Guangchuang Yu GuangchuangYu

## 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

## 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

## 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)

## speed.md

      
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              Last active May 23, 2019
            
              
                mirrorselect for R
              
          
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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


## 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,

## 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)
  }

## breast_curve.R
f <- function(a, b) function(y) a * y * log(y, base=10) -1/b * exp(-(b*y - b/exp(1))^4)


y <- seq(0, 1, length.out=100)
cols = colorspace::rainbow_hcl(5)
d1 <- data.frame(x = f(3, 30)(y),   y=y, color=cols[1])
d2 <- data.frame(x = f(2.8, 33)(y),   y=y, color=cols[2])
d3 <- data.frame(x = f(2.5, 36)(y), y=y, color=cols[3])
d4 <- data.frame(x = f(2.2, 40)(y), y=y, color=cols[4])
d5 <- data.frame(x = f(2, 50)(y),   y=y, color=cols[5])

## issues of CorMut.md

      
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      View issues of CorMut.md
  
    
    No update since 2014.
## Access date: 2017-01-04
$ svn log -q |grep 'z.li'
r87229 | z.li | 2014-03-08 12:13:28 +0800 (Sat, 08 Mar 2014)
r85532 | z.li | 2014-01-15 13:00:43 +0800 (Wed, 15 Jan 2014)
r85454 | z.li | 2014-01-12 13:57:16 +0800 (Sun, 12 Jan 2014)
r72578 | z.li | 2013-01-16 15:11:20 +0800 (Wed, 16 Jan 2013)
r72577 | z.li | 2013-01-16 13:43:05 +0800 (Wed, 16 Jan 2013)


## 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]

## hack chipseeker annotation with CpGI
require(TxDb.Hsapiens.UCSC.hg19.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
library(ChIPseeker)
library(GenomicRanges)
# Hack chipseeker function to add annotaiton
getGenomicAnnoStat <- function(peakAnno) {
    if ( class(peakAnno) == "GRanges" )
        peakAnno <- as.data.frame(peakAnno)
    anno <- peakAnno$annotation
    ## anno <- sub(" \\(.+", "", anno)
	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
	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)
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
	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,

	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)
	}
	f <- function(a, b) function(y) a * y * log(y, base=10) -1/b * exp(-(b*y - b/exp(1))^4)


	y <- seq(0, 1, length.out=100)
	cols = colorspace::rainbow_hcl(5)
	d1 <- data.frame(x = f(3, 30)(y), y=y, color=cols[1])
	d2 <- data.frame(x = f(2.8, 33)(y), y=y, color=cols[2])
	d3 <- data.frame(x = f(2.5, 36)(y), y=y, color=cols[3])
	d4 <- data.frame(x = f(2.2, 40)(y), y=y, color=cols[4])
	d5 <- data.frame(x = f(2, 50)(y), y=y, color=cols[5])
	## 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]
	require(TxDb.Hsapiens.UCSC.hg19.knownGene)
	txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
	library(ChIPseeker)
	library(GenomicRanges)
	# Hack chipseeker function to add annotaiton
	getGenomicAnnoStat <- function(peakAnno) {
	if ( class(peakAnno) == "GRanges" )
	peakAnno <- as.data.frame(peakAnno)
	anno <- peakAnno$annotation
	## anno <- sub(" \\(.+", "", anno)