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 |
View Antithetic_variates.R
## 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) |
View ss-pkg.R
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) |
View speed.md
View Temperature.txt
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 |
View Temperature.txt
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 |
View worldcup2018.R
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, |
View valetine.R
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 |
View qbar.R
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) | |
} |
View msigdf_clusterprofiler.R
## 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] |
View jiong.R
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)) |
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