Load the libraries
Get the data
read_csv("RootingWindow_RawData.csv")%>%
tbl_df()->
dat
dat%>%
select(-DegreeOfBranching,-NoOfRootingPts)->
dat
Stem width vs stem height
dat%>%
mutate(Plant="ALL")%>%
bind_rows(dat)%>%
filter(StemWidth_mm!=max(StemWidth_mm))%>%
ggplot(aes(StemWidth_mm,Height_mm))+
geom_point()+
geom_smooth(method="lm")+
stat_poly_line(se=FALSE) +
stat_poly_eq(aes(label = paste(after_stat(eq.label),
after_stat(rr.label), sep = "*\", \"*")))+
facet_wrap(~Plant)+
ylab("Height (mm)")+xlab("Stem diameter (mm)")
Stem width at start of experiment vs above ground biomass at
end
dat%>%
mutate(Plant="ALL")%>%
bind_rows(dat)%>%
filter(StemWidth_mm!=max(StemWidth_mm))%>%
ggplot(aes(StemWidth_mm,AboveGroundMass_g))+
geom_point()+
geom_smooth(method="lm")+
stat_poly_line(se=FALSE) +
stat_poly_eq(aes(label = paste(after_stat(eq.label),
after_stat(rr.label), sep = "*\", \"*")))+
facet_wrap(~Plant)+
ylab("Above ground biomass at end of experiment")+xlab("Stem diameter (mm)")
ggsave(paste("ScatterPlot_Width_Mass_",format(Sys.time(),"%Y%m%d-%Hh%Mm"),".jpg",sep=""),width=8,height=8)
Stem Height at start of experiment vs above ground biomass at
end
dat%>%
mutate(Plant="ALL")%>%
bind_rows(dat)%>%
filter(StemWidth_mm!=max(StemWidth_mm))%>%
ggplot(aes(Height_mm,AboveGroundMass_g))+
geom_point()+
geom_smooth(method="lm")+
stat_poly_line(se=FALSE) +
stat_poly_eq(aes(label = paste(after_stat(eq.label),
after_stat(rr.label), sep = "*\", \"*")))+
facet_wrap(~Plant)+
ylab("Above ground biomass at end of experiment")+xlab("Height (mm)")
ggsave(paste("ScatterPlot_Height_Mass_",format(Sys.time(),"%Y%m%d-%Hh%Mm"),".jpg",sep=""),width=8,height=8)
Figure: Boxplot of StemWidth across Plants with post-hoc Tukey
dissimilarity letters
dat%>%
lm(StemWidth_mm~Plant,data=.)%>%
aov()->anova
anova%>%
TukeyHSD()->tukey
cld <- multcompLetters4(anova, tukey)
print(cld)
dat%>%
group_by(Plant)%>%
summarise(max=max(StemWidth_mm))%>%
#summarise(mean=mean(weight), quant = quantile(weight, probs = 0.75)) %>%
arrange(desc(max))->
Tk
# extracting the compact letter display and adding to the Tk table
as.data.frame.list(cld$Plant)%>%
rownames_to_column("Plant")%>%
right_join(Tk)->
Tk2
dat%>%
ggplot(aes(Plant,StemWidth_mm))+
geom_violin()+
geom_boxplot(width=0.3)+
geom_text(data = Tk2, aes(x = Plant, y = 10, label = Letters))+
theme_bw()+
xlab("Plant ID")+ylab("Basal Stem Width of Cutting (mm)")-> width.fig
width.fig
ggsave(paste("StemWidth_",format(Sys.time(),"%Y%m%d-%Hh%Mm"),".jpg",sep=""))
Figure: Boxplot of Stem Height across Plants with post-hoc Tukey
dissimilarity letters
dat%>%
lm(Height_mm~Plant,data=.)%>%
aov()->anova
anova%>%
TukeyHSD()->tukey
cld <- multcompLetters4(anova, tukey)
print(cld)
dat%>%
group_by(Plant)%>%
summarise(max=max(Height_mm))%>%
#summarise(mean=mean(weight), quant = quantile(weight, probs = 0.75)) %>%
arrange(desc(max))->
Tk
# extracting the compact letter display and adding to the Tk table
as.data.frame.list(cld$Plant)%>%
rownames_to_column("Plant")%>%
right_join(Tk)->
Tk2
dat%>%
ggplot(aes(Plant,Height_mm))+
geom_violin()+
geom_boxplot(width=0.3)+
geom_text(data = Tk2, aes(x = Plant, y = 290, label = Letters))+
theme_bw()+
xlab("Plant ID")+ylab("Stem Height of Cutting (mm)")-> height.fig
height.fig
#ggsave(paste("Height_",format(Sys.time(),"%Y%m%d-%Hh%Mm"),".jpg",sep=""))
Figure: Boxplot of Dry Stem mass across Plants with post-hoc Tukey
dissimilarity letters
dat%>%
lm(StemDryMass_g~Plant,data=.)%>%
aov()->anova
anova%>%
TukeyHSD()->tukey
cld <- multcompLetters4(anova, tukey)
print(cld)
dat%>%
group_by(Plant)%>%
summarise(max=max(StemDryMass_g))%>%
#summarise(mean=mean(weight), quant = quantile(weight, probs = 0.75)) %>%
arrange(desc(max))->
Tk
# extracting the compact letter display and adding to the Tk table
as.data.frame.list(cld$Plant)%>%
rownames_to_column("Plant")%>%
right_join(Tk)->
Tk2
dat%>%
ggplot(aes(Plant,StemDryMass_g))+
geom_violin()+
geom_boxplot(width=0.3)+
geom_text(data = Tk2, aes(x = Plant, y = 290, label = Letters))+
theme_bw()+
xlab("Plant ID")+ylab("Stem Height of Cutting (mm)")
stemdrymass.fig
#ggsave(paste("Height_",format(Sys.time(),"%Y%m%d-%Hh%Mm"),".jpg",sep=""))
Linear model and correlation of stem width with rooting
fraction
dat%>%
mutate(Plant="ALL")%>%
bind_rows(dat)%>%
ggplot(aes(StemWidth_mm,RootFraction*100))+
geom_point()+
geom_smooth(method="lm",se=FALSE)+
stat_poly_line(se=FALSE) +
stat_poly_eq(aes(label = paste(after_stat(eq.label),
after_stat(rr.label), sep = "*\", \"*")))+
facet_wrap(~Plant)+
ylab("Root Percentage")+xlab("Stem diameter (mm)")
ggsave(paste("Width_vs_RootFraction_",format(Sys.time(),"%Y%m%d-%Hh%Mm"),".jpg",sep=""),width=8,height=8)
Linear model and correlation of cutting height with rooting
fraction
dat%>%
mutate(Plant="ALL")%>%
bind_rows(dat)%>%
ggplot(aes(Height_mm,RootFraction*100))+
geom_point()+
geom_smooth(method="lm",se=FALSE)+
stat_poly_line(se=FALSE) +
stat_poly_eq(aes(label = paste(after_stat(eq.label),
after_stat(rr.label), sep = "*\", \"*")))+
facet_wrap(~Plant)+
ylab("Root Percentage")+xlab("Plant Height (mm)")
ggsave(paste("Height_vs_RootFraction_",format(Sys.time(),"%Y%m%d-%Hh%Mm"),".jpg",sep=""),width=9,height=9)
Nested ANOVA: Root mass
dat%>%
filter(Treatment!="C")%>%
filter(!is.na(RootMass_g))%>%
aov(RootMass_g~Treatment/Plant,data=.)%>%
summary()
dat%>%
group_by(Plant,Treatment)%>%summarise(n=n()
)
Nested ANOVA: Root Fraction
dat%>%
filter(Treatment!="C")%>%
filter(!is.na(RootMass_g))%>%
aov(RootFraction~Treatment/Plant,data=.)%>%
summary()
Root mass overall and individual boxplot
dat%>%
ggplot(aes(Treatment,RootMass_g))+
geom_boxplot(size=1,outlier.size = 2)+
geom_boxplot(aes(fill=Plant),alpha=0.5,outlier.shape = "")+
scale_fill_manual(values=wes_palette(7, name = "Zissou1", type = "continuous"))+
#geom_violin()+
#geom_line()+
#geom_jitter(width = 0.15,aes(colour=Plant,shape=Plant))+
#facet_wrap(~Plant)+
ylab("Dry Root Mass (g)")
ggsave(paste("RootMassBoxplot_",format(Sys.time(),"%Y%m%d-%Hh%Mm"),".jpg",sep=""),width=7,height=5)
Root mass overall and individual boxplot v2
dat%>%
bind_rows(mutate(dat,Plant="All"))->
tutu
tutu%>%
filter(Treatment!="C")->tutu
for (i in unique(tutu$Plant)) {
print(i)
tutu%>%
filter(Plant==i)->
toto
kruskal.test(toto$RootMass_g,as.factor(toto$Treatment))%>%
print()
}
res <- NULL
for (i in unique(tutu$Plant)) {
tutu%>%
filter(Plant==i)->
toto
#kruskal.test(toto$RootMass_g,as.factor(toto$Treatment))
kruskalmc(toto$RootMass_g,as.factor(toto$Treatment)) ->
tata
dif3 <- tata$dif.com$difference
names(dif3)<-rownames(tata$dif.com)
multcompLetters(dif3)-> tyty
bind_rows(res,c(Plant=i,tyty$Letters))->res
}
# res%>%
# mutate(C="")->res
apply(res,1,function(fx) length(unique(fx)))->ind
res[ind==2,2:7]<-""
res%>%
pivot_longer(2:7,names_to="Treatment",values_to = "Symbol")->
res2
tutu%>%
group_by(Plant,Treatment)%>%
summarise(RootMass_g=max(RootMass_g,na.rm=T))%>%
left_join(res2)%>%
as.data.frame()->
sigletter
tutu%>%
ggplot(aes(Treatment,RootMass_g))+
geom_boxplot(size=1,outlier.size = 2)+
#geom_boxplot(aes(fill=Plant),alpha=0.5,outlier.shape = "")+
#scale_fill_manual(values=wes_palette(7, name = "Zissou1", type = "continuous"))+
#geom_violin()+
#geom_line()+
#geom_jitter(width = 0.15,aes(colour=Plant,shape=Plant))+
facet_wrap(~Plant,ncol=4)+
geom_text(data=sigletter,aes(Treatment,RootMass_g+0.05,label=Symbol))+
ylab("Dry Root Mass (g)")+
scale_x_discrete(guide = guide_axis(n.dodge = 2))
ggsave(paste("RootMassBoxplot_V3_",format(Sys.time(),"%Y%m%d-%Hh%Mm"),".jpg",sep=""),width=7,height=5)
Kruskal Test within plants across treatments
dat%>%
filter(Treatment!="C")%>%
group_by(Plant)%>%
do(tidy(kruskal.test(x = .$RootMass_g, g = .$Treatment)))
Rooting fraction per plant across treatments
(dat%>%
filter(Treatment!="C")%>%
group_by(Treatment,Plant)%>%
summarise(Rooted=sum(RootMass_g>0,na.rm=T),
n=n(),
RootedFrac=Rooted/n*100)->for_stats)%>%
ungroup()%>%
ggplot(aes(Treatment,RootedFrac,label=Plant))+
#geom_violin()+
geom_line(colour="grey",size=3)+
geom_text_repel(colour="darkgrey",fontface=3)+
geom_point(size=3)+
ylim(c(0,100))+
ylab("Rooted percentage per plant")+
xlab("Day of watering")+
labs(title = expression(paste(italic("Portulacaria afra")," rooting window experiment")),
subtitle = "Rooting percentage per plant for day of watering during the first month\n
Seven plants were harvested and the day of watering in the first month is shown. 12 cuttings per plant.",
caption = paste("Spekboom Restoration Research Group, Nelson Mandela University, 2021\n
Figure generated by Alastair.Potts@mandela.ac.za (",Sys.time(),")",sep=""))
ggsave("RootingWindow-RootPercent.jpg",width=8,height=5)
(dat%>%
filter(Treatment!="C")%>%
group_by(Treatment,Plant)%>%
summarise(Rooted=sum(RootMass_g>0,na.rm=T),
n=n(),
RootedFrac=Rooted/n*100)->for_stats)%>%
ungroup()%>%
ggplot(aes(Treatment,RootedFrac,label=Plant))+
#geom_violin()+
geom_line(colour="grey",size=3)+
geom_text_repel(colour="darkgrey",fontface=3)+
geom_point(size=3)+
ylim(c(0,100))+
ylab("Rooted percentage per plant")+
xlab("Day of watering")
ggsave("Fig1_Publication_RootingWindow-RootPercent.jpg",width=7,height=4)
Mean rooting fraction per plant for each treatment
(dat%>%
filter(Treatment!="C")%>%
group_by(Treatment,Plant)%>%
summarise(Rooted=sum(RootMass_g>0,na.rm=T),
n=n(),
uRootMass=mean(RootMass_g,na.rm=T),
medRootMass=median(RootMass_g)))%>%
ungroup()%>%
ggplot(aes(Treatment,uRootMass,label=Plant))+
#geom_violin()+
geom_line(colour="grey",size=3)+
geom_text_repel(colour="darkgrey",fontface=3)+
geom_point(size=3)+
#ylim(c(0,100))+
ylab("Mean root percentage per plant")+
xlab("Day of watering")
dat%>%
filter(Treatment!="C")%>%
group_by(Treatment,Plant)%>%
summarise(Rooted=sum(RootMass_g>0,na.rm=T),
n=n(),
uRootMass=mean(RootMass_g),
medRootMass=median(RootMass_g,na.rm=T))%>%
ungroup()%>%
ggplot(aes(Treatment,medRootMass,label=Plant))+
#geom_violin()+
geom_line(colour="grey",size=3)+
geom_text_repel(colour="darkgrey",fontface=3,min.segment.length = 0)+
geom_point(size=3)+
#ylim(c(0,100))+
ylab("Median root percentage per plant")+
xlab("Day of watering")
#Suplementary glasshouse environmental data analysis (.CSV files not
provided, if you would like to run this script please generate the
relevant data sets using “RootingWindow_FullData” provided)
read_csv(construct_download_url(url,sheetid="838520867"))%>%
tbl_df()->
dat_temp
read_csv(construct_download_url(url,sheetid="1444740489"))%>%
tbl_df()->
dat_treat
dat_treat%>%
mutate(Date=mdy_hm(paste(Date,"11h59")))->
dat_treat2
dat_temp%>%
mutate(Timestamp=ymd_hm(Timestamp))%>%
ggplot(aes(Timestamp,Temperature))+
geom_vline(xintercept = dat_treat2$Date,colour="darkgrey",size=1)+
geom_line()+
ylim(0,36)+
scale_x_datetime(sec.axis = sec_axis(~.,breaks=dat_treat2$Date,labels=dat_treat2$Treatment))+
xlab("")+
ylab("Temperature (°C) ")+
theme(axis.text.x.top =element_text(color="darkgrey"))+
geom_line(aes(Timestamp,DewPoint_dC),colour="black",linetype=2)->
A
dat_temp%>%
ggplot(aes("",Temperature))+
geom_boxplot()+
ylim(0,36)+
xlab("")+
ylab("")->
B
dat_temp%>%
mutate(Timestamp=ymd_hm(Timestamp))%>%
ggplot(aes(Timestamp,RelativeHumidity))+
geom_vline(xintercept = dat_treat2$Date,colour="darkgrey",size=1)+
geom_line()+
ylim(0,100)+
scale_x_datetime(sec.axis = sec_axis(~.,breaks=dat_treat2$Date,labels=dat_treat2$Treatment))+
theme(axis.text.x.top =element_text(color="darkgrey"))+
xlab("")+
ylab("Relative humidity (%)")->
C
(A+B)/C
A/C+plot_annotation(tag_levels = 'A')
ggsave("Env.conditions.jpg",width=8,height=5.5)
dat_temp%>%
mutate(Timestamp=ymd_hm(Timestamp),
Day=date(Timestamp))%>%
group_by(Day)%>%
summarise(maxT=max(Temperature),
minT=min(Temperature))%>%
ungroup()%>%
summarise(mean(maxT),
mean(minT),
sd(maxT),
sd(minT))
dat_temp%>%
summarise(mean(RelativeHumidity),sd(RelativeHumidity))
dat_temp%>%
group_by(Da)
summarise(uT=mean(Temperature))
---
title: "rootingWindow_PeerJ 2022"
output: html_notebook
editor_options: 
  chunk_output_type: inline
---

# Load the libraries
```{r libraries, echo=F}

library("dplyr")
library("ggplot2")
library("ggpmisc")
library("ggpubr")
library("patchwork")
library("lubridate")
library("tibble")
library("tidyr")
library("gsheet")
library("readr")
library("gtools")
library("wesanderson")
library("ggforce")
library("ggrepel")
library("tibbletime")
library("broom")
library("nlme")
library("multcompView")
library("lme4")
library("rmarkdown")
library("pgirmess")

#Please set your own working directory
setwd()
```

# Get the data
```{r}


read_csv("RootingWindow_RawData.csv")%>%
  tbl_df()->
  dat

dat%>%
  select(-DegreeOfBranching,-NoOfRootingPts)->
  dat


```

# Stem width vs stem height
```{r}
dat%>%
  mutate(Plant="ALL")%>%
  bind_rows(dat)%>%
  filter(StemWidth_mm!=max(StemWidth_mm))%>%
  ggplot(aes(StemWidth_mm,Height_mm))+
  geom_point()+
  geom_smooth(method="lm")+
  stat_poly_line(se=FALSE) +
  stat_poly_eq(aes(label = paste(after_stat(eq.label),
                                 after_stat(rr.label), sep = "*\", \"*")))+
  facet_wrap(~Plant)+
  ylab("Height (mm)")+xlab("Stem diameter (mm)")
```

# Stem width at start of experiment vs above ground biomass at end
```{r}
dat%>%
  mutate(Plant="ALL")%>%
  bind_rows(dat)%>%
  filter(StemWidth_mm!=max(StemWidth_mm))%>%
  ggplot(aes(StemWidth_mm,AboveGroundMass_g))+
  geom_point()+
  geom_smooth(method="lm")+
  stat_poly_line(se=FALSE) +
  stat_poly_eq(aes(label = paste(after_stat(eq.label),
                                 after_stat(rr.label), sep = "*\", \"*")))+
  facet_wrap(~Plant)+
  ylab("Above ground biomass at end of experiment")+xlab("Stem diameter (mm)")
ggsave(paste("ScatterPlot_Width_Mass_",format(Sys.time(),"%Y%m%d-%Hh%Mm"),".jpg",sep=""),width=8,height=8)
```
# Stem Height at start of experiment vs above ground biomass at end
```{r}
dat%>%
  mutate(Plant="ALL")%>%
  bind_rows(dat)%>%
  filter(StemWidth_mm!=max(StemWidth_mm))%>%
  ggplot(aes(Height_mm,AboveGroundMass_g))+
  geom_point()+
  geom_smooth(method="lm")+
  stat_poly_line(se=FALSE) +
  stat_poly_eq(aes(label = paste(after_stat(eq.label),
                                 after_stat(rr.label), sep = "*\", \"*")))+
  facet_wrap(~Plant)+
  ylab("Above ground biomass at end of experiment")+xlab("Height (mm)")
ggsave(paste("ScatterPlot_Height_Mass_",format(Sys.time(),"%Y%m%d-%Hh%Mm"),".jpg",sep=""),width=8,height=8)
```

# Figure: Boxplot of StemWidth across Plants with post-hoc Tukey dissimilarity letters
```{r}
dat%>%
  lm(StemWidth_mm~Plant,data=.)%>%
  aov()->anova
anova%>%
  TukeyHSD()->tukey
cld <- multcompLetters4(anova, tukey)
print(cld)
dat%>%
  group_by(Plant)%>%
  summarise(max=max(StemWidth_mm))%>%
  #summarise(mean=mean(weight), quant = quantile(weight, probs = 0.75)) %>%
  arrange(desc(max))->
  Tk
# extracting the compact letter display and adding to the Tk table
as.data.frame.list(cld$Plant)%>%
  rownames_to_column("Plant")%>%
  right_join(Tk)->
  Tk2
dat%>%
  ggplot(aes(Plant,StemWidth_mm))+
  geom_violin()+
  geom_boxplot(width=0.3)+
  geom_text(data = Tk2, aes(x = Plant, y = 10, label = Letters))+
  theme_bw()+
  xlab("Plant ID")+ylab("Basal Stem Width of Cutting (mm)")-> width.fig
width.fig
ggsave(paste("StemWidth_",format(Sys.time(),"%Y%m%d-%Hh%Mm"),".jpg",sep=""))
```

# Figure: Boxplot of Stem Height across Plants with post-hoc Tukey dissimilarity letters
```{r}
dat%>%
  lm(Height_mm~Plant,data=.)%>%
  aov()->anova
anova%>%
  TukeyHSD()->tukey
cld <- multcompLetters4(anova, tukey)
print(cld)
dat%>%
  group_by(Plant)%>%
  summarise(max=max(Height_mm))%>%
  #summarise(mean=mean(weight), quant = quantile(weight, probs = 0.75)) %>%
  arrange(desc(max))->
  Tk
# extracting the compact letter display and adding to the Tk table
as.data.frame.list(cld$Plant)%>%
  rownames_to_column("Plant")%>%
  right_join(Tk)->
  Tk2
dat%>%
  ggplot(aes(Plant,Height_mm))+
  geom_violin()+
  geom_boxplot(width=0.3)+
  geom_text(data = Tk2, aes(x = Plant, y = 290, label = Letters))+
  theme_bw()+
  xlab("Plant ID")+ylab("Stem Height  of Cutting (mm)")-> height.fig
height.fig
#ggsave(paste("Height_",format(Sys.time(),"%Y%m%d-%Hh%Mm"),".jpg",sep=""))
```

# Figure: Boxplot of Dry Stem mass across Plants with post-hoc Tukey dissimilarity letters
```{r}
dat%>%
  lm(StemDryMass_g~Plant,data=.)%>%
  aov()->anova
anova%>%
  TukeyHSD()->tukey
cld <- multcompLetters4(anova, tukey)
print(cld)
dat%>%
  group_by(Plant)%>%
  summarise(max=max(StemDryMass_g))%>%
  #summarise(mean=mean(weight), quant = quantile(weight, probs = 0.75)) %>%
  arrange(desc(max))->
  Tk
# extracting the compact letter display and adding to the Tk table
as.data.frame.list(cld$Plant)%>%
  rownames_to_column("Plant")%>%
  right_join(Tk)->
  Tk2
dat%>%
  ggplot(aes(Plant,StemDryMass_g))+
  geom_violin()+
  geom_boxplot(width=0.3)+
  geom_text(data = Tk2, aes(x = Plant, y = 290, label = Letters))+
  theme_bw()+
  xlab("Plant ID")+ylab("Stem Height  of Cutting (mm)")

stemdrymass.fig
#ggsave(paste("Height_",format(Sys.time(),"%Y%m%d-%Hh%Mm"),".jpg",sep=""))
```
# Combination of the two figures above
```{r}

(width.fig+xlab("")+ggtitle('A)'))/(height.fig+xlab("")+ggtitle('B)'))
ggsave(paste("Boxplots_Width_Height_",format(Sys.time(),"%Y%m%d-%Hh%Mm"),".jpg",sep=""))
```


# Linear model and correlation of stem width with rooting fraction
```{r}
dat%>%
  mutate(Plant="ALL")%>%
  bind_rows(dat)%>%
  ggplot(aes(StemWidth_mm,RootFraction*100))+
  geom_point()+
  geom_smooth(method="lm",se=FALSE)+
  stat_poly_line(se=FALSE) +
  stat_poly_eq(aes(label = paste(after_stat(eq.label),
                                 after_stat(rr.label), sep = "*\", \"*")))+
  facet_wrap(~Plant)+
  ylab("Root Percentage")+xlab("Stem diameter (mm)")
ggsave(paste("Width_vs_RootFraction_",format(Sys.time(),"%Y%m%d-%Hh%Mm"),".jpg",sep=""),width=8,height=8)
```


# Linear model and correlation of cutting height with rooting fraction
```{r}
dat%>%
  mutate(Plant="ALL")%>%
  bind_rows(dat)%>%
  ggplot(aes(Height_mm,RootFraction*100))+
  geom_point()+
  geom_smooth(method="lm",se=FALSE)+
  stat_poly_line(se=FALSE) +
  stat_poly_eq(aes(label = paste(after_stat(eq.label),
                                 after_stat(rr.label), sep = "*\", \"*")))+
  facet_wrap(~Plant)+
  ylab("Root Percentage")+xlab("Plant Height (mm)")
ggsave(paste("Height_vs_RootFraction_",format(Sys.time(),"%Y%m%d-%Hh%Mm"),".jpg",sep=""),width=9,height=9)
```

# Nested ANOVA: Root mass
```{r}
dat%>%
  filter(Treatment!="C")%>%
  filter(!is.na(RootMass_g))%>%
  aov(RootMass_g~Treatment/Plant,data=.)%>%
  summary()

dat%>%
 group_by(Plant,Treatment)%>%summarise(n=n()
                                  )

```
# Nested ANOVA: Root Fraction
```{r}
dat%>%
  filter(Treatment!="C")%>%
  filter(!is.na(RootMass_g))%>%
  aov(RootFraction~Treatment/Plant,data=.)%>%
  summary()
```

# Root mass overall and individual boxplot
```{r}
dat%>%
  ggplot(aes(Treatment,RootMass_g))+
  geom_boxplot(size=1,outlier.size = 2)+
  geom_boxplot(aes(fill=Plant),alpha=0.5,outlier.shape = "")+
  scale_fill_manual(values=wes_palette(7, name = "Zissou1", type = "continuous"))+
  #geom_violin()+
  #geom_line()+
  #geom_jitter(width = 0.15,aes(colour=Plant,shape=Plant))+
  #facet_wrap(~Plant)+
  ylab("Dry Root Mass (g)")
ggsave(paste("RootMassBoxplot_",format(Sys.time(),"%Y%m%d-%Hh%Mm"),".jpg",sep=""),width=7,height=5)
```
# Root mass overall and individual boxplot v2
```{r}
dat%>%
  bind_rows(mutate(dat,Plant="All"))->
  tutu
 tutu%>%
   filter(Treatment!="C")->tutu

for (i in unique(tutu$Plant)) {
  print(i)
  tutu%>%
    filter(Plant==i)->
    toto
  kruskal.test(toto$RootMass_g,as.factor(toto$Treatment))%>%
    print()
}
 
 
res <- NULL
for (i in unique(tutu$Plant)) {
  tutu%>%
    filter(Plant==i)->
    toto
  #kruskal.test(toto$RootMass_g,as.factor(toto$Treatment))
  kruskalmc(toto$RootMass_g,as.factor(toto$Treatment)) ->
  tata
  dif3 <- tata$dif.com$difference 
  names(dif3)<-rownames(tata$dif.com)
  multcompLetters(dif3)-> tyty
  
  bind_rows(res,c(Plant=i,tyty$Letters))->res
}
# res%>%
#   mutate(C="")->res
apply(res,1,function(fx) length(unique(fx)))->ind
res[ind==2,2:7]<-""
res%>%
  pivot_longer(2:7,names_to="Treatment",values_to = "Symbol")->
  res2
tutu%>%
  group_by(Plant,Treatment)%>%
  summarise(RootMass_g=max(RootMass_g,na.rm=T))%>%
  left_join(res2)%>%
  as.data.frame()->
  sigletter

tutu%>%
  ggplot(aes(Treatment,RootMass_g))+
  geom_boxplot(size=1,outlier.size = 2)+
  #geom_boxplot(aes(fill=Plant),alpha=0.5,outlier.shape = "")+
  #scale_fill_manual(values=wes_palette(7, name = "Zissou1", type = "continuous"))+
  #geom_violin()+
  #geom_line()+
  #geom_jitter(width = 0.15,aes(colour=Plant,shape=Plant))+
  facet_wrap(~Plant,ncol=4)+
  geom_text(data=sigletter,aes(Treatment,RootMass_g+0.05,label=Symbol))+
  ylab("Dry Root Mass (g)")+
  scale_x_discrete(guide = guide_axis(n.dodge = 2)) 
ggsave(paste("RootMassBoxplot_V3_",format(Sys.time(),"%Y%m%d-%Hh%Mm"),".jpg",sep=""),width=7,height=5)
```


# Kruskal Test within plants across treatments
```{r}
dat%>%
  filter(Treatment!="C")%>%
  group_by(Plant)%>%
  do(tidy(kruskal.test(x = .$RootMass_g, g = .$Treatment)))
```

# Rooting fraction per plant across treatments
```{r}
(dat%>%
  filter(Treatment!="C")%>%
  group_by(Treatment,Plant)%>%
  summarise(Rooted=sum(RootMass_g>0,na.rm=T),
            n=n(),
            RootedFrac=Rooted/n*100)->for_stats)%>%
  ungroup()%>%
  ggplot(aes(Treatment,RootedFrac,label=Plant))+
  #geom_violin()+
  geom_line(colour="grey",size=3)+
  geom_text_repel(colour="darkgrey",fontface=3)+
  geom_point(size=3)+
  ylim(c(0,100))+
  ylab("Rooted percentage per plant")+
  xlab("Day of watering")+
  labs(title = expression(paste(italic("Portulacaria afra")," rooting window experiment")), 
       subtitle = "Rooting percentage per plant for day of watering during the first month\n
       Seven plants were harvested and the day of watering in the first month is shown. 12 cuttings per plant.", 
       caption = paste("Spekboom Restoration Research Group, Nelson Mandela University, 2021\n
       Figure generated by Alastair.Potts@mandela.ac.za (",Sys.time(),")",sep=""))
ggsave("RootingWindow-RootPercent.jpg",width=8,height=5)
```

```{r}
(dat%>%
  filter(Treatment!="C")%>%
  group_by(Treatment,Plant)%>%
  summarise(Rooted=sum(RootMass_g>0,na.rm=T),
            n=n(),
            RootedFrac=Rooted/n*100)->for_stats)%>%
  ungroup()%>%
  ggplot(aes(Treatment,RootedFrac,label=Plant))+
  #geom_violin()+
  geom_line(colour="grey",size=3)+
  geom_text_repel(colour="darkgrey",fontface=3)+
  geom_point(size=3)+
  ylim(c(0,100))+
  ylab("Rooted percentage per plant")+
  xlab("Day of watering")
ggsave("Fig1_Publication_RootingWindow-RootPercent.jpg",width=7,height=4)
```


# Mean rooting fraction per plant for each treatment
```{r}
(dat%>%
    filter(Treatment!="C")%>%
    group_by(Treatment,Plant)%>%
    summarise(Rooted=sum(RootMass_g>0,na.rm=T),
              n=n(),
              uRootMass=mean(RootMass_g,na.rm=T),
              medRootMass=median(RootMass_g)))%>%
  ungroup()%>%
  ggplot(aes(Treatment,uRootMass,label=Plant))+
  #geom_violin()+
  geom_line(colour="grey",size=3)+
  geom_text_repel(colour="darkgrey",fontface=3)+
  geom_point(size=3)+
  #ylim(c(0,100))+
  ylab("Mean root percentage per plant")+
  xlab("Day of watering")  
```

```{r}
dat%>%
    filter(Treatment!="C")%>%
    group_by(Treatment,Plant)%>%
    summarise(Rooted=sum(RootMass_g>0,na.rm=T),
              n=n(),
              uRootMass=mean(RootMass_g),
              medRootMass=median(RootMass_g,na.rm=T))%>%
  ungroup()%>%
  ggplot(aes(Treatment,medRootMass,label=Plant))+
  #geom_violin()+
  geom_line(colour="grey",size=3)+
  geom_text_repel(colour="darkgrey",fontface=3,min.segment.length = 0)+
  geom_point(size=3)+
  #ylim(c(0,100))+
  ylab("Median root percentage per plant")+
  xlab("Day of watering")  
```

#Suplementary glasshouse environmental data analysis (.CSV files not provided, if you would like to run this script please generate the relevant data sets using "RootingWindow_FullData" provided)
```{r}
read_csv(construct_download_url(url,sheetid="838520867"))%>%
  tbl_df()->
  dat_temp

read_csv(construct_download_url(url,sheetid="1444740489"))%>%
  tbl_df()->
  dat_treat

dat_treat%>%
  mutate(Date=mdy_hm(paste(Date,"11h59")))->
  dat_treat2



dat_temp%>%
  mutate(Timestamp=ymd_hm(Timestamp))%>%
  ggplot(aes(Timestamp,Temperature))+
  geom_vline(xintercept = dat_treat2$Date,colour="darkgrey",size=1)+
  geom_line()+
  ylim(0,36)+
  scale_x_datetime(sec.axis = sec_axis(~.,breaks=dat_treat2$Date,labels=dat_treat2$Treatment))+
  xlab("")+
  ylab("Temperature (°C) ")+
  theme(axis.text.x.top =element_text(color="darkgrey"))+
  geom_line(aes(Timestamp,DewPoint_dC),colour="black",linetype=2)->
  A
  
dat_temp%>%
  ggplot(aes("",Temperature))+
  geom_boxplot()+
  ylim(0,36)+
  xlab("")+
  ylab("")->
  B

dat_temp%>%
  mutate(Timestamp=ymd_hm(Timestamp))%>%
  ggplot(aes(Timestamp,RelativeHumidity))+
  geom_vline(xintercept = dat_treat2$Date,colour="darkgrey",size=1)+
  geom_line()+
  ylim(0,100)+
  scale_x_datetime(sec.axis = sec_axis(~.,breaks=dat_treat2$Date,labels=dat_treat2$Treatment))+
  theme(axis.text.x.top =element_text(color="darkgrey"))+
  xlab("")+
  ylab("Relative humidity (%)")->
  C


(A+B)/C
A/C+plot_annotation(tag_levels = 'A')
ggsave("Env.conditions.jpg",width=8,height=5.5)  

dat_temp%>%
  mutate(Timestamp=ymd_hm(Timestamp),
         Day=date(Timestamp))%>%
  group_by(Day)%>%
  summarise(maxT=max(Temperature),
            minT=min(Temperature))%>%
  ungroup()%>%
  summarise(mean(maxT),
            mean(minT),
            sd(maxT),
            sd(minT))
dat_temp%>%
  summarise(mean(RelativeHumidity),sd(RelativeHumidity))
dat_temp%>%
  group_by(Da)
  summarise(uT=mean(Temperature))

```

