Hello Uwe
Can you explain what you mean by "try to plot the sensity only with few
hundreds of segemnts"
Do you mean that I should take some kind of sample and do it? And if so
what's the best number for the sample and how do i ensure that its not
biased?
From: Uwe Ligges <lig...@statistik.tu-dortmund.de>
To: Pavneet Arora/UK/RoyalSun@RoyalSun, r-help@r-project.org,
pavnee...@yahoo.co.uk
Date: 15/04/2014 14:08
Subject: Re: [R] Fw: Save multiple plots as pdf or jpeg
You have > 1e6 observations and your lines() have these many segments,
try to plot the sensity only with few hndreds of segemnts.
Best,
Uwe Ligges
On 15.04.2014 12:27, Pavneet Arora wrote:
> Hello All,
>
> I have multiple plots that I want to save it a single file. At the
moment,
> I am saving as pdf. Each plot has a qqnorm, qqline, tiny histogram in
the
> top left graph with its density drawn top of it and the normal
> superimposed on the histogram.
>
> As a result, the pdf takes forever to load and doesn?t let me print, as
it
> runs out of memory. I was wondering if there is a way if I can save each
> plot as jpeg and then export it on pdf. Will that make it quicker in
> loading? If so, how can I do this? And if not, then what are the
> alternatives.
>
> The code that I have at the moment is as follows:
>
>
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
> pdf(file="C:/qqnorm/qqnorms.pdf") ##- Saves all plots in the same pdf
file
>
>
> for (k in 1:ncol(nums2)){
> par(mfrow=c(1,1))
>
> ##- QQNorm
> qqnorm(nums2[,k],col="lightblue",main=names(nums2)[k])
> qqline(nums2[,k],col="red",lwd=2)
>
> ##- Tiny Histogram
> op = par(fig=c(.02,.5,0.4,0.98), new=TRUE)
> hist(nums2[,k],freq=F,col="blue",xlab="", ylab="", main="",
> axes=F,density=20)
>
> ##- Density of the variable
> lines(density(nums2[,k],na.rm=T), col="darkred", lwd=2)
>
> ##- Super-imposed Normal Density
> curve(dnorm(x,mean=mean(nums2[,k],na.rm=T),sd=sd(nums2[,k],na.rm=T)),
> col="black",lwd=3,add=T) ##- Footnote: title1 <- "nums2[k]"
>
> library(moments)
> s_kurt <- kurtosis (nums2[,k])
> s_skew <- skewness (nums2[,k])
> mtxt <- paste ("Variable=",title1, ":" ,
> "Kurt=",round(s_kurt,digits=4), "Skew",
> round(s_skw,digits=4), sep=" ")
>
> mtext (mtxt,col="green4",side=1,line=15.6,adj=0.0,cex=0.8,font=2,las=1)
>
> }
> dev.off()
>
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
>
> Structure of My data:
>
> str(nums2)
> 'data.frame': 1355615 obs. of 39 variables:
> $ month : int 1 1 1 1 1 1 1 1 1 1 ...
> $ Location_Easting_OSGR : int 525680 524170 524520 526900
> 528060 524770 524220 525890 527350 524550 ...
> $ Location_Northing_OSGR : int 178240 181650 182240 177530
> 179040 181160 180830 179710 177650 180810 ...
> $ Longitude : num -0.191 -0.212 -0.206 -0.174
> -0.157 ...
> $ Latitude : num 51.5 51.5 51.5 51.5 51.5 ...
> $ Police_Force : int 1 1 1 1 1 1 1 1 1 1 ...
> $ Number_of_Vehicles : int 1 1 2 1 1 2 2 1 2 2 ...
> $ Number_of_Casualties : int 1 1 1 1 1 1 1 2 2 5 ...
> $ Day_of_Week : int 3 4 5 6 2 3 5 6 7 7 ...
> $ Local_Authority__District_ : int 12 12 12 12 12 12 12 12 12 12
> ...
> $ X_1_st_Road_Class : int 3 4 5 3 6 6 5 3 3 4 ...
> $ X_1_st_Road_Number : int 3218 450 0 3220 0 0 0 315
3212
> 450 ...
> $ Road_Type : int 6 3 6 6 6 6 6 3 6 6 ...
> $ Speed_limit : int 30 30 30 30 30 30 30 30 30 30
> ...
> $ Junction_Detail : int 0 6 0 0 0 0 3 0 6 3 ...
> $ Junction_Control : int -1 2 -1 -1 -1 -1 4 -1 2 4 ...
> $ X_2_nd_Road_Class : int -1 5 -1 -1 -1 -1 6 -1 4 5 ...
> $ X_2_nd_Road_Number : int 0 0 0 0 0 0 0 0 304 0 ...
> $ Pedestrian_Crossing_Human_Contro: int 0 0 0 0 0 0 0 0 0 0 ...
> $ Pedestrian_Crossing_Physical_Fac: int 1 5 0 0 0 0 0 0 5 8 ...
> $ Light_Conditions : int 1 4 4 1 7 1 4 1 4 1 ...
> $ Weather_Conditions : int 2 1 1 1 1 2 1 1 1 1 ...
> $ Road_Surface_Conditions : int 2 1 1 1 2 2 1 1 1 1 ...
> $ Special_Conditions_at_Site : int 0 0 0 0 0 6 0 0 0 0 ...
> $ Carriageway_Hazards : int 0 0 0 0 0 0 0 0 0 0 ...
> $ Urban_or_Rural_Area : int 1 1 1 1 1 1 1 1 1 1 ...
> $ Did_Police_Officer_Attend_Scene_: int 1 1 1 1 1 1 1 1 1 1 ...
> $ year : int 2005 2005 2005 2005 2005 2005
> 2005 2005 2005 2005 ...
> $ m : int 1 1 1 1 1 1 1 1 1 1 ...
> $ Qrtr : int 1 1 1 1 1 1 1 1 1 1 ...
> $ h2 : int 17 17 0 10 21 12 20 17 22 16
...
> $ NumberVehGrp : int 1 1 2 1 1 2 2 1 2 2 ...
> $ NumberCasultGrp : int 1 1 1 1 1 1 1 2 2 5 ...
> $ lati_round : num 51.5 51.5 51.5 51.5 51.5 ...
> $ longi_round : num -0.19 -0.21 -0.21 -0.17 -0.16
> -0.2 -0.21 -0.19 -0.17 -0.21 ...
> $ lati_2dp : num 51.5 51.5 51.5 51.5 51.5 ...
> $ lati_1dp : num 51.5 51.5 51.5 51.5 51.5 51.5
> 51.5 51.5 51.5 51.5 ...
> $ longi_2dp : num -0.19 -0.21 -0.21 -0.17 -0.16
> -0.2 -0.21 -0.19 -0.17 -0.21 ...
> $ longi_1dp : num -0.2 -0.2 -0.2 -0.2 -0.2 -0.2
> -0.2 -0.2 -0.2 -0.2 ...
>
>
> Also when saving as jpeg in R, I realise there is a wildcard in
filenames,
> i.e.. 6 plots can be saved as:
> jpeg(filename="foo%03d.jpeg",. . . )
> dev.off()
>
> But is there any way, where I can save them as the variable name instead
-
> so perhaps some use of macro or loop to do so?
>
>
>
>
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