Chapter 9 More than two groups

#::::::::::::::::::::::::::::::::::::::::::::::::: # Pairwise comparisons: Specify the comparisons you want my_comparisons <- list( c(“0.5”, “1”), c(“1”, “2”), c(“0.5”, “2”) ) ggboxplot(ToothGrowth, x = “dose”, y = “len”, color = “dose”, palette = “npg”)+ # Add pairwise comparisons p-value stat_compare_means(comparisons = my_comparisons, label.y = c(29, 35, 40))+ stat_compare_means(label.y = 45)

 allsites_filtered <- All_sites %>% mutate(hc = log(height)) %>% 
  dplyr::filter(height > 0 & hr %in% c(1:24)) %>%
  dplyr::filter(temp > -20) %>% as.data.frame()
  
my_comparisons <- list( c(0.3, 0.6) ,c(0.6, 0.9),c(0.9, 1.2),c(0.3, 1.2))
   ggboxplot(allsites_filtered, x = "height", y = "temp",
         palette = c("#00AFBB","#E7B800"),
         add = "boxplot", add.params = list(fill = "white"))+
  stat_compare_means(comparisons = my_comparisons) + # Add significance levels
  stat_compare_means(label.y = 50)   +
    labs(title="Temperature between open and dense canopies (Hourly)",x="", y="Temperature(°C)", tag="", colour = "")

density plots

 allsites_filtered_ridge <- All_sites %>% mutate(hc = log(height), hfac = as.factor(height)) %>% 
  dplyr::filter(height > 0 & hr %in% c(1:24)) %>%
  dplyr::filter(temp > -20) %>% as.data.frame()
  
ggdensity(allsites_filtered_ridge, x = "temp",
   add = "mean", rug = TRUE,
   color = "canopy", palette = c("#00AFBB", "#E7B800")) +
     theme_minimal(base_size=24) + labs(x=" Temperature (°C)", y="Density",color="Canopy")

ggsave("chap1_buffer_gaps_density.png",dpi=300, dev='png', height=8, width=8, units="in")

by height across all elevations

ggdensity(allsites_filtered_ridge, x = "temp",
   add = "mean", rug = TRUE,facet.by = "height",
   color = "canopy", palette = c("#00AFBB", "#E7B800")) +
     theme_minimal(base_size=24) + labs(x=" Temperature (°C)", y="Density",color="Canopy")

ggsave("chap1_buffer_gaps_density_heights.png",dpi=300, dev='png', height=8, width=8, units="in")

by elevation

by height across all elevations, by hour

ggdensity(allsites_filtered_ridge, x = "temp",
   add = "mean", rug = TRUE,facet.by = "hr",
   color = "canopy", palette = c("#00AFBB", "#E7B800")) +
     theme_minimal(base_size=24) + labs(x="Temperature (°C)", y="Density",color="Canopy")

ggsave("chap1_buffer_gaps_density_hr.png",dpi=300, dev='png', height=8, width=12, units="in")

by canopy across all the hours

allsites_filtered_ridge %>% group_by(canopy,hr) %>% 
  dplyr::summarise(mt= mean(temp)) %>% ggdotplot( "hr", "mt", 
            fill = "canopy",
    palette = c("#00AFBB", "#E7B800")) +   labs(fill="Canopy",subtitle = "",  x=" Time (hour)", y="Mean Temperature (°C)") + theme_minimal(base_size=18) +theme(legend.position = "bottom")

## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.

ggsave("chap1_bufferhrs_redo.png",dpi=300, dev='png', height=8, width=12, units="in")

## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.

library(ggpubr)
allsites_filtered_ridge %>% group_by(canopy,hr) %>% 
  dplyr::summarise(mt= mean(temp)) %>% ggboxplot(x= "canopy", y="mt", color = "canopy",
add="jitter", error.plot = "errorbar",
    palette = c("#00AFBB", "#E7B800")) +   labs(subtitle = "",  x="", y="Mean Temperature (°C)") +
   stat_compare_means( aes(label = ..p.signif..), 
                        label.x = 1.5, label.y = 15) +
  ylim(0,15)+
   scale_x_discrete(labels=c("Dense canopy", "Gaps")) +
  theme_minimal(base_size=24) +theme(legend.position = "bottom") +
  theme(legend.position = "")

temp1_hyp1 <- allsites_filtered_ridge %>% group_by(canopy,hr) %>% 
  dplyr::summarise(mt= mean(temp)) %>% ggboxplot(x= "canopy", y="mt", color = "canopy",
add="jitter", error.plot = "errorbar",
    palette = c("#00AFBB", "#E7B800")) +   labs(subtitle = "",  x="", y="Mean Temperature (°C)") +
   stat_compare_means( aes(label = ..p.signif..), 
                        label.x = 1.5, label.y = 15) +
  ylim(0,15)+
   scale_x_discrete(labels=c("Dense canopy", "Gaps")) +
  theme_minimal(base_size=24) +theme(legend.position = "bottom") +
  theme(legend.position = "")
ggsave("chap1_bufferhrs_redo_mt.png",dpi=300, dev='png', height=8, width=12, units="in")

Mean temperature at different eight

library(ggpubr)
allsites_filtered_ridge %>% group_by(height,canopy,hr) %>% 
  dplyr::summarise(mt= mean(temp)) %>% ggboxplot(x= "canopy", y="mt", color = "canopy",
add="jitter", error.plot = "errorbar", facet.by = "height", short.panel.labs = FALSE,
    palette = c("#00AFBB", "#E7B800")) +   labs(subtitle = "",  x="", y="Mean Temperature (°C)") +
   stat_compare_means( aes(label = ..p.signif..), 
                        label.x = 1.5, label.y = 15) +
  ylim(0,15)+
   scale_x_discrete(labels=c("Dense canopy", "Gaps")) +
  theme_minimal(base_size=24) +theme(legend.position = "bottom") +
  theme(legend.position = "")

temp2_hyp1 <- allsites_filtered_ridge %>% group_by(height,canopy,hr) %>% 
  dplyr::summarise(mt= mean(temp)) %>% ggboxplot(x= "canopy", y="mt", color = "canopy",
add="jitter", error.plot = "errorbar", facet.by = "height", short.panel.labs = FALSE,
    palette = c("#00AFBB", "#E7B800")) +   labs(subtitle = "",  x="", y="Mean Temperature (°C)") +
   stat_compare_means( aes(label = ..p.signif..), 
                        label.x = 1.5, label.y = 15) +
  ylim(0,15)+
   scale_x_discrete(labels=c("Dense canopy", "Gaps")) +
  theme_minimal(base_size=24) +theme(legend.position = "bottom") +
  theme(legend.position = "")

ggsave("chap1_bufferhrs_redo_mt_height.png",dpi=300, dev='png', height=8, width=12, units="in")

library(ggpubr)
allsites_filtered_ridge %>% group_by(canopy,day) %>% 
  dplyr::summarise(mt= mean(temp),dv=max(temp)-min(temp)) %>% ggboxplot(x= "canopy", y="dv", color = "canopy",
add="jitter", error.plot = "errorbar", 
    palette = c("#00AFBB", "#E7B800")) +   labs(subtitle = "",  x="", y="Diurnal variation (°C)") +
   stat_compare_means( aes(label = ..p.signif..), 
                        label.x = 1.5, label.y = 15) +
  ylim(0,15)+
   scale_x_discrete(labels=c("Dense canopy", "Gaps")) +
  theme_minimal(base_size=24) +theme(legend.position = "bottom") +
  theme(legend.position = "")

dv1_hyp1 <- allsites_filtered_ridge %>% group_by(canopy,day) %>% 
  dplyr::summarise(mt= mean(temp),dv=max(temp)-min(temp)) %>% ggboxplot(x= "canopy", y="dv", color = "canopy",
add="jitter", error.plot = "errorbar", 
    palette = c("#00AFBB", "#E7B800")) +   labs(subtitle = "",  x="", y="Diurnal variation (°C)") +
   stat_compare_means( aes(label = ..p.signif..), 
                        label.x = 1.5, label.y = 15) +
  ylim(0,15)+
   scale_x_discrete(labels=c("Dense canopy", "Gaps")) +
  theme_minimal(base_size=24) +theme(legend.position = "bottom") +
  theme(legend.position = "")
ggsave("chap1_bufferhrs_redo_mt_diurnal.png",dpi=300, dev='png', height=8, width=12, units="in")

library(ggpubr)
allsites_filtered_ridge %>% group_by(canopy,height,day) %>% 
  dplyr::summarise(mt= mean(temp),dv=max(temp)-min(temp)) %>% ggboxplot(x= "canopy", y="dv", color = "canopy",
add="jitter", error.plot = "errorbar",  facet.by = "height", short.panel.labs = FALSE,
    palette = c("#00AFBB", "#E7B800")) +   labs(subtitle = "",  x="", y="Diurnal variation (°C)") +
   stat_compare_means( aes(label = ..p.signif..), 
                        label.x = 1.5, label.y = 15) +
  ylim(0,15)+
   scale_x_discrete(labels=c("Dense canopy", "Gaps")) +
  theme_minimal(base_size=24) +theme(legend.position = "bottom") +
  theme(legend.position = "")

dv2_hyp1 <-  allsites_filtered_ridge %>% group_by(canopy,height,day) %>% 
  dplyr::summarise(mt= mean(temp),dv=max(temp)-min(temp)) %>% ggboxplot(x= "canopy", y="dv", color = "canopy",
add="jitter", error.plot = "errorbar",  facet.by = "height", short.panel.labs = FALSE,
    palette = c("#00AFBB", "#E7B800")) +   labs(subtitle = "",  x="", y="Diurnal variation (°C)") +
   stat_compare_means( aes(label = ..p.signif..), 
                        label.x = 1.5, label.y = 15) +
  ylim(0,15)+
   scale_x_discrete(labels=c("Dense canopy", "Gaps")) +
  theme_minimal(base_size=24) +theme(legend.position = "bottom") +
  theme(legend.position = "")

ggsave("chap1_bufferhrs_redo_mt_diurnal_height.png",dpi=300, dev='png', height=8, width=12, units="in")

library(patchwork)

## 
## Attaching package: 'patchwork'

## The following object is masked from 'package:cowplot':
## 
##     align_plots

## The following object is masked from 'package:raster':
## 
##     area

(temp2_hyp1 + temp1_hyp1) /(dv2_hyp1 + dv1_hyp1)

ggsave("chap1_buffer_gaps_hyp1_mp.png",dpi=300, dev='png', height=12, width=14, units="in")

Combine the plot

ggdensity(allsites_elevation_sigf, x = "dv",
   add = "mean", rug = TRUE,facet.by = "elevation",
   color = "canopy", palette = c("#00AFBB", "#E7B800")) +
     theme_minimal(base_size=24) + labs(x=" Diel Temperature (°C)", y="Density",color="Canopy")

ggsave("chap1_buffer_gaps_density_elevation_dv.png",dpi=300, dev='png', height=8, width=8, units="in")

by height across all elevations, by hour

point6 <- allsites_filtered_ridge %>% group_by(canopy,hr) %>% 
  filter(height==0.6) %>%
  dplyr::summarise(mt= mean(temp)) %>% ggdotplot( "hr", "mt", 
            fill = "canopy",
    palette = c("#00AFBB", "#E7B800")) +   labs(fill="",subtitle = "0.6m",  x=" Time (hour)", y="Mean Temperature (°C)") + theme_minimal(base_size=18) +theme(legend.position = "")

point3 <- allsites_filtered_ridge %>% group_by(canopy,hr) %>% 
  filter(height==0.3) %>%
  dplyr::summarise(mt= mean(temp)) %>% ggdotplot( "hr", "mt", 
            fill = "canopy", 
    palette = c("#00AFBB", "#E7B800")) +       labs(fill="",subtitle = "0.3m",x=" Time (hour)", y="Mean Temperature (°C)")+
  theme_minimal(base_size=18) +theme(legend.position = "")
point9 <- allsites_filtered_ridge %>% group_by(canopy,hr) %>% 
  filter(height==0.9) %>%
  dplyr::summarise(mt= mean(temp)) %>% ggdotplot( "hr", "mt", 
            fill = "canopy",
    palette = c("#00AFBB", "#E7B800")) +      labs(fill="",subtitle = "0.9m",x=" Time (hour)", y="Min Temperature (°C)") +
  theme_minimal(base_size=18) +theme(legend.position = "bottom",legend.direction = "horizontal")

onepoint2 <- allsites_filtered_ridge %>% group_by(canopy,hr) %>% 
  filter(height==1.2) %>%
  dplyr::summarise(mt= mean(temp)) %>% ggdotplot( "hr", "mt", 
            fill = "canopy",
    palette = c("#00AFBB", "#E7B800")) +     labs(fill="",subtitle = "1.2m",x=" Time (hour)", y="Min Temperature (°C)") +
  theme_minimal(base_size=18) +theme(legend.position = "bottom",legend.direction = "horizontal")

library(patchwork)
(point3 + point6) / (point9 + onepoint2)

## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.

ggsave("chap1_buffer_gaps_hr_by_height_redo.png",dpi=300, dev='png', height=10, width=12, units="in")

## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.

by height across all elevations, by hour min

point6 <- allsites_filtered_ridge %>% group_by(canopy,hr) %>% 
  filter(height==0.6) %>%
  dplyr::summarise(mt= min(temp)) %>% ggdotplot( "hr", "mt", 
            fill = "canopy",
    palette = c("#00AFBB", "#E7B800")) +   labs(fill="",subtitle = "0.6m",  x=" Time (hour)", y="Min Temperature (°C)") + theme_minimal(base_size=18) +theme(legend.position = "")

point3 <- allsites_filtered_ridge %>% group_by(canopy,hr) %>% 
  filter(height==0.3) %>%
  dplyr::summarise(mt= min(temp)) %>% ggdotplot( "hr", "mt", 
            fill = "canopy",
    palette = c("#00AFBB", "#E7B800")) +       labs(fill="",subtitle = "0.3m",x=" Time (hour)", y="Min Temperature (°C)")+
  theme_minimal(base_size=18) +theme(legend.position = "")
point9 <- allsites_filtered_ridge %>% group_by(canopy,hr) %>% 
  filter(height==0.9) %>%
  dplyr::summarise(mt= min(temp)) %>% ggdotplot( "hr", "mt", 
             fill = "canopy",
    palette = c("#00AFBB", "#E7B800")) +    labs(fill="",subtitle = "0.9m",x=" Time (hour)", y="Min Temperature (°C)") +
  theme_minimal(base_size=18) +theme(legend.position = "bottom",legend.direction = "horizontal")

onepoint2 <- allsites_filtered_ridge %>% group_by(canopy,hr) %>% 
  filter(height==1.2) %>%
  dplyr::summarise(mt= min(temp)) %>% ggdotplot( "hr", "mt", 
            fill = "canopy", 
    palette = c("#00AFBB", "#E7B800")) +    labs(fill="",subtitle = "1.2m",x=" Time (hour)", y="Min Temperature (°C)") +
  theme_minimal(base_size=18) +theme(legend.position = "bottom",legend.direction = "horizontal")

library(patchwork)
(point3 + point6) / (point9 + onepoint2)

## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.

ggsave("chap1_buffer_gaps_hr_by_height_min_redo.png",dpi=300, dev='png', height=10, width=12, units="in")

## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.

max

point6 <- allsites_filtered_ridge %>% group_by(canopy,hr) %>% 
  filter(height==0.6) %>%
  dplyr::summarise(mt= max(temp)) %>% ggdotplot( "hr", "mt", 
            fill = "canopy",
    palette = c("#00AFBB", "#E7B800")) +   labs(fill="",subtitle = "0.6m",  x=" Time (hour)", y="Max Temperature (°C)") + theme_minimal(base_size=18) +theme(legend.position = "")

point3 <- allsites_filtered_ridge %>% group_by(canopy,hr) %>% 
  filter(height==0.3) %>%
  dplyr::summarise(mt= max(temp)) %>% ggdotplot( "hr", "mt", 
            fill = "canopy",
    palette = c("#00AFBB", "#E7B800")) +       labs(fill="",subtitle = "0.3m",x=" Time (hour)", y="Max Temperature (°C)")+
  theme_minimal(base_size=18) +theme(legend.position = "")
point9 <- allsites_filtered_ridge %>% group_by(canopy,hr) %>% 
  filter(height==0.9) %>%
  dplyr::summarise(mt= max(temp)) %>% ggdotplot( "hr", "mt", 
             fill = "canopy",
    palette = c("#00AFBB", "#E7B800")) +    labs(fill="",subtitle = "0.9m",x=" Time (hour)", y="Max Temperature (°C)") +
  theme_minimal(base_size=18) +theme(legend.position = "bottom",legend.direction = "horizontal")

onepoint2 <- allsites_filtered_ridge %>% group_by(canopy,hr) %>% 
  filter(height==1.2) %>%
  dplyr::summarise(mt= max(temp)) %>% ggdotplot( "hr", "mt", 
            fill = "canopy", 
    palette = c("#00AFBB", "#E7B800")) +    labs(fill="",subtitle = "1.2m",x=" Time (hour)", y="Max Temperature (°C)") +
  theme_minimal(base_size=18) +theme(legend.position = "bottom",legend.direction = "horizontal")

library(patchwork)
(point3 + point6) / (point9 + onepoint2)

## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.

ggsave("chap1_buffer_gaps_hr_by_height_max_redo.png",dpi=300, dev='png', height=10, width=12, units="in")

## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.

first hump is early mornings, where the open canopy is warming

ggdensity(allsites_elevation_sigf, x = "dv",
   add = "mean", rug = TRUE,facet.by = "elevation",
   color = "canopy", palette = c("#00AFBB", "#E7B800")) +
     theme_minimal(base_size=24) + labs(x=" Diel Temperature (°C)", y="Density",color="Canopy")

ggsave("chap1_buffer_gaps_density_elevation_dv.png",dpi=300, dev='png', height=8, width=8, units="in")

ggdensity(allsites_elevation_sigf, x = "mean_temp",
   add = "mean", rug = TRUE,facet.by = "elevation",
   color = "canopy", palette = c("#00AFBB", "#E7B800")) +
     theme_minimal(base_size=24) 

ggsave("chap1_buffer_gaps_density_elevation_mean_temp.png",dpi=300, dev='png', height=8, width=8, units="in")

All_site_withelevationheight <- All_site_withelevation %>% mutate(hcf= factor(height))
ggdensity(All_site_withelevationheight, x = "temp",
   add = "mean", rug = TRUE,facet.by = "elevation",
   color = "hcf",fill="hcf") +
     theme_minimal(base_size=24) 

ggsave("chap1_buffer_gaps_density_elevation_height_temp.png",dpi=300, dev='png', height=8, width=8, units="in")

impo resut - early morning 1.2 is much warmer , day time ground is warmer

All_site_withelevationheight <- All_site_withelevation %>% mutate(hcf= factor(height))
ap <- ggdensity(All_site_withelevationheight, x = "temp",
   add = "mean", rug = TRUE,
   color = "hcf") +
     theme_minimal(base_size=24) 
ap + labs(x=" Temperature (°C)", y="Density",color="Height (m)")

ggsave("chap1_buffer_gaps_density_height_profile.png",dpi=300, dev='png', height=8, width=8, units="in")

All_site_withelevationheight <- All_site_withelevation %>% mutate(hcf= factor(height))
ap <- ggdensity(All_site_withelevationheight, x = "temp",
   add = "mean", rug = TRUE,
   color = "hcf") +
     theme_minimal(base_size=24) 
ap + facet_grid(elevation ~ hcf, scales='free')

ggsave("chap1_buffer_gaps_density_height_elevation_profile.png",dpi=300, dev='png', height=8, width=8, units="in")

All_site_withelevationheight <- All_site_withelevation %>% mutate(hcf= factor(height))
ap <- ggdensity(All_site_withelevationheight,x = "temp",
   color = "canopy", rug = TRUE) +
     theme_minimal(base_size=24) 
ap + facet_grid(elevation ~ hcf, scales='free') + labs(x=" Temperature (°C)", y="Density",color="Canopy")

ggsave("chap1_buffer_gaps_density_height_elevation_profile.png",dpi=300, dev='png', height=8, width=12, units="in")