遍历R数据帧中的列

Question

I'm writing a code in R that allow me to draw histograms along with median and the quartiles, but I have problems with cycling in the dataframe columns.

You can find in attach the header of my dataframe and the code.

At the end, histograms are produced but median and quartiles are not linked to the real distributions.

  ROI    DOY_119    DOY_127   DOY_143    DOY_151   DOY_175    DOY_191    DOY_215    DOY_239    DOY_263
1   4 -11.592668  -9.457701 -12.57275 -11.073490 -8.999743  -9.132843  -9.995659  -9.511699  -9.393022
2   4 -11.518109 -10.231917 -11.96543 -10.757207 -9.558524  -8.529423  -9.562449  -9.511699  -9.578184
3   4  -9.633711  -9.488475 -12.09012 -10.357404 -8.535619  -8.294449  -9.179331  -7.652297  -6.952941
4   4  -7.752080  -9.578184 -11.30182 -11.073490 -8.992849  -6.197888  -6.556077  -5.883803  -6.324577
5   4 -12.533658  -9.347939 -12.74088 -10.506100 -8.958544 -10.486625 -10.809219 -10.550241  -9.307020
6   4 -13.036436  -8.054857 -13.45823  -9.122186 -7.654827 -10.159230 -10.423927 -11.319436 -10.736576

for (i in 2:ncol(fileIn)){

  myHist <- paste(directory, (i-1), sep="")
  x11(width = 50, height = 50)

  medi <- ddply(fileIn, "ROI", summarise, grp.medi=median (as.numeric(as.matrix(fileIn[i]))))
  q05  <- ddply(fileIn, "ROI", summarise, grp.q05=quantile(as.numeric(as.matrix(fileIn[i]))),0.05)
  q25  <- ddply(fileIn, "ROI", summarise, grp.q25=quantile(as.numeric(as.matrix(fileIn[i]))),0.25)
  q75  <- ddply(fileIn, "ROI", summarise, grp.q75=quantile(as.numeric(as.matrix(fileIn[i]))),0.75)
  q95  <- ddply(fileIn, "ROI", summarise, grp.q95=quantile(as.numeric(as.matrix(fileIn[i]))),0.95)

  plotHist <-
    ggplot(fileIn) +
    aes(x = as.numeric(as.matrix(fileIn[i,]))) +                                              
    # aes(x = DOY_119) +                                              
    geom_histogram(alpha = 0.5, binwidth = 0.5, color="grey", fill= "yellow") +                                       
    geom_density(color = "green", fill= "green", alpha = 0.5) +
    geom_vline(data=medi, aes(xintercept=grp.medi), color="red",   size = 0.7) +
    geom_vline(data=q05,  aes(xintercept=grp.q05),  color="black", size = 0.3) +
    geom_vline(data=q25,  aes(xintercept=grp.q25),  color="blue",  size = 0.5) +
    geom_vline(data=q75,  aes(xintercept=grp.q75),  color="blue",  size = 0.5) +
    geom_vline(data=q95,  aes(xintercept=grp.q95),  color="black", size = 0.3) +
    theme(axis.text.x = element_text(colour = "black"),
          axis.text.y = element_text(colour = "black")) +         
    facet_wrap( ~ ROI, scales = "free")

  plot(plotHist)

  #------------------------------------------------------------------------------------------------------
  # salvataggio X11

  dev.copy(jpeg, myHist, width=2000, height=1000, res=100)
  dev.off()
}

Answer 1

Here is a start for you. This is the kind of problem that can be solved in multiple ways, and one is not always better than the other. In your code, you were doing some things quite inefficiently (like calculating the quantiles and creating the vline ). Generally in ggplot, if you find yourself repeating very similar lines (like 5 calls to vline) there is a better approach. I've replaced with the calculation of one 'vline_data and fed that to geom_vline` in combination with some manual scales.

#add second ROI for plotting/demonstration purposes
fileIn2 <- fileIn
fileIn2$ROI <- 5

fileIn <- rbind(fileIn,fileIn2)


myplots <- lapply(colnames(fileIn)[-1],function(col_of_interest){
#create summary_data for quantiles
  vline_data <- ddply(fileIn,.(ROI), function(x){
    myprobs=c(0.05,0.25,0.5,0.75,0.95)
    res <- data.frame(prob=as.character(myprobs),value=quantile(x[,col_of_interest],probs=myprobs) )
    res
  })
  #create plot. Note the use of aes_string here.
  plotHist <-
    ggplot(fileIn, aes_string(x=col_of_interest))+
    geom_histogram(alpha = 0.5, binwidth = 0.5, color="grey", fill= "yellow") +                                       
    geom_density(color = "green", fill= "green", alpha = 0.5) +
    geom_vline(data=vline_data, aes(xintercept=value,size=prob,color=prob)) +
    scale_color_manual(values=c("black","blue","red","black","blue"),
                       breaks=as.character(c(0.05,0.25,0.5,0.75,0.95)))+
    scale_size_manual(values=c(0.3,0.5,0.7,0.5,0.3),
                      breaks=as.character(c(0.05,0.25,0.5,0.75,0.95)))+
    facet_wrap( ~ ROI, scales = "free") 
  #optional: use `ggsave` here. 
  #ggsave(file=paste(directory,col_of_interest,".png"),plot=plotHist)
  return(plotHist)
}
)