thomasjensen

library(tikzDevice)
library(ggplot2)

y <- exp(seq(1,10,.1))
x <- 1:length(y)

data <- data.frame(x = x, y = y)

tikz(file = "test.tex")
plot <- ggplot(data, aes(x = x, y = y)) + geom_line()

thomasjensen

#read the ggplot2 library
library(ggplot2)

#read the data
mepDat <- read.csv("ep6.csv")

#plot the density for the questions variable
plot <- ggplot(mepDat, aes(x = Questions))
plot <- plot + geom_density()
plot <- plot + geom_segment(aes(x = median(mepDat$Questions), xend = median(mepDat$Questions), y = 0, yend = .031), linetype = 2)

thomasjensen

library(MASS)
library(ggplot2)

#generate the fake data
covvar <- matrix(c(1,.6,.6,.6,1,.6,.6,.6,1),nr = 3)
means <- c(20,25,30)
data <- as.data.frame(mvrnorm(100,means,covvar))
#dichotomize the dependent variable
data$V1 <- ifelse(data$V1 > 20,1,0)

thomasjensen

#generate data
library(MASS)
covvar <- matrix(c(1,.6,.6,.6,1,.6,.6,.6,1),nr = 3)
means <- c(20,25,30)

data <- as.data.frame(mvrnorm(100,means,covvar))

#dichotomize the dependent variable
data$V1 <- ifelse(data$V1 > 20,1,0)

thomasjensen

x <- c(1,2,3)
plot(x)