Hello gurus,
I have a dataframe containing two groups viz., 'control' and 'case', each of
these groups contains longitudinal data for 100 subjects. I have to plot all
these subjects on a single chart and then put a regression line for each of
the group for all the subjects. I have written a function to do the chart
grpcharts<-function (dat, group,group2,molecule,cutoff){
dat2<-dat[grep(group,dat$Group),]
ylim=log2(c(min(dat2[,molecule],na.rm=T)+4,max(dat2[,molecule],na.rm=T)+1))
all.sub.id<-dat2$Subject.ID
if (group=='control'){
col=c('blue')
}else{col=c('red')}
if(group2=='case'){
col2=c('red')
}else{ col2=c('blue')}
uniq.sub.id<-unique(all.sub.id)
errcol<-c()
for (i in 1: length(uniq.sub.id))
{
sub<-dat2[grep(uniq.sub.id[i],dat2$Subject.ID),]
sub<- sub[order(sub$Age.at.Sample.Collection),]
sub<-sub[sub[,molecule]>cutoff,]
sub.id<-uniq.sub.id[i]
if (dim(sub)[1]<=1) errcol<-c(errcol, sub.id)
if (dim(sub)[1]>1)
{
par(new=TRUE)
plot(log2(sub[,molecule])~sub$Age.at.Sample.Collection,
ylab=paste('Log2_',molecule,sep=''),xlab="Age at Sample",pch=1, ylim=ylim,
xlim=c(0,25),main=paste(group,'-',molecule))
mod<-loess(log2(sub[,molecule])~Age.at.Sample.Collection,
na.action=na.exclude, data=sub)
pred<-predict(mod)
lines(pred~Age.at.Sample.Collection, data=sub,lwd=1, lty=1)
}
}
dat2<-dat2[order(dat2$Age.at.Sample.Collection),]
mod<-loess(log2(dat2[,molecule])~Age.at.Sample.Collection,
na.action=na.exclude, data=dat2)
pred<-predict(mod)
lines(pred~Age.at.Sample.Collection, data=dat2,lwd=2, lty=1,col=col)
dat2<-dat[grep(group2,dat$Group),]
dat2<-dat2[order(dat2$Age.at.Sample.Collection),]
mod<-loess(log2(dat2[,molecule])~Age.at.Sample.Collection,
na.action=na.exclude, data=dat2)
pred<-predict(mod)
lines(pred~Age.at.Sample.Collection, data=dat2,lwd=2, lty=1,col=col2)
legend(c(20,20),c(ylim),c(group,group2), lty=1,lwd=2,col=c(col,col2),
bty='n')
print('done')
}
the function subsets the data based on the 'group' and plots the datapoints
currently I am using a loop to assign the colors under two conditions. I
need some pointers to assign the colors to the regression lines for the two
groups without using a loop.
thanks
sharad
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A better way to do this
5 messages · Lao Meng, 1Rnwb, Dennis Murphy
Hi: Could you post a small, reproducible data set that illustrates what you want to do? It sounds like you're creating 'spaghetti plots', which can be done with a minimal amount of pain in ggplot2. Dennis
On Thu, May 19, 2011 at 11:29 AM, 1Rnwb <sbpurohit at gmail.com> wrote:
Hello gurus,
I have a dataframe containing two groups viz., 'control' and 'case', each of
these groups contains longitudinal data for 100 subjects. I have to plot all
these subjects on a single chart and then put a regression line for each of
the group for all the subjects. I have written a function to do the chart
grpcharts<-function (dat, group,group2,molecule,cutoff){
dat2<-dat[grep(group,dat$Group),]
ylim=log2(c(min(dat2[,molecule],na.rm=T)+4,max(dat2[,molecule],na.rm=T)+1))
all.sub.id<-dat2$Subject.ID
?if (group=='control'){
? ? ? ? col=c('blue')
}else{col=c('red')}
if(group2=='case'){
? ? ? ?col2=c('red')
}else{ col2=c('blue')}
uniq.sub.id<-unique(all.sub.id)
? ? ? ? ? ? ? ? errcol<-c()
? ? ? ? ? ? ? ? ? ? ? ?for (i in 1: length(uniq.sub.id))
? ? ? ? ? ? ? ? ? ? ? ?{
? ? ? ? ? ? ? ? ? ? ? ? sub<-dat2[grep(uniq.sub.id[i],dat2$Subject.ID),]
? ? ? ? ? ? ? ? ? ? ? ? sub<- sub[order(sub$Age.at.Sample.Collection),]
? ? ? ? ? ? ? ? ? ? ? ? sub<-sub[sub[,molecule]>cutoff,]
? ? ? ? ? ? ? ? ? ? ? ? sub.id<-uniq.sub.id[i]
? ? ? ? ? ? ? ? ? ? ? ? if (dim(sub)[1]<=1) errcol<-c(errcol, sub.id)
? ? ? ? ? ? ? ? ? ? ? ? if (dim(sub)[1]>1)
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?{
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? par(new=TRUE)
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? plot(log2(sub[,molecule])~sub$Age.at.Sample.Collection,
ylab=paste('Log2_',molecule,sep=''),xlab="Age at Sample",pch=1, ylim=ylim,
xlim=c(0,25),main=paste(group,'-',molecule))
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? mod<-loess(log2(sub[,molecule])~Age.at.Sample.Collection,
na.action=na.exclude, data=sub)
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? pred<-predict(mod)
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? lines(pred~Age.at.Sample.Collection, data=sub,lwd=1, lty=1)
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? }
? ? ? ? ? ? ? ? ? ? ? ?}
? ? ? ? ? ? ? ? ? dat2<-dat2[order(dat2$Age.at.Sample.Collection),]
? ? ? ? ? ? ? ? ? mod<-loess(log2(dat2[,molecule])~Age.at.Sample.Collection,
na.action=na.exclude, data=dat2)
? ? ? ? ? ? ? ? ? pred<-predict(mod)
? ? ? ? ? ? ? ? ? lines(pred~Age.at.Sample.Collection, data=dat2,lwd=2, lty=1,col=col)
? ? ? ? ? ? ? ? ? dat2<-dat[grep(group2,dat$Group),]
? ? ? ? ? ? ? ? ? dat2<-dat2[order(dat2$Age.at.Sample.Collection),]
? ? ? ? ? ? ? ? ? mod<-loess(log2(dat2[,molecule])~Age.at.Sample.Collection,
na.action=na.exclude, data=dat2)
? ? ? ? ? ? ? ? ? pred<-predict(mod)
? ? ? ? ? ? ? ? ? lines(pred~Age.at.Sample.Collection, data=dat2,lwd=2, lty=1,col=col2)
? ? ? ? ? ? ? ? ? legend(c(20,20),c(ylim),c(group,group2), lty=1,lwd=2,col=c(col,col2),
bty='n')
? ? ? ? ? ? ? ? ?print('done')
? ? ? ?}
the function subsets the data based on the 'group' and plots the datapoints
currently I am using a loop to assign the colors under two conditions. I
need some pointers to assign the colors to the regression lines for the two
groups without using a loop.
thanks
sharad
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2 days later
here is the data set, yes i am doing spagghetti plots for each Subject for MCP1 with respect to the Age.at.Sample.Collection, with a final of all the controls and all the cases Subject.ID sample Group Age.at.Sample.Collection MCP1 19 00173-0 3455 control 11.767282 212.4438625 104 00173-0 4992 control 4.087611 222.6706897 148 00173-0 5046 control 5.103353 257.5997389 192 00173-0 5205 control 7.249828 191.8050229 223 00173-0 4991 control 6.061601 171.2995728 235 00173-0 5295 control 7.843942 240.2288223 275 00173-0 5366 control 9.817932 64.9237708 319 00173-0 5473 control 10.704996 199.4368169 2102 00173-0 3140 control 15.260780 366.1121484 3174 00173-0 4950 control 14.012320 253.4390051 3883 00173-0 4103 control 12.974674 260.3490578 23 01039-0 901388 control 3.058179 273.6916770 491 01039-0 901068 control 1.284052 255.5981436 735 01039-0 900985 control 1.968514 450.2616943 1199 01039-0 900733 control 5.086926 161.5011670 3948 01039-0 901422 control 4.041067 365.8560986 24 21038-0 901594 control 8.418891 303.9160091 128 21038-0 900089 control 12.440793 250.4846256 381 21038-0 901157 control 6.477754 35.3079462 486 21038-0 901739 control 6.669404 77.1044173 582 21038-0 900313 control 10.464065 233.8540385 972 21038-0 900527 control 11.438740 194.8173368 1256 21038-0 900262 control 14.472279 175.7577419 1565 21038-0 901475 control 2.015058 246.2465683 1641 21038-0 901216 control 4.416153 99.7496760 1957 21038-0 901618 control 6.669404 141.4459310 2291 21038-0 900563 control 3.025325 278.6264989 3131 21038-0 900363 control 5.470225 103.3293132 3704 21038-0 900872 control 9.434633 153.7875157 3984 21038-0 900766 control 13.437371 269.4589484 2 00041-0 3810 case 17.368925 203.1979716 310 00041-0 4923 case 17.648186 168.8229783 444 00041-0 3842 case 16.375085 102.6136126 1082 00041-0 4078 case 16.813141 89.7548225 1459 00041-0 5005 case 14.817248 131.0459201 1464 00041-0 4816 case 16.082135 131.1120569 1470 00041-0 3495 case 15.323750 118.7034216 1677 00041-0 3991 case 9.782340 169.1765831 1845 00041-0 4824 case 8.520191 122.3348897 1928 00041-0 4599 case 16.651608 2.3051960 2142 00041-0 3146 case 15.164955 191.6823727 2184 00041-0 3220 case 15.526351 188.6667153 2283 00041-0 5239 case 18.269678 174.9620952 2542 00041-0 3536 case 10.910335 301.6238182 2883 00041-0 4787 case 13.527720 159.0956166 2921 00041-0 4058 case 17.845311 137.7700442 2932 00041-0 5015 case 7.520876 101.6216590 3641 00041-0 4266 case 5.497604 125.6831543 3683 00041-0 4360 case 4.421629 187.2961108 3725 00041-0 4428 case 13.451060 157.4203222 3767 00041-0 4506 case 6.507871 141.1115853 3889 00041-0 4455 case 12.147843 213.7489685 3 00709-0 5023 case 2.004106 412.4962003 398 00709-0 3180 case 5.234770 9.6108457 639 00709-0 3205 case 4.974674 262.5951990 1058 00709-0 3788 case 4.577686 281.6220685 1700 00709-0 3450 case 4.156057 131.7354794 2050 00709-0 3183 case 5.878165 0.3707844 2483 00709-0 4434 case 2.162902 339.0410965 2789 00709-0 3849 case 4.405201 405.9685980 3281 00709-0 3619 case 5.541409 255.1460421 3842 00709-0 3943 case 3.515400 624.2636701 4011 00709-0 5304 case 3.312799 2.9206063 4 00174-0 4142 case 16.060232 240.4858235 13 00174-0 4244 case 5.615331 NA 22 00174-0 4446 case 15.523613 223.3748681 57 00174-0 4292 case 6.209445 168.6679707 100 00174-0 4314 case 6.666666 147.6358300 183 00174-0 3689 case 16.224503 0.5689179 270 00174-0 4208 case 3.468856 286.9223725 313 00174-0 4223 case 4.427104 178.0005964 471 00174-0 3188 case 11.633127 150.2065531 554 00174-0 3212 case 12.377823 217.9799114 1304 00174-0 5193 case 14.694045 150.0169015 1463 00174-0 4443 case 13.626283 155.9339661 1589 00174-0 5294 case 13.990417 167.2262451 1884 00174-0 4349 case 15.271731 146.8486102 1975 00174-0 3857 case 10.611909 82.6978002 2084 00174-0 3174 case 10.924024 217.8106656 2196 00174-0 5126 case 16.892539 272.8141490 2748 00174-0 4607 case 16.481861 156.5503087 2963 00174-0 4861 case 11.967145 294.4989050 3294 00174-0 4859 case 12.783025 205.6801413 3566 00174-0 4380 case 9.070499 84.6743348 3608 00174-0 4405 case 10.132785 92.1683089 3844 00174-0 4252 case 10.324435 305.3678248 3923 00174-0 4193 case 11.340177 336.7327758
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Hi:
Here are a few options for making this type of plot:
# ggplot2
library(ggplot2)
ggplot(dd, aes(x = Age.at.Sample.Collection, y = MCP1, group =
Subject.ID, colour = Subject.ID,
shape = Group)) +
geom_point(size = 2.5) + geom_line(size = 0.6) + labs(x = 'Age')
ggplot(dd, aes(x = Age.at.Sample.Collection, y = MCP1, group =
Subject.ID, colour = Subject.ID)) +
geom_point(size = 2.5) + geom_line() + labs(x = 'Age') +
facet_wrap( ~ Group, ncol = 1)
ggplot(dd, aes(x = Age.at.Sample.Collection, y = MCP1, group =
Subject.ID, colour = Subject.ID)) +
theme_bw() + geom_point(size = 2.5) + geom_line() +
labs(x = 'Age') + facet_wrap( ~ Group, nrow = 1)
# lattice
subjectKey <- list(space = 'right',
title = 'Subject ID',
cex.title = 1.2,
text = list(levels(dd$Subject.ID)),
points = list(pch = rep(c(16, 17), each = 3), cex =
1, col = 1:6))
xyplot(MCP1 ~ Age.at.Sample.Collection | Group, data = dd, group = Subject.ID,
type = c('p', 'a', 'g'), cex = 1, pch = rep(c(16, 17), each = 3),
col = 1:6, xlab = 'Age',
key = subjectKey)
xyplot(MCP1 ~ Age.at.Sample.Collection, data = dd, group = Subject.ID,
cex = 1, pch = rep(c(16, 17), each = 3), col = 1:6, type =
c('p', 'a', 'g'), xlab = 'Age',
key = subjectKey)
HTH,
Dennis
On Sun, May 22, 2011 at 9:46 AM, 1Rnwb <sbpurohit at gmail.com> wrote:
here is the data set, yes i am doing spagghetti plots for each Subject for MCP1 with respect to the Age.at.Sample.Collection, with a final of all the controls and all the cases ?Subject.ID sample ? Group Age.at.Sample.Collection ? ? ? ?MCP1 19 ? ? ?00173-0 ? 3455 control ? ? ? ? ? ? ? ?11.767282 212.4438625 104 ? ? 00173-0 ? 4992 control ? ? ? ? ? ? ? ? 4.087611 222.6706897 148 ? ? 00173-0 ? 5046 control ? ? ? ? ? ? ? ? 5.103353 257.5997389 192 ? ? 00173-0 ? 5205 control ? ? ? ? ? ? ? ? 7.249828 191.8050229 223 ? ? 00173-0 ? 4991 control ? ? ? ? ? ? ? ? 6.061601 171.2995728 235 ? ? 00173-0 ? 5295 control ? ? ? ? ? ? ? ? 7.843942 240.2288223 275 ? ? 00173-0 ? 5366 control ? ? ? ? ? ? ? ? 9.817932 ?64.9237708 319 ? ? 00173-0 ? 5473 control ? ? ? ? ? ? ? ?10.704996 199.4368169 2102 00173-0 ? 3140 control ? ? ? ? ? ? ? ?15.260780 366.1121484 3174 00173-0 ? 4950 control ? ? ? ? ? ? ? ?14.012320 253.4390051 3883 ? ?00173-0 ? 4103 control ? ? ? ? ? ? ? ?12.974674 260.3490578 23 ? ? ?01039-0 901388 control ? ? ? ? ? ? ? ? 3.058179 273.6916770 491 ? ? 01039-0 901068 control ? ? ? ? ? ? ? ? 1.284052 255.5981436 735 ? ? 01039-0 900985 control ? ? ? ? ? ? ? ? 1.968514 450.2616943 1199 ? ?01039-0 900733 control ? ? ? ? ? ? ? ? 5.086926 161.5011670 3948 ? ?01039-0 901422 control ? ? ? ? ? ? ? ? 4.041067 365.8560986 24 ? ? ?21038-0 901594 control ? ? ? ? ? ? ? ? 8.418891 303.9160091 128 ? ? 21038-0 900089 control ? ? ? ? ? ? ? ?12.440793 250.4846256 381 ? ? 21038-0 901157 control ? ? ? ? ? ? ? ? 6.477754 ?35.3079462 486 ? ? 21038-0 901739 control ? ? ? ? ? ? ? ? 6.669404 ?77.1044173 582 ? ? 21038-0 900313 control ? ? ? ? ? ? ? ?10.464065 233.8540385 972 ? ? 21038-0 900527 control ? ? ? ? ? ? ? ?11.438740 194.8173368 1256 ? ?21038-0 900262 control ? ? ? ? ? ? ? ?14.472279 175.7577419 1565 ? ?21038-0 901475 control ? ? ? ? ? ? ? ? 2.015058 246.2465683 1641 ? ?21038-0 901216 control ? ? ? ? ? ? ? ? 4.416153 ?99.7496760 1957 ? ?21038-0 901618 control ? ? ? ? ? ? ? ? 6.669404 141.4459310 2291 ? ?21038-0 900563 control ? ? ? ? ? ? ? ? 3.025325 278.6264989 3131 ? ?21038-0 900363 control ? ? ? ? ? ? ? ? 5.470225 103.3293132 3704 ? ?21038-0 900872 control ? ? ? ? ? ? ? ? 9.434633 153.7875157 3984 ? ?21038-0 900766 control ? ? ? ? ? ? ? ?13.437371 269.4589484 2 ? ? ? 00041-0 ? 3810 ? ?case ? ? ? ? ? ? ? ?17.368925 203.1979716 310 ? ? 00041-0 ? 4923 ? ?case ? ? ? ? ? ? ? ?17.648186 168.8229783 444 ? ? 00041-0 ? 3842 ? ?case ? ? ? ? ? ? ? ?16.375085 102.6136126 1082 ? ?00041-0 ? 4078 ? ?case ? ? ? ? ? ? ? ?16.813141 ?89.7548225 1459 ? ?00041-0 ? 5005 ? ?case ? ? ? ? ? ? ? ?14.817248 131.0459201 1464 ? ?00041-0 ? 4816 ? ?case ? ? ? ? ? ? ? ?16.082135 131.1120569 1470 ? ?00041-0 ? 3495 ? ?case ? ? ? ? ? ? ? ?15.323750 118.7034216 1677 ? ?00041-0 ? 3991 ? ?case ? ? ? ? ? ? ? ? 9.782340 169.1765831 1845 ? ?00041-0 ? 4824 ? ?case ? ? ? ? ? ? ? ? 8.520191 122.3348897 1928 ? ?00041-0 ? 4599 ? ?case ? ? ? ? ? ? ? ?16.651608 ? 2.3051960 2142 ? ?00041-0 ? 3146 ? ?case ? ? ? ? ? ? ? ?15.164955 191.6823727 2184 ? ?00041-0 ? 3220 ? ?case ? ? ? ? ? ? ? ?15.526351 188.6667153 2283 ? ?00041-0 ? 5239 ? ?case ? ? ? ? ? ? ? ?18.269678 174.9620952 2542 ? ?00041-0 ? 3536 ? ?case ? ? ? ? ? ? ? ?10.910335 301.6238182 2883 ? ?00041-0 ? 4787 ? ?case ? ? ? ? ? ? ? ?13.527720 159.0956166 2921 ? ?00041-0 ? 4058 ? ?case ? ? ? ? ? ? ? ?17.845311 137.7700442 2932 ? ?00041-0 ? 5015 ? ?case ? ? ? ? ? ? ? ? 7.520876 101.6216590 3641 ? ?00041-0 ? 4266 ? ?case ? ? ? ? ? ? ? ? 5.497604 125.6831543 3683 ? ?00041-0 ? 4360 ? ?case ? ? ? ? ? ? ? ? 4.421629 187.2961108 3725 ? ?00041-0 ? 4428 ? ?case ? ? ? ? ? ? ? ?13.451060 157.4203222 3767 ? ?00041-0 ? 4506 ? ?case ? ? ? ? ? ? ? ? 6.507871 141.1115853 3889 ? ?00041-0 ? 4455 ? ?case ? ? ? ? ? ? ? ?12.147843 213.7489685 3 ? ? ? 00709-0 ? 5023 ? ?case ? ? ? ? ? ? ? ? 2.004106 412.4962003 398 ? ? 00709-0 ? 3180 ? ?case ? ? ? ? ? ? ? ? 5.234770 ? 9.6108457 639 ? ? 00709-0 ? 3205 ? ?case ? ? ? ? ? ? ? ? 4.974674 262.5951990 1058 ? ?00709-0 ? 3788 ? ?case ? ? ? ? ? ? ? ? 4.577686 281.6220685 1700 ? ?00709-0 ? 3450 ? ?case ? ? ? ? ? ? ? ? 4.156057 131.7354794 2050 ? ?00709-0 ? 3183 ? ?case ? ? ? ? ? ? ? ? 5.878165 ? 0.3707844 2483 ? ?00709-0 ? 4434 ? ?case ? ? ? ? ? ? ? ? 2.162902 339.0410965 2789 ? ?00709-0 ? 3849 ? ?case ? ? ? ? ? ? ? ? 4.405201 405.9685980 3281 ? ?00709-0 ? 3619 ? ?case ? ? ? ? ? ? ? ? 5.541409 255.1460421 3842 ? ?00709-0 ? 3943 ? ?case ? ? ? ? ? ? ? ? 3.515400 624.2636701 4011 ? ?00709-0 ? 5304 ? ?case ? ? ? ? ? ? ? ? 3.312799 ? 2.9206063 4 ? ? ? 00174-0 ? 4142 ? ?case ? ? ? ? ? ? ? ?16.060232 240.4858235 13 ? ? ?00174-0 ? 4244 ? ?case ? ? ? ? ? ? ? ? 5.615331 ? ? ? ? ?NA 22 ? ? ?00174-0 ? 4446 ? ?case ? ? ? ? ? ? ? ?15.523613 223.3748681 57 ? ? ?00174-0 ? 4292 ? ?case ? ? ? ? ? ? ? ? 6.209445 168.6679707 100 ? ? 00174-0 ? 4314 ? ?case ? ? ? ? ? ? ? ? 6.666666 147.6358300 183 ? ? 00174-0 ? 3689 ? ?case ? ? ? ? ? ? ? ?16.224503 ? 0.5689179 270 ? ? 00174-0 ? 4208 ? ?case ? ? ? ? ? ? ? ? 3.468856 286.9223725 313 ? ? 00174-0 ? 4223 ? ?case ? ? ? ? ? ? ? ? 4.427104 178.0005964 471 ? ? 00174-0 ? 3188 ? ?case ? ? ? ? ? ? ? ?11.633127 150.2065531 554 ? ? 00174-0 ? 3212 ? ?case ? ? ? ? ? ? ? ?12.377823 217.9799114 1304 ? ?00174-0 ? 5193 ? ?case ? ? ? ? ? ? ? ?14.694045 150.0169015 1463 ? ?00174-0 ? 4443 ? ?case ? ? ? ? ? ? ? ?13.626283 155.9339661 1589 ? ?00174-0 ? 5294 ? ?case ? ? ? ? ? ? ? ?13.990417 167.2262451 1884 ? ?00174-0 ? 4349 ? ?case ? ? ? ? ? ? ? ?15.271731 146.8486102 1975 ? ?00174-0 ? 3857 ? ?case ? ? ? ? ? ? ? ?10.611909 ?82.6978002 2084 ? ?00174-0 ? 3174 ? ?case ? ? ? ? ? ? ? ?10.924024 217.8106656 2196 ? ?00174-0 ? 5126 ? ?case ? ? ? ? ? ? ? ?16.892539 272.8141490 2748 ? ?00174-0 ? 4607 ? ?case ? ? ? ? ? ? ? ?16.481861 156.5503087 2963 ? ?00174-0 ? 4861 ? ?case ? ? ? ? ? ? ? ?11.967145 294.4989050 3294 ? ?00174-0 ? 4859 ? ?case ? ? ? ? ? ? ? ?12.783025 205.6801413 3566 ? ?00174-0 ? 4380 ? ?case ? ? ? ? ? ? ? ? 9.070499 ?84.6743348 3608 ? ?00174-0 ? 4405 ? ?case ? ? ? ? ? ? ? ?10.132785 ?92.1683089 3844 ? ?00174-0 ? 4252 ? ?case ? ? ? ? ? ? ? ?10.324435 305.3678248 3923 ? ?00174-0 ? 4193 ? ?case ? ? ? ? ? ? ? ?11.340177 336.7327758
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