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=""))

Combination of the two figures above


(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

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))

```

