sckott

> # Simulate a dataset for the example
> modelflag <- 1
> sim.dat <- PGLMM.sim(stree(16, "balanced"), nsites = 30, modelflag = modelflag,
+                     second.env = TRUE, compscale = 1)

> sim.dat$Vphylo  # I think this is the variance-covariance matrix from the phylogeny
      t1   t2   t3   t4   t5   t6   t7   t8   t9  t10  t11  t12  t13  t14  t15  t16
t1  1.00 0.75 0.50 0.50 0.25 0.25 0.25 0.25 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
t2  0.75 1.00 0.50 0.50 0.25 0.25 0.25 0.25 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
t3  0.50 0.50 1.00 0.75 0.25 0.25 0.25 0.25 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

sckott

# Blog post about a new food web
> require(ggplot2); require(igraph); require(bipartite); require(NetIndices)

# read in edge list 
> mygraph <- read.graph("/Mac/R_stuff/Blog_etc/newfoodweb/edgelist_new.txt", 
+            format = "edgelist")

> str(mygraph)
List of 9
 $ : num 181

sckott

# Code from Liam Revell, and put up here by Scott Chamberlain

require(phytools); require(geiger) # load packages
ntaxa1<-20; ntaxa2<-2 # number of taxa in each clade
total.height<-10 # total tree height (may need to be adjusted)

# simulate subtree 1
tr1<-birthdeath.tree(b=1,d=0,taxa.stop=ntaxa1)
tr1$tip.label<-1:ntaxa1; tr1$root.edge<-0

sckott

#######################################################
# by Scott Chamberlain <[email protected]>
#   for blog post titled "Awesome use case: check my species names and 
#       gimme some distribution data"
#######################################################
# Load packages
install.packages(c("RCurl","stringr","XML","plyr","RJSONIO"))
require(RCurl);require(stringr);require(XML);require(plyr);require(RJSONIO) 

# Clone taxize and rgbif repositories from GitHub

sckott

require(plyr); require(stringr); require(ggplot2); require(lubridate); require(twitteR)

datout_1 <- searchTwitter("I work for the internet", n = 1500, since='2011-11-11', until='2011-12-12')
datout_2 <- searchTwitter("I work for the internet", n = 1500, since='2011-11-13', until='2011-12-14')
datoutdf <- ldply(c(datout_1, datout_2), function(x) x$toDataFrame(), .progress="text")

actual <- grep("I work for the internet", datoutdf[,1], ignore.case=T)
datoutdf2 <- datoutdf[actual,]

datoutdf2$newtime <- round_date(datoutdf2[,4], "hour")

sckott

piechart <- function(set) {
  ggplot(dat[dat$type==set,], aes(x = "", y = pageviews, fill = name)) + 
    theme_bw() +
    geom_bar(width = 1) + 
    coord_polar(theta = "y") +
    labs(y="", x='') +
    opts(panel.border = theme_rect(colour = 'white'),
         axis.text.x = theme_text(colour='white'))
}

sckott

# setwd("/Mac/R_stuff/Blog_etc/Mammal_Dataset")

# URLs for datasets
comm <- "http://esapubs.org/archive/ecol/E092/201/data/MCDB_communities.csv"
refs <- "http://esapubs.org/archive/ecol/E092/201/data/MCDB_references.csv"
sites <- "http://esapubs.org/archive/ecol/E092/201/data/MCDB_sites.csv"
spp <- "http://esapubs.org/archive/ecol/E092/201/data/MCDB_species.csv"
trap <- "http://esapubs.org/archive/ecol/E092/201/data/MCDB_trapping.csv"

# read them

sckott

library(reshape2)
library(ggplot2) 
dataset <- data.frame(var1 = rep(c("a","b","c","d","e","f"), each = 4), 
 var2 = rep(c("level1","level1","level2","level2"), 6), 
 var3 = rep(c("h","m"), 12), meas = rep(1:12))

Created by Pretty R at inside-R.org

# simply pivot table
dcast(dataset, var1 ~ var2 + var3)

sckott

# Define the function
loghistplot  <- function(data) {
  
  require(ggplot2); require(gridExtra) # load packages

  names(data) <- c('x','y') # rename columns

  # get min and max axis values
  min_x <- min(data$x)
  max_x <- max(data$x)

sckott

# calculate tree resolution stats
treeresstats <- function(x) {
  require(phangorn) # load the phangorn package
  todo <- ( 1+Ntip(x)) : (Ntip(x) + Nnode(x) )
  trsize_tips <- Ntip(x)
  trsize_nodes <- Nnode(x)
  polytomyvec <- sapply(todo, function(y) length(Children(x, y)))
  numpolys <- length(polytomyvec[polytomyvec > 2])
  numpolysbytrsize_tips <- numpolys/trsize_tips
  numpolysbytrsize_nodes <- numpolys/trsize_nodes