stations$province.code,
                        estacion        = stations$station.code ,

goldingn

# install the experimental parallel branch
# remotes::install_github("zoonproject/zoon@parallel")
library (zoon)

# example workflow for 4 independent models that may take a while to run
run_wf <- function () {
  workflow(occurrence = UKAnophelesPlumbeus,
           covariate = UKBioclim,
           process = Replicate(Background(n = 1000), 4),
           model = GBM(max.trees = 10000),

benmarwick

---
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

hrbrmstr