Today I'm presenting my research at the Western Society of Naturalist Conference in Tacoma. I will be live tweeting during the event both before and after my talk. Follow my twitter @HeareBraindIdea for live updates.
Friday, November 14, 2014
Wednesday, November 12, 2014
11 12 2014 WSN Practice Talk
Tuesday, November 11, 2014
11 11 2014 NEW Size Distribution Graphs with STATS!
y1sizedist.R
Jake H
Tue Nov 11 13:02:23 2014
require(ggplot2)## Loading required package: ggplot2require(plyr)## Loading required package: plyrrequire(splitstackshape)## Loading required package: splitstackshape
## Loading required package: data.tabley1size=read.csv('Y1size.csv')
#creates dataframe and reads in the CSV file for sizes
View(y1size)
#check data
y1size$Date<-as.Date(y1size$Date, "%m/%d/%Y")
#make R understand dates
y1meansize<-ddply(y1size,.(Date,Site,Pop),summarise, mean_size=mean(Length.mm,na.rm=T))
#create table of ave size for outplant and year one for each pop at each site
#print it out
print(y1meansize)## Date Site Pop mean_size
## 1 2013-08-16 Fidalgo 2H 10.67
## 2 2013-08-16 Fidalgo 2N 11.60
## 3 2013-08-16 Fidalgo 2S 11.25
## 4 2013-08-16 Manchester 4H 10.53
## 5 2013-08-16 Manchester 4N 13.40
## 6 2013-08-16 Manchester 4S 11.30
## 7 2013-08-16 Oyster Bay 1H 10.49
## 8 2013-08-16 Oyster Bay 1N 10.90
## 9 2013-08-16 Oyster Bay 1S 12.15
## 10 2014-09-19 Oyster Bay 1H 27.96
## 11 2014-09-19 Oyster Bay 1N 35.81
## 12 2014-09-19 Oyster Bay 1S 27.98
## 13 2014-10-17 Fidalgo 2H 24.40
## 14 2014-10-17 Fidalgo 2N 29.10
## 15 2014-10-17 Fidalgo 2S 28.91
## 16 2014-10-24 Manchester 4H 21.49
## 17 2014-10-24 Manchester 4N 24.37
## 18 2014-10-24 Manchester 4S 23.99#now we need to create subsets for each site for out plant and end of year 1
outmany1<-ddply(y1size,.(Length.mm,Pop,Tray,Sample,Area),subset,Date=="2013-08-16"&Site=="Manchester")
outfidy1<-ddply(y1size,.(Length.mm,Pop,Tray,Sample,Area),subset,Date=="2013-08-16"&Site=="Fidalgo")
outoysy1<-ddply(y1size,.(Length.mm,Pop,Tray,Sample,Area),subset,Date=="2013-08-16"&Site=="Oyster Bay")
endmany1<-ddply(y1size,.(Length.mm,Pop,Tray,Sample,Area),subset,Date=="2014-10-24"&Site=="Manchester")
endfidy1<-ddply(y1size,.(Length.mm,Pop,Tray,Sample,Area),subset,Date=="2014-10-17"&Site=="Fidalgo")
endoysy1<-ddply(y1size,.(Length.mm,Pop,Tray,Sample,Area),subset,Date=="2014-09-19"&Site=="Oyster Bay")
#Plot the distributions of the sizes for each pop at each site for outplant and end of year 1.
#This allows us to visualize differences in populations.
ggplot()+
geom_density(data=outmany1,aes(x=Length.mm,group=Pop,colour=Pop,fill=Pop),alpha=0.3)+
geom_density(data=endmany1,aes(x=Length.mm,group=Pop,colour=Pop,fill=Pop),alpha=0.3)+
scale_colour_manual(values=c("blue","purple","orange"))+
scale_fill_manual(values=c("blue","purple","orange"))+
ggtitle("Size Comparison\nAugust 2013 vs. October 2014")
ggplot()+
geom_density(data=outfidy1,aes(x=Length.mm,group=Pop,colour=Pop,fill=Pop),alpha=0.3)+
geom_density(data=endfidy1,aes(x=Length.mm,group=Pop,colour=Pop,fill=Pop),alpha=0.3)+
scale_colour_manual(values=c("blue","purple","orange"))+
scale_fill_manual(values=c("blue","purple","orange"))+
ggtitle("Size Comparison\nAugust 2013 vs. October 2014")
ggplot()+
geom_density(data=outoysy1,aes(x=Length.mm,group=Pop,colour=Pop,fill=Pop),alpha=0.3)+
geom_density(data=endoysy1,aes(x=Length.mm,group=Pop,colour=Pop,fill=Pop),alpha=0.3)+
scale_colour_manual(values=c("blue","purple","orange"))+
scale_fill_manual(values=c("blue","purple","orange"))+
ggtitle("Size Comparison\nAugust 2013 vs. September 2014")
#Now we need to do some anovas as we assume the data is normally distributed.
#First we have to create a column of pop labels that don't have the site designation in them
y1size$Pop2<-y1size$Pop
y1size$Pop2<-revalue(y1size$Pop2,c("1H"="H","2H"="H","4H"="H","1N"="N","2N"="N","4N"="N","1S"="S","2S"="S","4S"="S"))
#Here we subset the data set to only include data from the end of year 1
endy1<-ddply(y1size,.(Length.mm,Site,Pop,Tray,Sample,Area,Pop2),subset,Date>="2014-09-19")
#Run the ANOVA comparing site,pop, and site:pop to show significant differences
sizeaov<-aov(endy1$Length.mm~endy1$Site+endy1$Pop2+endy1$Site:endy1$Pop2,endy1)
summary(sizeaov)## Df Sum Sq Mean Sq F value Pr(>F)
## endy1$Site 2 14168 7084 263.5 <2e-16 ***
## endy1$Pop2 2 8254 4127 153.5 <2e-16 ***
## endy1$Site:endy1$Pop2 4 3368 842 31.3 <2e-16 ***
## Residuals 2283 61368 27
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Everything is significantly different. YAY!
#Time to do a Tukey test to illucidate what is different from what exactly.
sizesvptukey<-TukeyHSD(sizeaov)
print(sizesvptukey)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = endy1$Length.mm ~ endy1$Site + endy1$Pop2 + endy1$Site:endy1$Pop2, data = endy1)
##
## $`endy1$Site`
## diff lwr upr p adj
## Manchester-Fidalgo -4.281 -4.862 -3.701 0
## Oyster Bay-Fidalgo 2.451 1.772 3.129 0
## Oyster Bay-Manchester 6.732 6.000 7.463 0
##
## $`endy1$Pop2`
## diff lwr upr p adj
## N-H 4.545 3.922 5.1683 0
## S-H 2.975 2.358 3.5911 0
## S-N -1.571 -2.198 -0.9432 0
##
## $`endy1$Site:endy1$Pop2`
## diff lwr upr p adj
## Manchester:H-Fidalgo:H -2.90887 -4.2476 -1.5702 0.0000
## Oyster Bay:H-Fidalgo:H 3.55552 2.1100 5.0010 0.0000
## Fidalgo:N-Fidalgo:H 4.69502 3.5223 5.8677 0.0000
## Manchester:N-Fidalgo:H -0.02951 -1.4073 1.3483 1.0000
## Oyster Bay:N-Fidalgo:H 11.40509 9.6848 13.1254 0.0000
## Fidalgo:S-Fidalgo:H 4.50490 3.3091 5.7007 0.0000
## Manchester:S-Fidalgo:H -0.41572 -1.7355 0.9041 0.9879
## Oyster Bay:S-Fidalgo:H 3.58281 1.9898 5.1758 0.0000
## Oyster Bay:H-Manchester:H 6.46439 4.9055 8.0232 0.0000
## Fidalgo:N-Manchester:H 7.60389 6.2941 8.9137 0.0000
## Manchester:N-Manchester:H 2.87936 1.3831 4.3756 0.0000
## Oyster Bay:N-Manchester:H 14.31397 12.4974 16.1306 0.0000
## Fidalgo:S-Manchester:H 7.41377 6.0832 8.7443 0.0000
## Manchester:S-Manchester:H 2.49316 1.0501 3.9362 0.0000
## Oyster Bay:S-Manchester:H 6.49168 4.7951 8.1882 0.0000
## Fidalgo:N-Oyster Bay:H 1.13950 -0.2793 2.5583 0.2355
## Manchester:N-Oyster Bay:H -3.58503 -5.1776 -1.9925 0.0000
## Oyster Bay:N-Oyster Bay:H 7.84957 5.9529 9.7463 0.0000
## Fidalgo:S-Oyster Bay:H 0.94938 -0.4886 2.3874 0.5085
## Manchester:S-Oyster Bay:H -3.97124 -5.5139 -2.4286 0.0000
## Oyster Bay:S-Oyster Bay:H 0.02729 -1.7548 1.8093 1.0000
## Manchester:N-Fidalgo:N -4.72453 -6.0743 -3.3748 0.0000
## Oyster Bay:N-Fidalgo:N 6.71007 5.0121 8.4080 0.0000
## Fidalgo:S-Fidalgo:N -0.19012 -1.3535 0.9733 0.9999
## Manchester:S-Fidalgo:N -5.11074 -6.4013 -3.8202 0.0000
## Oyster Bay:S-Fidalgo:N -1.11221 -2.6811 0.4567 0.4052
## Oyster Bay:N-Manchester:N 11.43460 9.5890 13.2802 0.0000
## Fidalgo:S-Manchester:N 4.53441 3.1645 5.9043 0.0000
## Manchester:S-Manchester:N -0.38621 -1.8656 1.0932 0.9966
## Oyster Bay:S-Manchester:N 3.61232 1.8848 5.3399 0.0000
## Fidalgo:S-Oyster Bay:N -6.90020 -8.6142 -5.1862 0.0000
## Manchester:S-Oyster Bay:N -11.82081 -13.6235 -10.0181 0.0000
## Oyster Bay:S-Oyster Bay:N -7.82228 -9.8337 -5.8109 0.0000
## Manchester:S-Fidalgo:S -4.92061 -6.2322 -3.6090 0.0000
## Oyster Bay:S-Fidalgo:S -0.92209 -2.5083 0.6641 0.6791
## Oyster Bay:S-Manchester:S 3.99853 2.3168 5.6802 0.0000#WOW That's a lot of differences. It would be easier to say what isn't significantly different.
#Manchester Fid Pop is not sig diff from Fidalgo Dabob pop
#Manchester Oys pop is not sig diff from Fidalgo Dabob pop
#Fidalgo Fid pop is not sig diff from Oyster Bay Dabob pop
#Fidalgo Oys pop is not sig diff from Oyster Bay Dabob pop
#Oyster Bay Oys pop is essentially equivalent to Oyster Bay Dabob pop
#Fidalgo Oys pop is not sig diff from Fidalgo Fid pop
#Oyster Bay Oys pop is not sig diff from Fidalgo Fid pop
#Manchester Oys pop is not sig diff from Manchester Fid pop
#Oyster Bay Oys pop is not sig diff from Fidalgo Oys Pop
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