Read csv is much faster than xlsx.
tb = read.csv(fullFileName, colClasses=c("character",NA, NA, "character", rep("numeric",8 ), NA));
options(echo=TRUE) # if you want see commands in output file
args <- commandArgs(trailingOnly = TRUE)print(args)
# trailingOnly=TRUE means that only your arguments are returned, check:
# print(commandsArgs(trailingOnly=FALSE))i = as.integer(args[1])
j = as.integer(args[2])
x = seq(i, j)
print(x)
R -f R-args.R --args 2 5
Rscript file
#from lower case to upper case
chartr(old, new, x)
tolower(x)
toupper(x)
casefold(x, upper = FALSE)
require(stringr)
conditions$media[r] = str_replace( conditions$media[r], "\\/", "")
tb$AssignmentTotal= apply(tb[, assignments], 1, FUN=function(x){sum(x,na.rm=T)} )
http://cran.r-project.org/doc/FAQ/R-FAQ.html#How-can-I-create-rotated-axis-labels_003f
cumsum()
with()
R -f filename
axis( 2, at=pretty(tbf$s), tcl=0.2, las=2 ) #rotate axis labels
text( tb$G + 0.01*nchar(tb$strain)/4, log10(tb$R0)-0.1*nchar(tb$strain)/4, tb$strain, pos=3)
layout(mat, heights= c( 1.15, rep(1, nrow(mat)-2), 1.2) );
par(mar=c(5.1,4.1,4.1,2.1)
http://www.r-bloggers.com/setting-graph-margins-in-r-using-the-par-function-and-lots-of-cow-milk/
text ( aa, bb, t, cex=0.8);
####aplha
names(fit)[ grep("alpha", names(fit))]
fit_alpha_tb = data.frame( t( fit[, grep("alpha", names(fit)) ]))
rownames(fit_alpha_tb) = names(fit)[grep("alpha", names(fit))]
fit_alpha_tb$names = gsub("_.*", "", rownames(fit_alpha_tb))
library(RColorBrewer);
#hmcol = colorRampPalette(brewer.pal(5,"RdBu"))(8);
hmcol = colorRampPalette(brewer.pal(3,"Blues"))(8);
format(Sys.time(), "%a %b %d %H:%M:%S %Y")
format(Sys.time(), "%Y%b%d_%H%M%S")
#regular expression
require(org.Sc.sgd.db)
x <- org.Sc.sgdALIAS
ls(x)[grep("^Y..\\d{3}", ls(x))]
http://www.regular-expressions.info/rlanguage.html
http://www.r-bloggers.com/regular-expressions-in-r-vs-rstudio/
(WD <- getwd())
if (!is.null(WD)) setwd(WD)
require(xlsx) # read Excel in R.
Usage
! x
x & y
x && y
x | y
x || y
xor(x, y)
rm(list=ls() );
unlist(strsplit("a.b.c", "\\."))
-----
str(x)
attributes(x)
--------------
outer( month.abb, 1999:2003, FUn="paster");
Letters <- c( LETTERS, letters);
Letters[ ! sapply(Letters, function(xx) exists(xx) ) ]; # anonymous function as a wrapper for a primitive function
------------
legend(100,60, seq(100,200,1), lty=1) # line legends
Library(MASS); example(Skye); #tenary plot
library(help = survivial)
ColorBrewer.org
useful comnds:
x11; factor; relevel; class; loess; contour; is.element; math %in%; grep; sample; nrow;
grepmisc: hist2d
url()
---
class and object
CA@a[1]
----
test1 <- list( time= c(4, 3,1,1,2,2,3),
status=c(1,NA,1,0,1,1,0),
x= c(0, 2,1,1,1,0,0),
sex= c(0, 0,0,0,1,1,1))
coxph( Surv(time, status) ~ x + strata(sex), test1) #stratified model
----
delete NA form matrix
> x<-matrix(1:16,4,4)
> x[col(x)>=row(x)]<-NA
> x[,! apply(x,2,function(x) all(is.na(x))) ]
[,1] [,2] [,3]
[1,] NA NA NA
[2,] 2 NA NA
[3,] 3 7 NA
[4,] 4 8 12
----
? R/Splus Perl interface RSperl
? R Pythong interface Rpy Rpython
not in CRAN
----
date.grouping <- function(d) {
# for ea date in d calculate date beginning 6 month period which contains it
mat <- matrix(as.numeric(unlist(strsplit(as.character(d),"-"))),nr=2)
f <- function(x) do.call( "ISOdate", as.list(x) )
POSIXct.dates <- apply(rbind(mat,1),2,f) + ISOdate(1970,1,1)
breaks <- c(seq(from=min(POSIXct.dates), to=max(POSIXct.dates), by="6 mo"), Inf)
format( as.POSIXct( cut( POSIXct.dates, breaks, include.lowest=T )), "%Y-%m" ) }
----
nonlinear regression
library(nls)
----
http://www.bioconductor.org/
----
library(lattice)
----
persp()
----
las=1 or 2
You can use the graphics parameter "srt" to rotate displayed text by a specified number of degrees,
e.g. srt=45 to put it on an angle, srt=90 to put it vertical.
----
cnams = dimnames(aa)[[2]]
cnams[which(cnams == 'blah3.Mg')] = 'Mg (%)'
...
dimnames(aa)[[2]] = cnams
----
eval(substitute(lf <- locfit(~s, data=age), list(s=s)))
------
sub=sort(sample(x,200, replace=F))
postscript("try.ps")
matplot(x[sub],y[sub,],type="l",lwd=5)
dev.off()
-----
>Does anyone know if R has the functionality to calculate a simple
>moving average. I cant seem to find it in the help menu.
filter in library ts. does filter() do what you need?
Or look at the 'running' function in the gregmisc package.
moving.average <-
function(x, k) {
n <- length(x)
y <- rep(0, n)
for (i in (1+k):n)
y[i] <- mean(x[(i-k):i])
return(y)
}
----
tree packages
----
# Create an Example Data Frame Containing Car x Color data, with long car names
carnames <- c("BMW: High End, German",
"Renault: Medium End, French",
"Mercedes: High End, German",
"Seat: Imaginary, Unknown Producer")
carcolors <- c("red","white","silver","green")
datavals <- round(rnorm(16, mean=100, sd=60),1)
data <- data.frame(Car=rep(carnames,4),
Color=rep(carcolors, c(4,4,4,4) ),
Value=datavals )
# generate balloon plot with default scaling, the column labels will overlap
# balloonplot( data$Color, data$Car, data$Value)
# try again, with column labels rodated 90 degrees, and given more space
balloonplot( data$Car, data$Color, data$Value, colmar=3, colsrt=90)
----
Here is a very rough addlogo() using pixmap:
"addlogo" <- function(x, y, pixmap) {
if (is.list(x)) {
y <- x$y
x <- x$x
}
else if (missing(y))
stop("missing y")
if (!is.numeric(x) || !is.numeric(y))
stop("non-numeric coordinates")
if ((nx <- length(x)) <= 1 || nx != length(y) || nx > 2)
stop("invalid coordinate lengths")
pixmap@bbox[1] <- x[1]
pixmap@bbox[2] <- y[1]
pixmap@bbox[3] <- x[2]
pixmap@bbox[4] <- y[2]
pixmap@cellres[1] <- (pixmap@bbox[3] - pixmap@bbox[1]) / pixmap@size[2]
pixmap@cellres[2] <- (pixmap@bbox[4] - pixmap@bbox[2]) / pixmap@size[1]
plot(pixmap, add=TRUE)
invisible(pixmap)
}
which will work with locator() too. To maintain aspect, it shouldn't alter
the relative cell resolutions, and should just use the new x or y, bur
this is the general case. The handling of the location of the logo is
copied & pasted from legend().
----
x <- readLines(myfile)
strsplit(substring(x,8),split="")
----
