GuangchuangYu

d <- data.frame(pkg = c("GOSim", "GOSemSim", "DOSE", "meshes",
                        "DOSim", "HPOSim", "ppiPre", "MeSHSim", "meshr"),
                year = c(2007, 2010, 2015, 2018, 2011, 2015, 2013, 2015, 2015),
                journal = c("BMC Bioinformatics", rep("Bioinformatics", 3),
                            "BMC Bioinformatics", "PLoS One", "BMC Systems Biology",
                            "Journal of Bioinformatics and computational biology",
                            "BMC Bioinformatics"),
                y = c(1, 1, 2, 3, 2, 4, 5, 2.8, 3.2))

require(ggplot2)

GuangchuangYu

mirror	speed
http://cran.stat.upd.edu.ph/	5.212
http://healthstat.snu.ac.kr/CRAN/	5.322
http://cran.ism.ac.jp/	5.327
http://ftp.yzu.edu.tw/CRAN/	5.340
http://ftp.iitm.ac.in/cran/	5.380
https://mirrors.eliteu.cn/CRAN/	5.398
http://cran.stat.nus.edu.sg/	5.445

GuangchuangYu

Sample	Date	DateNr	dDay1	dDay2	dDay3	Station	Area	31UE_ED50	31UN_ED50	Year	Month	Season	Salinity	Temperature	CHLFa
DANT.19900110	19900110	1990/10/1	7	9	9	DANT	WZ	681379.62	5920571.24	1990	1	winter	29.19	4	1.3
DANT.19900206	19900206	1990/6/2	                 34	    36	36	   DANT	WZ	681379.62	5920571.24	1990	2	winter	27.37	6	NA
DANT.19900308	19900308	1990/8/3	                 64	66	66	DANT	WZ	681379.62	5920571.24	1990	3	spring	24.99	7.3	21.1
DANT.19900404	19900404	1990/4/4	                 91	93	93	DANT	WZ	681379.62	5920571.24	1990	4	spring	28.79	8.2	25
DANT.19900509	19900509	1990/9/5	               126	128	128	DANT	WZ	681379.62	5920571.24	1990	5	spring	33.28	17.4	10.2
DANT.19900620	19900620	6/20/1990	168	170	170	DANT	WZ	681379.62	5920571.24	1990	6	summer	32.69	18.1	6.2
DANT.19900718	19900718	7/18/1990	196	198	198	DANT	WZ	681379.62	5920571.24	1990	7	summer	31.9	17	7.9
DANT.19900815	19900815	8/15/1990	224	226	226	DANT	WZ	681379.62	5920571.24	1990	8	summer	33.76	21	7.85
DANT.19900919	19900919	9/19/1990	259	261	26

GuangchuangYu

Sample	Date	DateNr	dDay1	dDay2	dDay3	Station	Area	31UE_ED50	31UN_ED50	Year	Month	Season	Salinity	Temperature	CHLFa
DANT.19900110	19900110	1990/10/1	7	9	9	DANT	WZ	681379.62	5920571.24	1990	1	winter	29.19	4	1.3
DANT.19900206	19900206	1990/6/2	                 34	    36	36	   DANT	WZ	681379.62	5920571.24	1990	2	winter	27.37	6	NA
DANT.19900308	19900308	1990/8/3	                 64	66	66	DANT	WZ	681379.62	5920571.24	1990	3	spring	24.99	7.3	21.1
DANT.19900404	19900404	1990/4/4	                 91	93	93	DANT	WZ	681379.62	5920571.24	1990	4	spring	28.79	8.2	25
DANT.19900509	19900509	1990/9/5	               126	128	128	DANT	WZ	681379.62	5920571.24	1990	5	spring	33.28	17.4	10.2
DANT.19900620	19900620	6/20/1990	168	170	170	DANT	WZ	681379.62	5920571.24	1990	6	summer	32.69	18.1	6.2
DANT.19900718	19900718	7/18/1990	196	198	198	DANT	WZ	681379.62	5920571.24	1990	7	summer	31.9	17	7.9
DANT.19900815	19900815	8/15/1990	224	226	226	DANT	WZ	681379.62	5920571.24	1990	8	summer	33.76	21	7.85
DANT.19900919	19900919	9/19/1990	259	261	26

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

d = structure(list(x = c(516.362976074219, 514.883316040039, 510.306549072266,
502.721382141113, 492.303680419922, 479.293769836426, 463.977825164795,
446.675720214844, 427.74100112915, 407.568737030029, 386.590072631836,
365.25927734375, 344.028457641602, 323.352523803711, 303.678215026855,
285.42236328125, 268.959743499756, 254.630310058594, 242.738302230835,
233.550735473633, 227.284624099731, 224.07541847229, 223.949848175049,
226.633100509644, 232.021848678589, 240.107601165771, 248.467231750488,
256.632625579834, 264.798046112061, 272.827682495117, 279.170124053955,
284.358814239502, 288.348812103271, 291.169918060303, 292.902908325195,
293.650428771973, 293.608024597168, 292.990119934082, 291.361457824707,

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

GuangchuangYu

qbar <- function(x, verbose=TRUE) {
  enc = encoding(quality(x))

  qual <- sapply(seq_along(x), function(i) enc[to_char_vector(x[i])])
  mq <- rowMeans(qual)
  if (verbose) {
    names(mq) <- NULL
    print(mq)
  }

GuangchuangYu

## devtools::install_github("stephenturner/msigdf")
library(msigdf)
library(dplyr)
library(clusterProfiler)

c2 <- msigdf.human %>% 
    filter(collection == "c2") %>% select(geneset, entrez) %>% as.data.frame

data(geneList)
de <- names(geneList)[1:100]

GuangchuangYu

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))