gu-mi

# http://trinkerrstuff.wordpress.com/2014/05/12/handling-s3methods-death-in-roxygen-4-0-0/

pth <- "C:/Users/trinker/qdap/R"

fls <- file.path(pth, dir(pth))

FUN <- function(x) {
    cont <- readLines(x)
    cont[grepl("#' @S3", cont)] <- "#' @export"
    cont[length(cont) + 1] <- ""

gu-mi

# check if you need to install these packages
pkg <- c("RODBC", "ggplot2")
inst <- pkg %in% installed.packages()
# inst
if (length(pkg[!inst]) > 0) {
  install.packages(pkg[!inst])
} 
# load all required packages into session
lapply(pkg, library, character.only = TRUE)

gu-mi

# solarized light color scheme for my TexShop setup

defaults write TeXShop background_R 0.99
defaults write TeXShop background_G 0.96
defaults write TeXShop background_B 0.89

defaults write TeXShop commandred 0.0
defaults write TeXShop commandgreen 0.0
defaults write TeXShop commandblue 1.0

gu-mi

# Note: using the devel versions of both packages!
library(DESeq) # version 1.9.11
library(edgeR) # version 2.99.8
library(VennDiagram)

# Read in data ------------------------------------------------------------

## Use pasilla data
datafile = system.file( "extdata/pasilla_gene_counts.tsv", package="pasilla" )
datafile

gu-mi

## I came across a Chinese science blog which discusses an interesting stuff that girls should reject 
## the first 37% (for 1/e = 36.79%) pursuers in her life. A little R simulation as outlined in the 
## gist is shown below just for fun. In the snippet, m is the total number of boys you can choose. 
## In other words, they are potential husband for you. Larger number means high "quality" but these 
## numbers are randomly sampled, i.e., you don’t know whether the one who just walks into your life 
## is your Mr. Right or not. You reject the first m/e people (probably without any reason...), where 
## e is the Euler’s number, and choose from the rest of the m people. If you find any one of the 
## remaning boys better than the best boy you saw earlier in the first m/e ones, then you choose that 
## guy; otherwise, you have to make do with the last guy for the rest of your life...

gu-mi

# A simple approach to visually-weighted regression plots

doInstall <- TRUE  # Change to FALSE if you don't want packages installed.
toInstall <- c("ggplot2", "reshape2", "MASS")
if(doInstall){install.packages(toInstall, repos = "http://cran.us.r-project.org")}
lapply(toInstall, library, character.only = TRUE)

# Generate some data:
nn <- 1000
myData <- data.frame(X = rnorm(nn),

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# Simple ggplot2 heatmap
# with colorBrewer "spectral" palette

doInstall <- TRUE  # Change to FALSE if you don't want packages installed.
toInstall <- c("ggplot2", "reshape2", "RColorBrewer")
if(doInstall){install.packages(toInstall, repos = "http://cran.us.r-project.org")}
lapply(toInstall, library, character.only = TRUE)

# Generate a random matrix
# This can be any type of numeric matrix,

gu-mi

# Randomly allocating observations into groups, for, e.g. cross-validation
kk <- 10  # Number of partitions, as in "kk-fold cross-validation."

# Here is a data.frame full of good data:
nn <- 1003
myData <- data.frame(matrix(rnorm(nn * 3), ncol = 3))
colnames(myData) <- LETTERS[1:3]

# This does not work:
whichK <- sample(LETTERS[1:kk], nrow(myData), replace = T)

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#' Constructor
EmailClass <- function(name, email) {
	nc = list(
		name = name,
		email = email,
		get = function(x) nc[[x]],
		set = function(x, value) nc[[x]] <<- value,
		props = list(),
		history = list(),
		getHistory = function() return(nc$history),

gu-mi

# Simple ggplot2 heatmap, with optimal seriation

doInstall <- TRUE  # Change to FALSE if you don't want packages installed.
toInstall <- c("ggplot2", "reshape2", "RColorBrewer", "seriation")
if(doInstall){install.packages(toInstall, repos = "http://cran.us.r-project.org")}
lapply(toInstall, library, character.only = TRUE)

# Using U.S. Judge Rating Data
myData <- as.matrix(USJudgeRatings)