require(ggplot2)
## Loading required package: ggplot2
require(scales)
## Loading required package: scales
require(plyr)
## Loading required package: plyr
require(splitstackshape)
## Loading required package: splitstackshape
## Loading required package: data.table
picsize7=read.csv('imagejsizefixed1.csv')
picsize8<-concat.split.multiple(picsize7, "Tray", " ")
mansize<-subset(picsize8, Site %in% c("Manchester","manchester"))
mansize$Date<-as.Date(mansize$Date, "%m/%d/%Y")
ggplot(mansize, aes(Date,Length.mm, group=Tray_1, color=Tray_1))+
geom_point()+geom_smooth(method=lm)+
scale_colour_manual(values=c("#3366CC","#CC66CC","#FF9900"))
![plot of chunk unnamed-chunk-1 plot of chunk 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)
mods<-dlply(mansize,.(Tray_1),function(x)lm(Length.mm~Date,data=x))
slope<-function(x)coef(x)[2]
pval<-function(x)anova(x)$'Pr(>F)'[1]
s1<-ldply(mods,slope)
s2<-ldply(mods,pval)
ds_outman<-join(s1,s2)
## Joining by: Tray_1
names(ds_outman)[3]<-"pvalue"
ds_outman$slab<-sprintf('Slope:%7.5f',ds_outman$Date)
ds_outman$plab<-sprintf('p-value:%7.5f',ds_outman$pvalue)
print(ds_outman)
## Tray_1 Date pvalue slab plab
## 1 4H 0.03183 8.371e-53 Slope:0.03183 p-value:0.00000
## 2 4N 0.03443 4.414e-34 Slope:0.03443 p-value:0.00000
## 3 4S 0.03942 1.914e-77 Slope:0.03942 p-value:0.00000
mods<-dlply(oyssize,.(Tray_1),function(x)lm(Length.mm~Date,data=x))
slope<-function(x)coef(x)[2]
pval<-function(x)anova(x)$'Pr(>F)'[1]
s1<-ldply(mods,slope)
s2<-ldply(mods,pval)
ds_outoys<-join(s1,s2)
## Joining by: Tray_1
names(ds_outoys)[3]<-"pvalue"
ds_outoys$slab<-sprintf('Slope:%7.5f',ds_outoys$Date)
ds_outoys$plab<-sprintf('p-value:%7.5f',ds_outoys$pvalue)
print(ds_outoys)
## Tray_1 Date5/1/2014 pvalue slab plab
## 1 1H 1.545 8.119e-68 Slope:1.54481 p-value:0.00000
## 2 1N 2.025 3.019e-92 Slope:2.02485 p-value:0.00000
## 3 1S 1.614 1.270e-27 Slope:1.61403 p-value:0.00000
mods<-dlply(fidsize,.(Tray_1),function(x)lm(Length.mm~Date,data=x))
slope<-function(x)coef(x)[2]
pval<-function(x)anova(x)$'Pr(>F)'[1]
s1<-ldply(mods,slope)
s2<-ldply(mods,pval)
ds_outfid<-join(s1,s2)
## Joining by: Tray_1
names(ds_outfid)[3]<-"pvalue"
ds_outfid$slab<-sprintf('Slope:%7.5f',ds_outfid$Date)
ds_outfid$plab<-sprintf('p-value:%7.5f',ds_outfid$pvalue)
print(ds_outfid)
## Tray_1 Date5/2/2014 pvalue slab plab
## 1 2H -0.03446 1.450e-114 Slope:-0.03446 p-value:0.00000
## 2 2N -0.32543 5.246e-75 Slope:-0.32543 p-value:0.00000
## 3 2S 1.44133 1.861e-92 Slope:1.44133 p-value:0.00000