bjurban

library(lattice)
library(MASS)
splom(iris, upper.panel = function(x, y, subscripts, ...) {
  if(is.numeric(x) & is.numeric(y)){

    # calculate bivariate kernel density 
    v <- current.panel.limits() # allows full bleed by setting limits explicitly
    f1 <- kde2d(x = x, y = y, n = 50, lims = c(v$xlim, v$ylim))

    f <- data.frame(x = f1$x, y = rep(f1$y, each = length(f1$x)), 

bjurban

# weather-underground.R
# Bryan Urban
# 2015-06-19

# this gets local weather data for a list of weather station codes from
# www.weatherunderground.com 
# 
# note: for large timespans or multiple cities, data scraping can be slow

## SETUP -----

bjurban

# alternating axis tick marks
# Bryan Urban
# 2015-06-01

xscale.components.alt <- function(lim, by=5, ...){
  ans <- xscale.components.default(lim, ...)
  
  tck.at  <- ans$bottom$ticks$at 
  tck.lab <- as.character(tck.at)
    

bjurban

# Legends and Groups in Lattice
# Bryan Urban
# 2015-06-01

library(lattice)
head(mtcars)

my.col <- c("black", "blue", "red")
my.settings <- 
  list(superpose.line    = list(col=my.col),

bjurban

# Make a levelplot with a custum linear scale
# Author: Bryan Urban
# Email: burban at fraunhofer dot org
# Date: 2015-04-30 

library(lattice)
library(RColorBrewer)

# make some data
foo <- foo <- expand.grid(x = 1:100, y=1:10)

bjurban

# Combine multiple raw HOBO data files into a single .csv file 
# Author: Bryan Urban
# Email: burban@fraunhofer.org
# Date: 2015-04-30

## SETUP --------------------

# install these two packages first if you don't have them:
# install.packages("data.table")
# install.packages("lubridate")

bjurban

## power analysis in R from
## http://www.statmethods.net/stats/power.html

library(pwr)

# http://meera.snre.umich.edu/plan-an-evaluation/related-topics/power-analysis-statistical-significance-effect-size
## Effect Size 
# ~ difference in means (control vs. treatment) divided by
#   the standard deviation (e.g., of the control group)
#      <0.1 = trivial effect

bjurban

# adding a custom legend to levelplot when z values are categorical (not continuous)
library(lattice)
foo <- expand.grid(x = 1:10, y=1:10)
foo$z <- as.integer(runif(100)*3)
# foo$z takes on values of 0, 1 or 2: this could represent a factor

# default levelplot
levelplot(z~x*y, foo)

# levelplot with custom colors and custom legend