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RMarkdown_MaterialProperties.Rmd
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RMarkdown_MaterialProperties.Rmd
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---
title: "Nanoindentation"
author: "Kaitlyn Lowder"
date: '2022-04-20'
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
require(plyr) #for ddplyr function for graph summary stats
require(tidyverse) #for count function
library(ggsignif) #for figure with lines of significance crossing trts
require(gridExtra) # for putting 4 plots together
require(cowplot) # for putting 4 plots together
require(ggpubr) #for ggannotate
```
#Import data
```{r}
nano<-read.csv("Data_MaterialProperties.csv")
head(nano)
attach(nano)
names(nano)
```
#Hardness
##Data cleaning
```{r}
boxplot(Hardness~Treatment, data=nano) #can see many outliers, all on the high end
abline(h = .80, col = "red")
##Dropping measurements that are outside interquartile ranges (outliers)/ outside the Tukey fence
hard<-aggregate(Hardness ~ Animal + Region + Molted. + Treatment , subset(nano, HIQR=="yes"), FUN = "mean")
#Separating into regions and molt status
HardDspUnmolted<-subset(subset(hard, Region=="Dsp"), Molted.=="No")
HardHornUnmolted<-subset(subset(hard, Region=="Horn"), Molted.=="No")
```
## Horn spine stats
```{r}
boxplot(Hardness~Treatment, data=HardHornUnmolted)
temporary<-HardHornUnmolted %>%
filter(Molted. == "No")%>%
select(Region, Treatment, Hardness) %>%
group_by(Region, Treatment) %>%
summarise_all(.funs=c(mean="mean", sd="sd"))
str(as.numeric(HardHornUnmolted$Hardness))
shapiro.test(subset(HardHornUnmolted,Treatment=="A")$Hardness)
shapiro.test(subset(HardHornUnmolted,Treatment=="B")$Hardness)
shapiro.test(subset(HardHornUnmolted,Treatment=="C")$Hardness)
shapiro.test(subset(HardHornUnmolted,Treatment=="D")$Hardness)
bartlett.test(HardHornUnmolted$Hardness~HardHornUnmolted$Treatment)
summary(aov(HardHornUnmolted$Hardness~HardHornUnmolted$Treatment))
TukeyHSD(aov(HardHornUnmolted$Hardness~HardHornUnmolted$Treatment))
```
##Dorsal spine stats
```{r}
boxplot(Hardness~Treatment, data=HardDspUnmolted)
shapiro.test(subset(HardDspUnmolted,Treatment=="A")$Hardness)
shapiro.test(subset(HardDspUnmolted,Treatment=="B")$Hardness)
shapiro.test(subset(HardDspUnmolted,Treatment=="C")$Hardness)
shapiro.test(subset(HardDspUnmolted,Treatment=="D")$Hardness)
bartlett.test(HardDspUnmolted$Hardness~HardDspUnmolted$Treatment)
kruskal.test(HardDspUnmolted$Hardness~HardDspUnmolted$Treatment)
summary(aov(HardDspUnmolted$Hardness~HardDspUnmolted$Treatment))
```
#Stiffness
```{r}
#Dropping measurements that are outside interquartile ranges (outliers)/ outside the Tukey fence
stiff<-aggregate(Stiffness ~ Animal + Region + Molted. + Treatment , subset(nano, SIQR=="yes"), FUN = "mean")
#Separating into regions and molt status
StiffDspUnmolted<-subset(subset(stiff, Region=="Dsp"), Molted.=="No")
StiffHornUnmolted<-subset(subset(stiff, Region=="Horn"), Molted.=="No")
```
##Horn spine stats
```{r}
boxplot(Stiffness~Treatment, data=StiffHornUnmolted) #looks great except for that weird horn tip
shapiro.test(subset(StiffHornUnmolted,Treatment=="A")$Stiffness)
shapiro.test(subset(StiffHornUnmolted,Treatment=="B")$Stiffness)
shapiro.test(subset(StiffHornUnmolted,Treatment=="C")$Stiffness)
shapiro.test(subset(StiffHornUnmolted,Treatment=="D")$Stiffness)
bartlett.test(StiffHornUnmolted$Stiffness~StiffHornUnmolted$Treatment)
summary(aov(StiffHornUnmolted$Stiffness~StiffHornUnmolted$Treatment))
#global mean
aggregate(Stiffness ~ Region + Molted. , StiffHornUnmolted, FUN = "mean")
aggregate(Stiffness ~ Region + Molted. , StiffHornUnmolted, FUN = "sd")
aggregate(Stiffness ~ Region + Molted. + Treatment , StiffHornUnmolted, FUN = "mean")
```
##Dorsal spine stats
```{r}
boxplot(Stiffness~Treatment, data=StiffDspUnmolted)
shapiro.test(subset(StiffDspUnmolted,Treatment=="A")$Stiffness)
shapiro.test(subset(StiffDspUnmolted,Treatment=="B")$Stiffness)
shapiro.test(subset(StiffDspUnmolted,Treatment=="C")$Stiffness) #
shapiro.test(subset(StiffDspUnmolted,Treatment=="D")$Stiffness) #
bartlett.test(StiffDspUnmolted$Stiffness~StiffDspUnmolted$Treatment)
summary(aov(StiffDspUnmolted$Stiffness~StiffDspUnmolted$Treatment))
```
#Graphing
```{r}
hard %>%
filter(Molted. == "No")%>%
select(Region, Treatment, Hardness) %>%
group_by(Region, Treatment) %>%
summarise_all(.funs=c(mean="mean", sd="sd"))
hardness_stats<-ddply(hard,~Treatment + Region,summarise,mean=mean(Hardness),sd=sd(Hardness)) #computes the mean thickness per treatment
hardness_stats <- hardness_stats %>% #This is changing the names from my defaults to what I want on the graphs
mutate(Region = fct_recode(Region,"Carapace spine" = "Dsp"))
hardness_stats <- hardness_stats %>% #This is changing the names from my defaults to what I want on the graphs
mutate(Treatment = fct_recode(Treatment,"7.97" = "A", "7.67" = "B", "7.67 ±0.10" = "D", "7.67 ±0.05" = "C"))
levels(hardness_stats$Treatment)<-gsub(" ", "\n",levels(hardness_stats$Treatment))
hardness_stats_dsp<-subset(hardness_stats, Region=="Carapace spine")
HardDspUnmolted %>% #How many replicates I have of each region for my graphing
select(Region, Treatment, Hardness) %>%
group_by(Treatment)%>%
dplyr::count()
harddsp<-ggplot(hardness_stats_dsp, aes(x=Treatment, y=mean, fill=Treatment)) +
theme_bw() +
geom_bar(stat="identity", position=position_dodge(1)) +
geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd), width=.2,position=position_dodge(0.9)) +
scale_fill_manual(values=c("#c92f20", "#870362", "#4492ba", "#003b14")) +
scale_y_continuous(name="Hardness (GPa)", breaks =seq(0,0.7,0.1)) +
scale_x_discrete(name="pH") +
expand_limits(y = c(0, 0.55)) +
theme(legend.position="none",
legend.title=element_blank(), panel.background = element_rect(fill='transparent'),
panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.border =element_rect(fill=NA, colour='black'),
plot.title = element_text(size = 20, hjust= 0.5),
axis.title.y = element_text(color="black", size=20),
axis.text.x = element_text(color="black", size=17), #x axis treatment labels
axis.text.y = element_text(color="black", size=17), #y axis number labels
axis.title.x = element_blank()) +
geom_hline(yintercept = 0) +
ggtitle("Carapace") +
annotate(geom="text", x=0.65, y=.65, label="A", color="black", size=8) +
annotate(geom="text", x=1, y=-0.02, label="10", color="black", size=5) +
annotate(geom="text", x=2, y=-0.02, label="11", color="black", size=5) +
annotate(geom="text", x=3, y=-0.02, label="9", color="black", size=5) +
annotate(geom="text", x=4, y=-0.02, label="11", color="black", size=5)
hardness_stats_horn<-subset(hardness_stats, Region=="Horn")
HardHornUnmolted%>% #How many replicates I have of each region for my graphing
group_by(Treatment) %>%
dplyr::count()
hardhorn<-ggplot(hardness_stats_horn, aes(x=Treatment, y=mean, fill=Treatment)) +
theme_bw() +
geom_bar(stat="identity", position=position_dodge(1)) +
geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd), width=.2,position=position_dodge(0.9)) +
scale_fill_manual(values=c("#f97537", "#ed3d83", "#17c9b7", "#0f6110")) +
scale_y_continuous(name="Hardness (GPa)", breaks =seq(0,0.7,0.1)) +
scale_x_discrete(name="pH") +
expand_limits(y = c(0, 0.55)) +
theme(legend.position="none",
legend.title=element_blank(), panel.background = element_rect(fill='transparent'),
panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.border =element_rect(fill=NA, colour='black'),
plot.title = element_text(hjust = 0.5, size= 20),
axis.title.y = element_blank(),
axis.text.x = element_text(color="black", size=17), #x axis treatment labels
axis.text.y = element_text(color="black", size=17), #y axis number labels
axis.title.x = element_blank()) +
geom_hline(yintercept = 0) +
annotate(geom="text", x=0.65, y=.65, label="B", color="black", size=8) +
ggtitle("Horn") +
annotate(geom="text", x=1, y=-0.02, label="10", color="black", size=5) +
annotate(geom="text", x=2, y=-0.02, label="10", color="black", size=5) +
annotate(geom="text", x=3, y=-0.02, label="9", color="black", size=5) +
annotate(geom="text", x=4, y=-0.02, label="11", color="black", size=5) +
geom_signif(annotations=c("0.004"), y_position=.50, xmin=2, xmax=3, tip_length = c(0.01, 0.01)) +
geom_signif(annotations=c("0.045"), y_position=.55, xmin=1, xmax=4, tip_length = c(0.01, 0.01)) +
geom_signif(annotations=c("0.003"), y_position=.45, xmin=2, xmax=4, tip_length = c(0.01, 0.01))
```