timcdlucas

library(dplyr)
library(glmnet)
library(splines)
library(tidyr)
library(ggplot2)
library(magrittr)

d <- data_frame(cov1 = rnorm(100),
                cov2 = rnorm(100),

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library(dplyr)
library(ggplot2)

d <- data.frame(x = rep(c('a', 'b'), 50), y = rbinom(100, 1, 0.4))

summary <- d %>% 
             group_by(x) %>% 
             summarise(prop = mean(y), CI = 0.95 * sqrt((sum(y)/50 * (50 - sum(y))/50) / 50))

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df1 <- data.frame(y = rnorm(100), x = letters[1:2])
df2 <- data.frame(aggregate(. ~ x, df1, mean),
                  upperCI = aggregate(. ~ x, df1, function(x) quantile(x, 0.025))[, 2],
                  lowerCI = aggregate(. ~ x, df1, function(x) quantile(x, 0.975))[, 2])

ggplot() +
  geom_violin(data = df1, aes(x = x, y = y)) +
  geom_errorbar(data = df2, aes(x = x, ymax = upperCI, ymin = lowerCI), width = 0.5) +
  geom_point(data = df2, aes(x, y))

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d <- data.frame(year = rep(2012:2016, each = 30),
                y = rnorm(5 * 30))


ggplot(d, aes(y = y)) + 
  geom_boxplot(aes(x = factor(year))) +
  geom_smooth(aes(x = as.numeric(factor(year))))

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library(ggplot2)
library(igraph)


set.seed(1)

# Make some data
x <- data.frame(y = c(rnorm(200), rnorm(200, 3), rnorm(200, 6)), 
                x = rep(letters[1:20], each = 30))

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# Code by Dave Redding


library(raster)
zzz=100
colsX<-raster(nrow=zzz,ncol=zzz)
values(colsX)<-1:ncell(colsX)
extent(colsX)<-c(-100,100,-100,100)

cols<-c("#00FF00","#FFFFFF")

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x <- c(NA, 5:1)
y <- c(5:1, NA)

psum <- function(x, y){
  x[is.na(x)] <- 0
  y[is.na(y)] <- 0

  x + y
}

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# Edit: whoops, radians

library(raster)

direction <- raster(nrows=100, ncols=100, xmn=0, xmx=10, , ymn=0, ymx=10)
values(direction) <- seq(0, 2 * pi, length.out = 100^2) + rnorm(100^2)

velocity <- raster(nrows=100, ncols=100, xmn=0, xmx=10, , ymn=0, ymx=10)
values(velocity) <- runif(100^2, 0, 0.1)

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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'

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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))))