timcdlucas

# 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)

timcdlucas

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
}

timcdlucas

# 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")

timcdlucas

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))

timcdlucas

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))))

timcdlucas

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))

timcdlucas

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))

timcdlucas

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

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

timcdlucas

# Run a workflow, specifying one module of each type.
work1 <- workflow(occurrence = UKAnophelesPlumbeus,
                  covariate  = UKAir,
                  process    = OneHundredBackground,
                  model      = LogisticRegression,
                  output     = PrintMap)

# Current form
work1 <- ChangeWorkflow(work1, model = RandomForest)

timcdlucas

x <- 1:100
n <- sapply(x, function(x) rpois(1, x))

pos <- sapply(1:length(n), function(i) rbinom(1, n[i], plogis(x[i] - 50)))
p <- pos / n

d <- data.frame(p = p, x = x)
m <- glm(p ~ x, data = d, weights = n, family = binomial)
m