Chapter 4 Weight distribution among groups

calotriton_resp1 %>%
  filter(time_point=="0")%>%
  ggplot(aes(x = population, y = weight, color=temperature, shape=temperature)) +
   geom_boxplot(outlier.shape = NA, position = position_dodge(width = 0.75)) +
    geom_jitter(position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.75), alpha = 0.8)+
     scale_color_manual(name="temperature",
          breaks=c("14","20"),
          values=c('#4477AA', "#ff0000"))+
    theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1))+
    stat_compare_means(size=3,vjust = 0.8, hjust=1 )+
    theme(legend.position="right")+
    labs(x = "Population", y= "Weight (g)")

weight_subset<-calotriton_resp1 %>%
  filter(time_point=="0")

#Overall
weight_subset$weight %>% mean()

[1] 6.22125

weight_subset$weight %>% sd()

[1] 1.806429

#In high elevation newts
weight_subset_high<-weight_subset %>% 
  filter(elevation=="High")

weight_subset_high$weight %>% mean()

[1] 7.04

weight_subset_high$weight %>% sd()

[1] 1.212436

#In low elevation newts
weight_subset_low<-weight_subset %>% 
  filter(elevation=="Low")

weight_subset_low$weight %>% mean()

[1] 5.4025

weight_subset_low$weight %>% sd()

[1] 2.087221

4.1 Metabolic scaling at the acclimation time-point

ggplot(weight_subset, aes(weight,o2, color=population, shape=temperature)) + 
  geom_point() +
  geom_smooth(method = lm, formula = y ~ x)+
    stat_poly_eq()+
    labs(x = "Weight (g)", y= "O2 consumption (ml/s)")

4.1.1 High elevation newts

ggplot(weight_subset_high, aes(weight ,o2, shape=temperature)) + 
  geom_point() +
  geom_smooth(method = lm, formula = y ~ x)+
    stat_poly_eq()+
    labs(x = "Weight (g)", y= "O2 consumption (ml/s)")

4.1.2 Low elevation newts

ggplot(weight_subset_low, aes(weight ,o2, shape=temperature)) + 
  geom_point() +
  geom_smooth(method = lm, formula = y ~ x)+
    stat_poly_eq()+
    labs(x = "Weight (g)", y= "O2 consumption (ml/s)")

Warning in qt((1 - level)/2, df): NaNs produced
Warning in qt((1 - level)/2, df): NaNs produced

Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning -Inf
Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning -Inf

4.2 Metabolic scaling at each group and time-point

ggplot(calotriton_resp1, aes(weight ,o2)) + 
  geom_point() +
  facet_grid(temperature~ elevation+factor(population)+time_point)+
  geom_smooth(method = lm, formula = y ~ x)+
    stat_poly_eq()+
    labs(x = "Weight (g)", y= "O2 consumption (ml/s)")

#Acclimation oxygen consumption per population

calotriton_resp1 %>%
  filter(time_point=="0")%>%
  ggplot(aes(x = population, y = o2_normalized, color=temperature, shape=elevation)) +
   geom_boxplot(outlier.shape = NA, position = position_dodge(width = 0.75)) +
    geom_jitter(position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.75), alpha = 0.8)+
     scale_color_manual(name="temperature",
          breaks=c("14","20"),
          values=c('#4477AA', "#ff0000"))+
    theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1))+
    stat_compare_means(size=3,vjust = 0.8, hjust=1 )+
    
    theme(legend.position="right")+
    labs(x = "Population", y= "Normalized o2 consumption (ml/s/g)")

#Acclimation oxygen consumption per elevation

calotriton_resp1 %>%
  filter(time_point == "0") %>%
  ggplot(aes(x = elevation, y = o2_normalized, colour = temperature)) +
    geom_boxplot(outlier.shape = NA, position = position_dodge(width = 0.75)) +
    geom_jitter(position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.75), alpha = 0.8) +
    scale_color_manual(
      name = "Temperature",
      breaks = c("14", "20"),
      values = c('#4477AA', "#ff0000")
    ) +
    theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust = 1)) +
    stat_compare_means(size = 3, vjust = 0.8, hjust = 1) +
    theme(legend.position = "right") +
    labs(x = "Population", y = "Normalized o2 consumption (ml/s/g)")

#Acclimation oxygen consumption per temperature

calotriton_resp1 %>%
  filter(time_point=="0")%>%
  ggplot(aes(x = temperature, y = o2_normalized, colour=population)) +
  geom_boxplot(outlier.shape = NA, position = position_dodge(width = 0.75)) +
    geom_jitter(position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.75), alpha = 0.8)+
    theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1))+
    stat_compare_means(size=3,vjust = 0.8, hjust=1 )+
    theme(legend.position="right")+
    labs(x = "Population", y= "Normalized o2 consumption (ml/s/g)")