dsparks dsparks

# improved list of objects
# From http://stackoverflow.com/a/4827843/479554
 
.ls.objects <- function (pos = 1, pattern, order.by,
                        decreasing=FALSE, head=FALSE, n=5) {
    napply <- function(names, fn) sapply(names, function(x)
                                         fn(get(x, pos = pos)))
    names <- ls(pos = pos, pattern = pattern)
    obj.class <- napply(names, function(x) as.character(class(x))[1])
    obj.mode <- napply(names, mode)

N <- 1000
nDims <- 2
randomNumbers <- rnorm(nDims * N, 0, 1)
randomNumbers <- matrix(randomNumbers, ncol = nDims)
 
plot(randomNumbers)
 
radius = sqrt(rowSums(randomNumbers ^ 2))
randomSphere <- 1/radius * randomNumbers
plot(randomSphere)

doInstall <- TRUE
toInstall <- c("ggplot2")
if(doInstall){install.packages(toInstall, repos = "http://cran.us.r-project.org")}
lapply(toInstall, library, character.only = TRUE)
 
# Make some random data:
randPoints <- data.frame(x = runif(1000), y = runif(1000))
randPoints$color <- hsv(runif(1000), runif(1000), runif(1000))
 
zp1 <- ggplot(randPoints,

library(digest)
set.seed(1)
 
(x <- sample(1e9, size=6))
# [1] 265508664 372123900 572853364 908207790 201681932 898389685
 
## To hash R's internal representation of these numbers
strtoi(substr(sapply(x, digest), 28, 32), 16L) %% 1e3
# [1] 552 511 233 293 607 819

#' Finds the local maxima (peaks) in the given vector after smoothing the data
#' with a kernel density estimator.
#'
#' First, we smooth the data using kernel density estimation (KDE) with the
#' \code{\link{density}} function. Then, we find all the local maxima such that
#' the density is concave (downward).
#'
#' Effectively, we find the local maxima with a discrete analogue to a second
#' derivative applied to the KDE. For details, see this StackOverflow post:
#' \url{http://bit.ly/Zbl7LV}.

myFunction <- function(x){prod(1:x)}
myFunction(10)
system.time(lapply(1:10000, myFunction))
 
library(parallel)
myCluster <- makeCluster(detectCores())
clusterExport(myCluster, ls())
system.time(parSapply(myCluster, 1:10000, myFunction))
stopCluster(myCluster)  

doInstall <- TRUE
toInstall <- c("ggplot2", "poLCA", "reshape2")
if(doInstall){install.packages(toInstall, repos = "http://cran.us.r-project.org")}
lapply(toInstall, library, character.only = TRUE)
 
ANES <- read.csv("http://www.oberlin.edu/faculty/cdesante/assets/downloads/ANES.csv")
ANES <- ANES[ANES$year == 2008, -c(1, 11, 17)]  # Limit to just 2008 respondents,
head(ANES)  # remove some non-helpful variables
# Adjust so that 1 is the minimum value for each variable:
ANES <- data.frame(apply(ANES, 2, function(cc){  cc - min(cc, na.rm = T) + 1  }))

doInstall <- TRUE  # Change to FALSE if you don't want packages installed.
toInstall <- c("maptools", "rgdal", "ggplot2", "spatstat", "RColorBrewer")
if(doInstall){install.packages(toInstall, repos = "http://cran.r-project.org")}
lapply(toInstall, library, character.only = TRUE)
 
# Taking an online compressed shapefile, and opening it in R
# From http://stackoverflow.com/a/3053883
temp <- tempfile()  # 110th & 111th Congressional District Shapefiles
download.file("http://www.census.gov/geo/cob/bdy/cd/cd110shp/cd99_110_shp.zip",
              temp)  # See http://www.census.gov/geo/www/cob/cd110.html#shp

doInstall <- TRUE
toInstall <- c("ggplot2")
if(doInstall){install.packages(toInstall, repos = "http://cran.us.r-project.org")}
lapply(toInstall, library, character.only = TRUE)
 
ANES <- read.csv("http://www.oberlin.edu/faculty/cdesante/assets/downloads/ANES.csv")
ANES <- ANES[ANES$year == 2008, -c(1, 11, 17)]  # Limit to just 2008 respondents,
head(ANES)  # remove some non-helpful variables
 
# Fit several models with the same DV: