html-document.Rmd

---

title: "HTML test"
# themes:
#   “cerulean”, “journal”, “flatly”, “readable”, “spacelab”, “united”,
#   “cosmo”, “lumen”, “paper”, “sandstone”, “simplex”, “yeti”
# code highlighting:
#   “default”, “tango”, “pygments”, “kate”, “monochrome”, “espresso”,
#   “zenburn”, “haddock”, “textmate”
output:
  html_document:
    toc: true
    toc_depth: 2
    self_contained: false
    code_folding: show
    theme: lumen
    highlight: textmate
    css: html-document.css

---

library(ggplot2)
theme_set(theme_bw(base_size=12))
theme_update(panel.background = element_rect(fill = "transparent", colour = NA),
             plot.background = element_rect(fill = "transparent", colour = NA))
knitr::opts_chunk$set(dev.args=list(bg="transparent"))

Inline R

Inline formula $\tfrac{7}{2^3 + 1} = `r 7 / (2^3 + 1)`$ code and a random r rnorm(10) vector.

Random points in R

This plots 2000 random points …

xx <- rnorm(2000)
yy <- rnorm(2000)
plot(xx, yy, pch=20, color = rgb(0,0,0,alpha=0.3))

Enhanced Scatterplot

library(car)
attach(mtcars)
scatterplotMatrix(~mpg+disp+drat+wt|cyl,
  data=mtcars,
  main="Three Cylinder Options"
)

ggplot2 demo

This is straight from a ggplot2 introduction.

# Setup ----------------------------------------------
options(scipen=999)
library(ggplot2)
data("midwest", package = "ggplot2")
theme_set(theme_bw())
# midwest <- read.csv("http://goo.gl/G1K41K")  # bkup data source

# Add plot components --------------------------------
gg <- ggplot(midwest, aes(x=area, y=poptotal)) +
  geom_point(aes(col=state, size=popdensity), alpha=0.5) +
  geom_smooth(method="loess", se=F) + xlim(c(0, 0.1)) + ylim(c(0, 500000)) +
  labs(title="Area Vs Population",
    y="Population", x="Area", caption="Source: midwest")

# Call plot ------------------------------------------
plot(gg)