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analysis_nutGR.Rmd
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analysis_nutGR.Rmd
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---
title: Nutrient Growth
author:
- M. Larsen^[Indiana University, Bloomington, IN]
abstract:
output:
pdf_document:
fig_caption: yes
keep_tex: no
latex_engine: xelatex
number_sections: yes
geometry: margin=1in
editor_options:
chunk_output_type: console
---
```{r setup}
# Setup Work Environment
rm(list=ls())
library(pander)
# Load Dependencies
source("./bin/modified_Gomp.r")
```
```{r gr-vis}
# read in data file
dat <- read.csv("./data/supplemental/NutrGR.csv", header = T)
p <- ggplot(dat,aes(med.add,GR)) +
facet_grid(~media.base) +
geom_point(aes(color= med.add), size=3) +
geom_boxplot(aes(fill=med.add), alpha=I(0.75)) +
scale_color_grey(start = 0.2, end = 0.8) +
scale_fill_grey(start = 0, end = 1)+
theme_bw()+
theme(legend.position='none',
axis.text.x = element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.x = element_text(colour="black", size=12,
margin = margin(20,20,10,10)),
axis.title.y = element_text(colour="black", size=12,
margin = margin(20,20,10,10)))+
labs(x ="Resource Addition",
#y = expression(~ Delta ~"Growth Rate (day^ -1)"))
y = "Growth Rate")
p$data$med.add <- factor(p$data$med.add, levels = c("C","P","N","NP"))
print(p)
ggsave("fig_nutrGR.pdf", plot = p, device = "pdf", path = "./supporting-files/chpt-figs", scale = 1, width = 6, height = 3.5, units = "in", dpi = 1200)
# calculate means for each group
dat.ag <- aggregate(GR ~ media.base + med.add, dat, function(x) c(mean = mean(x), sd = sd(x)))
write.csv(dat.ag, file = "./supporting-files/chpt-tables/table_nutrGR.csv",row.names = FALSE)
#set.caption("Resource Growth Rate Aggregate Data")
#pander(dat.ag, justify = "center")
```
\newpage
### Growth rate ANOVA tables
**N-limited**
```{r gr-statsN}
fitN = aov(GR ~ med.add, dat[dat$media.base == "NL",])
sum.fitN = summary(fitN)
tukey.fitN = TukeyHSD(fitN)
round(tukey.fit$med.add, digits = 3)
set.caption("ANOVA table for NL nutrient addition")
pander(aov(GR ~ med.add, dat[dat$media.base == "NL",]),table.split.table = inf)
set.caption("Posthoc comparisons using Tukey HSD")
#emphasize.strong.rows(which(tukey.fitN$med.add[,4] < 0.05, arr.ind = TRUE))
pander(tukey.fitN$med.add[,1:4],table.split.table = inf)
write.csv(file = "./supporting-files/data/NL-nutrGRstats.csv",tukey.fitN)
#kable(tukey.fitN$med.add[,1:4], format = "latex", digits = 3,
# caption = "ANOVA table for N-limited treatments with Tukey post-hoc comparisons")
```
**P-limited**
```{r gr-statsP}
fitP = aov(GR ~ med.add, dat[dat$media.base == "PL",])
sum.fitP = summary(fitP)
tukey.fitP = TukeyHSD(fitP)
#round(tukey.fit$med.add, digits = 3)
set.caption("ANOVA table for PL nutrient addition")
pander(aov(GR ~ med.add, dat[dat$media.base == "PL",]),table.split.table = inf)
set.caption("Posthoc comparisons using Tukey HSD")
emphasize.strong.rows(which(tukey.fitP$med.add[,4] < 0.05, arr.ind = TRUE))
pander(tukey.fitP$med.add[,1:4],table.split.table = inf)
#kable(tukey.fitP$med.add[,1:4], format = "latex", digits = 3,
# caption = "ANOVA table for P-limited treatments with Tukey post-hoc comparisons")
```
\newpage
## Percent Change in Growth
```{r perc.change}
media <- c(); trt <- c()
avgGR <- c(); stdGR <- c()
delta <- c(); perc <- c()
for(i in unique(dat[,1])){
for(j in unique(dat[,2])){
for(k in unique(dat[,3])){
tmp <- dat[,5][dat[,1]==i&dat[,2]==j&dat[,3]==k]
ctrl <- mean(dat[,5][dat[,1]==i&dat[,2]=="C"])
avg <- mean(tmp) #average replicate value
d <- avg-ctrl #subtract mean control from replicate average
sdm <- sd(tmp)
perc.change <- ((avg-ctrl)/ctrl)*100
avgGR <- append(avgGR,avg)
stdGR <- append(stdGR,sdm)
media <- append(media,i)
trt <- append(trt,j)
delta <- append(delta,d)
perc <- append(perc, perc.change)
}
}
}
results = data.frame(media,trt,avgGR,stdGR,delta, perc)
colnames(results) = c("med.base","med.add","mean.GR","std.GR", "delta.GR","perc.change")
results = results[results$med.add != "C" & perc >= 0,]
p2 <- ggplot(results,aes(med.add,perc.change)) + facet_grid(~med.base) +
geom_point(aes(color=med.add), size=3) +
geom_boxplot(aes(fill=med.add), alpha=I(0.5)) +
scale_color_grey(start = 0.2, end = 0.8) +
scale_fill_grey(start = 0, end = 1)+
theme_bw()+
theme(legend.position='none',
axis.text.x = element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.x = element_text(colour="black", size=12,
margin = margin(20,20,10,10)),
axis.title.y = element_text(colour="black", size=12,
margin = margin(20,20,10,10)))+
labs(x ="Resource Addition",
y = "Percent Change")
p2$data$med.add = factor(p2$data$med.add, levels = c("P","N","NP"))
print(p2)
ggsave("fig_perc.change.pdf", plot = p2, device = "pdf", path = "./supporting-files/chpt-figs", scale = 1, width = 6, height = 3.5, units = "in", dpi = 1200)
# calculate means for each group
results.ag <- aggregate(. ~ med.add + med.base, results,
function(x) c(mean = mean(x),sd = sd(x)))
results.ag[,3:5] = round(results.ag[,3:5], digits = 3)
set.caption("Resource Percent Change in Growth Aggregate Data")
pander(results.ag, justify = "center",table.split.table = inf)
write.csv(results.ag, file = "./supporting-files/chpt-tables/table_perchange.csv",row.names = FALSE)
```
\newpage
### Growth rate ANOVA tables
**N-limited**
```{r perc.change-statsN}
fitN = aov(perc.change ~ med.add, results[results$med.base == "NL",])
sum.fitN = summary(fitN)
tukey.fitN = TukeyHSD(fitN)
#round(tukey.fit$med.add, digits = 3)
set.caption("ANOVA table for NL nutrient addition")
pander(fitN,table.split.table = inf)
set.caption("Posthoc comparisons using Tukey HSD")
#emphasize.strong.rows(which(tukey.fitN$med.add[,4] < 0.05, arr.ind = TRUE))
pander(tukey.fitN$med.add,table.split.table = inf)
#kable(tukey.fitN$med.add[,1:4], format = "latex", digits = 3,
# caption = "ANOVA table for N-limited percent change treatments with Tukey post-hoc comparisons")
```
**P-limited**
```{r perc.change-statsP}
fitP = aov(perc.change ~ med.add, results[results$med.base == "PL",])
sum.fitP = summary(fitP)
tukey.fitP = TukeyHSD(fitP)
#round(tukey.fit$med.add, digits = 3)
set.caption("ANOVA table for PL nutrient addition")
pander(fitP,table.split.table = inf)
set.caption("Posthoc comparisons using Tukey HSD")
#emphasize.strong.rows(which(tukey.fitP$med.add[,4] < 0.05, arr.ind = TRUE))
pander(tukey.fitP$med.add,table.split.table = inf)
#kable(tukey.fitP$med.add[,1:4], format = "latex", digits = 3,
# caption = "ANOVA table for P-limited percent change treatments with Tukey post-hoc comparisons")
```
```{r}
dat.perc <- read.csv("./data/supplemental/20130125_percchange_2.csv", header = T)
dataN <- dat.perc[dat.perc$med.base=="NL", ]
dataP <- dat.perc[dat.perc$med.base=="PL", ]
t.test(dataN[1:5, 3], dataN[6:10, 3], alternative="greater", conf.level = 0.95, var.equal=TRUE)
t.test(dataP[1:5, 3], dataP[6:10, 3], alternative="less", conf.level = 0.95, var.equal=TRUE)
```
```{r, echo=FALSE, eval = F}
#Percent Change in GR
boxplot(dataN$perc.change~dataN$med.add, col=c("grey66", "white"),
cex.axis=1.5, las=1,
xlab = "t = 4.5459, df = 8, p-value = 0.0009423",
ylim = c(0, 100), ylab = list(cex = 1.5, "Percent Change"))
legend("topright", c("N-Limited", "P-limited"), pch=c(15, 22), col=c("grey66", "black")
, bty='n', cex=1.5)
boxplot(dataP$perc.change~dataP$med.add,
col=c("grey66", "white"), cex.axis=1.5, las=1,
xlab = "t = -3.3615, df = 8, p-value = 0.004955",
ylim = c(0, 100), ylab = list(cex = 1.5, "Percent Change"))
legend("topright", c("N-Limited", "P-limited"), pch=c(15, 22), col=c("grey66", "black")
, bty='n', cex=1.5)
```
```{r, fig.width = 4, fig.height = 4, echo=FALSE, fig.cap="Fig S1: Percent change in growth"}
# Calculate means
perc.means <- aggregate(dat.perc[,3],list(dat.perc$med.base, dat.perc$med.add), mean)
perc.means$plot <- c(1,2,3,4)
# Calculate SEM
# Define functions for calculations
sem <- function(x){
sd(x)/sqrt(length(x))
}
perc.sem <- aggregate(dat.perc[,3],list(dat.perc$med.base, dat.perc$med.add), sem)
#pdf(file = "./chpt-figs/fig-pub_perchange.pdf",width=5,height=5,pointsize = 12, family ="sans")
#png(filename = "./pub-figures/fig1-perchange.png",
# width = 570, height = 570, units = "px", pointsize = 17,
# bg = "white", family = "sans", type = "cairo")
par(mar = c(5,5,2,1) + 0.1, mfrow = c(1,1))
perc.plot<- plot(perc.means[,3] ~ perc.means[,4],
xaxt = 'n', las = 1,
xlim = c(0.5,4.5),ylim = c(0,100),
xlab = list("N-limited P-limited", cex = 1.5, font = 2) ,
ylab = list("Change in Growth (%)", cex = 1.5, font = 2),
pch = c(21,21,22,22),col = "black", bg = c("black","white","black","white"),
cex = 2, lwd = 2, cex.axis = 1.25)
arrows(perc.means[,4],perc.means[,3]-perc.sem[,3],
perc.means[,4],perc.means[,3]+perc.sem[,3],
code = 0, lwd = 3, col = "black")
points(perc.means[,3] ~ perc.means[,4],
pch = c(21,21,22,22),cex = 3, lwd = 3,
col = "black", bg = c("black","white","black","white"))
box(lwd=3)
#axis.break(1, 2.5, style = "gap")
abline(v = 2.5, lwd = 1, lty = 1, col = "black")
axis(1, at = c(1,2,3,4), labels = c("+N","+P","+N","+P"), cex.axis = 1.25)
#text(1.5,10,"N-limited", cex = 1.25, font = 2)
#text(1.5,3,"N:P = 10:1", cex = 1.15, font = 1)
#text(3.5,10,"P-limited", cex = 1.25, font = 2)
#text(3.5,3,"N:P = 40:1", cex = 1.15, font = 1)
dev.off()
```