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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RH12 1XL.
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Financial Conduct Authority and the Prudential Regulation Authority.
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