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scrapePub <- function(sp){
    
  Sys.sleep(2)
  spString <- tolower(gsub(' ', '+', sp))

  url <- paste0('http://www.ncbi.nlm.nih.gov/pubmed/?term=%22', spString, '%22')


  page <- html(url)

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   data(shorebird)
     shorebird <- comparative.data(shorebird.tree, shorebird.data, Species, vcv=TRUE, vcv.dim=3)
     mod1 <- pgls(log(Egg.Mass) ~ log(M.Mass) * log(F.Mass), shorebird) 
     print(mod1)
     
     mod1.sum <- summary(mod1)
     print(mod1.sum)
     mod1.aov <- anova(mod1)
     print(mod1.aov)

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library(ggplot2)

x <- data.frame(y = rnorm(100), g = rep(letters[1:4], 25))


ggplot(x, aes(y = y, x = g, fill = g)) + 
  geom_boxplot() + 
  ylim(c(-15, 15)) +
  scale_fill_manual(values = grey(c(0, 0.5, 0.9, 1))) +

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library(dplyr)


# Web scraping.
library(rvest)

# For synonym list
library(taxize)

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d <- data.frame(x = rnorm(100) + c(1, 2), y = c(0, 1))

m <- glm(y ~ x, data = d, family = binomial(link = cloglog))


newx <- seq(-5, 5, length.out = 100)
predict.line <- predict(m, newdata = data.frame(x = newx), type = 'response')

plot(y ~ x, d)

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# Print an R matrix in a form that can be copied straight into mathematica

MathematicaPaste.matrix <- function(m){

  charmat <- apply(m, 2, as.numeric)
  charmat[, -NCOL(charmat)] <- paste0(charmat[, -NCOL(charmat)], ',')

  charmat <- rbind('{', t(charmat), '},')
  charmat[dim(charmat)[1], dim(charmat)[2]] <- '}'

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#' Turn a string copied from Mathematica into an R array
#'
#'@param text Text representation of a mathematica array.
#'@param drop Logical. If TRUE, 1 and 2 dimensional arrays will be returned as vectors and matrices. 
#'  If FALSE, 1 and 2 dimensional arrays will be returned.
#'
#'@return An array
#'@export

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# Make up some data
data1 <- data.frame(length = rnorm(20, 8), weight = 1:20)

data2 <- data.frame(length = rnorm(30, 8), weight = 1:30)


# add a dummy variable and combine the data 

data1$dummy <- 'data1'

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# Simulate timeseries with period 10
time <- 1:200
y <- sin(time * 2 * pi / 10)


FT <- rep(NA, length(y) - 1)

for(k in 1:(length(y) - 1)){
  FT[k] <- sum(y * (cos(-2 * pi * k * 0:(length(y) - 1) / length(y)) + 1i * sin(-2 * pi * k * 0:(length(y) - 1) / length(y))))

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t <- read.nexus('data/Chapter3/fritz2009geographical.tre')

tr1 <- t[[1]]


mat <- data.frame(name = tr1$tip.label)

mat$col1 = 'a'
mat$col1[1:300] <- 'b'