GuangchuangYu

p <- ggplot(mtcars, aes(factor(cyl),mpg,
                        fill=factor(cyl),
                        colour=factor(cyl)))
 
p1 <- p+geom_violin(alpha=0.3, width=0.5) +
    geom_boxplot(width=0.2, outlier.colour=NA)
 
p2 <- p+geom_violin(alpha=0.3, width=0.5) +
    geom_dotplot(binaxis='y', stackdir='center', dotsize=0.5)
 

GuangchuangYu

fibonacci <- function(n) {
    u <- (1+sqrt(5))/2
    (u^n - (1-u)^n) / sqrt(5)
}

GuangchuangYu

require(ggplot2)

t <- seq(0,2*pi, by=0.1)
x <- 16*sin(t)^3
y <- 13*cos(t) - 5*cos(2*t) - 2*cos(3*t) - cos(4*t)

d <- data.frame(x=x,y=y, f=0)

a <- x
b <- y

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f <- function(x) 1/(x^2-1)
x <- seq(-3,3, by=0.001)
y <- f(x)
d <- data.frame(x=x,y=y)

p <- ggplot()
p <- p+geom_rect(fill = "white",color="black",size=3,
                 aes(NULL, NULL,xmin=-3, xmax=3,
                     ymin=-3,ymax=3, alpha=0.1))

GuangchuangYu

## Antithetic sampling reframes our estimate as a sum of negatively
## correlated random variables, using the fact that negative
## correlation reduces the variance of a sum.

## http://en.wikipedia.org/wiki/Antithetic_variates

g <- function(x) 1/(1+x)
N <- 1500
n <- 50
u1 <- matrix(runif(2 * n * N), ncol = n)

GuangchuangYu

## http://ygc.name/2011/08/02/q-q-plots/

qqplot <- function(y, distribution=qnorm) {
  x <- distribution(ppoints(y))
  plot(x, sort(y),
       xlab="Theoretical Quantiles",
       ylab="Sample Quantiles",
       main="Normal Q-Q Plot"
       )
  lines(y,y)

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set.seed(123)

x <- rnorm(100)
y <- rnorm(100)
points.df <- data.frame(x=x,y=y)

center <- c(0.2, 0.3)
diameter <- 2

d <- apply(points.df, 1, function(x) x-center)

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# http://ygc.name/2012/04/29/bootstrap-for-enrichment-analysis/

goboot <- cmpfun(function(goid,
                          sampleSize,
                          nboot=1000) {
    ratio <- rep(0, nboot)

    allgene <- unique(mappedkeys(org.Hs.egGO))
    allgeneInCategory <- unique(get(goid, org.Hs.egGO2ALLEGS))

GuangchuangYu

x <- read.csv("/Volumes/YGC/Researches/ARCHIVE/Others/AL-1-by-GR.Yan/2D-data-and-analysis-result/2D.csv")
eg <- as.character(x[,1])
require(clusterProfiler)
xx <- enrichGO(eg, ont="BP")
gg <- xx@geneInCategory
names(gg) <- clusterProfiler:::TERM2NAME.BP(names(gg))

g <- gg[1:4]
require(gplots)
vm <- venn(g)

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## http://ygc.name/2012/05/29/t-test/


p1 <- rnorm(1000)
p2 <- rnorm(1000, sd=3)

hist(p2, col="green", xlab="", main="")
hist(p1, col="red", add=T)

legend(legend=c("sd=1", "sd=3"), fill=c("green", "red"), x="topright")