Question

你写的是什么功能，不值得一个包，但你想分享？

我会投入一些我的：

destring <- function(x) {
    ## convert factor to strings
    if (is.character(x)) {
        as.numeric(x)
    } else if (is.factor(x)) {
        as.numeric(levels(x))[x]
    } else if (is.numeric(x)) {
        x
    } else {
        stop("could not convert to numeric")
    }
}

pad0 <- function(x,mx=NULL,fill=0) {
  ## pad numeric vars to strings of specified size
  lx <- nchar(as.character(x))
  mx.calc <- max(lx,na.rm=TRUE)
  if (!is.null(mx)) {
    if (mx<mx.calc) {
      stop("number of maxchar is too small")
    }
  } else {
    mx <- mx.calc
  }
  px <- mx-lx
  paste(sapply(px,function(x) paste(rep(fill,x),collapse="")),x,sep="")
}


.eval <- function(evaltext,envir=sys.frame()) {
  ## evaluate a string as R code
  eval(parse(text=evaltext), envir=envir)
}

## trim white space/tabs
## this is marek's version
trim<-function(s) gsub("^[[:space:]]+|[[:space:]]+$","",s)

Answer 1

这是一个用伪透明度绘制重叠直方图的函数：

Overlapping Histograms http://chrisamiller.com/images/histOverlap.png

plotOverlappingHist <- function(a, b, colors=c("white","gray20","gray50"),
                            breaks=NULL, xlim=NULL, ylim=NULL){

  ahist=NULL
  bhist=NULL

  if(!(is.null(breaks))){
    ahist=hist(a,breaks=breaks,plot=F)
    bhist=hist(b,breaks=breaks,plot=F)
  } else {
    ahist=hist(a,plot=F)
    bhist=hist(b,plot=F)

    dist = ahist$breaks[2]-ahist$breaks[1]
    breaks = seq(min(ahist$breaks,bhist$breaks),max(ahist$breaks,bhist$breaks),dist)

    ahist=hist(a,breaks=breaks,plot=F)
    bhist=hist(b,breaks=breaks,plot=F)
  }

  if(is.null(xlim)){
    xlim = c(min(ahist$breaks,bhist$breaks),max(ahist$breaks,bhist$breaks))
  }

  if(is.null(ylim)){
    ylim = c(0,max(ahist$counts,bhist$counts))
  }

  overlap = ahist
  for(i in 1:length(overlap$counts)){
    if(ahist$counts[i] > 0 & bhist$counts[i] > 0){
      overlap$counts[i] = min(ahist$counts[i],bhist$counts[i])
    } else {
      overlap$counts[i] = 0
    }
  }

  plot(ahist, xlim=xlim, ylim=ylim, col=colors[1])
  plot(bhist, xlim=xlim, ylim=ylim, col=colors[2], add=T)
  plot(overlap, xlim=xlim, ylim=ylim, col=colors[3], add=T)
}

如何运行它的示例：

a = rnorm(10000,5)
b = rnorm(10000,3)
plotOverlappingHist(a,b)

更新：FWIW，有一种可能更简单的方法来实现这一点，我已经学会了透明度：

a=rnorm(1000, 3, 1)
b=rnorm(1000, 6, 1)
hist(a, xlim=c(0,10), col="red")
hist(b, add=T, col=rgb(0, 1, 0, 0.5)

Answer 2

R中fft（快速傅里叶变换）函数的输出处理起来可能有点繁琐。我写了这个plotFFT函数，以便进行FFT的频率与功率曲线。 getFFTFreqs函数（由plotFFT内部使用）返回与每个FFT值关联的频率。

这主要基于http://tolstoy.newcastle.edu.au/R/help/05/08/11236.html

上非常有趣的讨论

# Gets the frequencies returned by the FFT function
getFFTFreqs <- function(Nyq.Freq, data)
    {
    if ((length(data) %% 2) == 1) # Odd number of samples
        {
        FFTFreqs <- c(seq(0, Nyq.Freq, length.out=(length(data)+1)/2), 
               seq(-Nyq.Freq, 0, length.out=(length(data)-1)/2))
        }
    else # Even number
        {
        FFTFreqs <- c(seq(0, Nyq.Freq, length.out=length(data)/2), 
               seq(-Nyq.Freq, 0, length.out=length(data)/2))
        }

    return (FFTFreqs)
    }

# FFT plot
# Params:
# x,y -> the data for which we want to plot the FFT 
# samplingFreq -> the sampling frequency
# shadeNyq -> if true the region in [0;Nyquist frequency] will be shaded
# showPeriod -> if true the period will be shown on the top
# Returns a list with:
# freq -> the frequencies
# FFT -> the FFT values
# modFFT -> the modulus of the FFT
plotFFT <- function(x, y, samplingFreq, shadeNyq=TRUE, showPeriod = TRUE)
    {
    Nyq.Freq <- samplingFreq/2
    FFTFreqs <- getFFTFreqs(Nyq.Freq, y)

    FFT <- fft(y)
    modFFT <- Mod(FFT)
    FFTdata <- cbind(FFTFreqs, modFFT)
    plot(FFTdata[1:nrow(FFTdata)/2,], t="l", pch=20, lwd=2, cex=0.8, main="",
        xlab="Frequency (Hz)", ylab="Power")
    if (showPeriod == TRUE)
        {
        # Period axis on top        
        a <- axis(3, lty=0, labels=FALSE)
        axis(3, cex.axis=0.6, labels=format(1/a, digits=2), at=a)
        }
    if (shadeNyq == TRUE)
        {
        # Gray out lower frequencies
        rect(0, 0, 2/max(x), max(FFTdata[,2])*2, col="gray", density=30)
        }

    ret <- list("freq"=FFTFreqs, "FFT"=FFT, "modFFT"=modFFT)
    return (ret)
    }

举个例子，你可以试试这个

# A sum of 3 sine waves + noise
x <- seq(0, 8*pi, 0.01)
sine <- sin(2*pi*5*x) + 0.5 * sin(2*pi*12*x) + 0.1*sin(2*pi*20*x) + 1.5*runif(length(x))
par(mfrow=c(2,1))
plot(x, sine, "l")
res <- plotFFT(x, sine, 100)

或

linearChirp <- function(fr=0.01, k=0.01, len=100, samplingFreq=100)
    {
    x <- seq(0, len, 1/samplingFreq)
    chirp <- sin(2*pi*(fr+k/2*x)*x) 

    ret <- list("x"=x, "y"=chirp)
    return(ret)
    }

chirp <- linearChirp(1, .02, 100, 500)
par(mfrow=c(2,1))
plot(chirp, t="l")
res <- plotFFT(chirp$x, chirp$y, 500, xlim=c(0, 4))

哪个给

FFT plot of sine waves http://www.nicolaromano.net/misc/sine.jpg FFT plot of a linear chirp http://www.nicolaromano.net/misc/chirp.jpg

Answer 3

非常简单，但我经常使用它：

setdiff2 <- function(x,y) {
    #returns a list of the elements of x that are not in y 
     #and the elements of y that are not in x (not the same thing...)

    Xdiff = setdiff(x,y)
    Ydiff = setdiff(y,x)
    list(X_not_in_Y=Xdiff, Y_not_in_X=Ydiff)
}

Answer 4

# Create a circle with n number of "sides" (kudos to Barry Rowlingson, r-sig-geo).
circle <-  function(x = 0, y = 0, r = 100, n = 30){
    t <- seq(from = 0, to = 2 * pi, length = n + 1)[-1]
    t <- cbind(x = x + r * sin(t), y = y + r * cos(t))
    t <- rbind(t, t[1,])
    return(t)
}
# To run it, use
plot(circle(x = 0, y = 0, r = 50, n = 100), type = "l")

Answer 5

我很烦恼打印了很多列的data.frame，我的意思是这是对列的拆分。所以我写了自己的版本：

print.data.frame <- function(x, ...) {
    oWidth <- getOption("width")
    oMaxPrint <- getOption("max.print")
    on.exit(options(width=oWidth, max.print=oMaxPrint))
    options(width=10000, max.print=300)
    base::print.data.frame(x, ...)
}

Answer 6

我经常想在回归中使用和比，我通常希望这些术语有意义地命名。所以我写了这个recontrast函数。

recontrast<-function(data,type = "sum"){
    data.type <-class(data)
    if(data.type == "factor"&!is.ordered(data)&nlevels(data)>1&nlevels(data)<1000){
        if(type == "sum"){
            contrasts(data)<-contr.sum(levels(data))
            colnames(contrasts(data))<-levels(data)[-nlevels(data)]
        }else if(type == "treatment"){
            contrasts(data)<-contr.treatment(levels(data))
        }
    }else if(data.type == "data.frame"){
        for(i in 1:ncol(data)){
            if(is.factor(data[,i]) &     !is.ordered(data[,i])&nlevels(data[,i])>1&nlevels(data[,i])<1000){
                if(type == "sum"){
                    contrasts(data[,i])<-contr.sum(levels(data[,i]))
                    colnames(contrasts(data[,i]))<-levels(data[,i])[-    nlevels(data[,i])]
                }else if(type == "treatment"){
                    contrasts(data[,i])<-    contr.treatment(levels(data[,i]))
                }
            }
        }
    }
return(data)
}

它将整个数据帧和因子作为参数。如果它是一个数据框，它会将无序因子的所有对比度转换为<1000水平的治疗或总和对比。通过总和对比，它有意义地命名列，因此您将在回归输出中包含有意义的标签。

Answer 7

在最有用的R技巧发布中，我看到了Keving从2009年11月3日发布的一篇文章，其中有关未使用的水平。那里提供了第一个功能。我在第二个函数中迈出了最好的一步，从子集中删除了级别。

drop.levels <- function (dat) {if (is.factor(dat)) dat <- dat[, drop = TRUE] else dat[] <- lapply(dat, function(x) x[, drop = TRUE]); return(dat) ;};

subset.d    <- function (...) drop.levels(subset(...)); # function to drop levels of subset

R中有用的小功能？

7 个答案: