Preliminaries

Load libraries

library(knitr)       #for R Markdown
library(MASS)        #for negative binomial glm (glm.nb)
library(lme4)        #for mixed models
library(emmeans)     #for posthoc
library(car)         #for Anova
library(survival)    #for survival analysis
library(coxme)
library(rptR)        #for repeatability analysis
library(MuMIn)       #for model selection (dredge)
library(ggfortify)   #for plotting survival analysis
library(ggsignif)    #for labeling significance in ggplots
library(tidyverse)   #for data processing, put last to avoid function masking

Data processing

Read dataset, correct variable types, and derive variables.

data_raw <- read.csv("data_personality_openfield_071825.csv",                 
                 # to avoid reading errors
                 fileEncoding="UTF-8-BOM", na.strings = c("","N/A","#VALUE!"))

data <- data_raw %>%
  #clean up some entries for easier analysis
  mutate(gosner_stage = ifelse(gosner_stage=="45/46", 46, as.integer(gosner_stage))) %>%
  mutate(tadpole_ID = factor(paste0("T", tadpole_ID), levels = paste0("T", sort(unique(tadpole_ID))))) %>%
  
  #correct variable types
  mutate_at(c("tadpole_ID","morph"), as.factor) %>%
  mutate_at(c("found_date","testing_date"),  lubridate::mdy) %>%
  mutate_at(c("total_time_frozen_hh_mm_ss", "invisible_time_hh_mm_ss"),lubridate::hms) %>%
  
  #derive variables
  mutate(condition = body_width_mm/length_without_tail_mm,
         frozen_sec = as.numeric(total_time_frozen_hh_mm_ss),
         invisible_sec = as.numeric(invisible_time_hh_mm_ss),
         age = testing_date - found_date) %>%
  
  #add trial sequence per tadpole
  arrange(tadpole_ID, week, replicate) %>%
  group_by(tadpole_ID) %>%
  mutate(trial_seq = row_number()) %>%
  ungroup()

Make a dataset with individual averages by week

data_wk_avg <- data %>%
  group_by(tadpole_ID, week) %>%
  summarise(avg_speed_mm = mean(avg_speed_mm),
            mob_avg_speed_mm = mean(mob_avg_speed_mm),
            mobility_rate = mean(mobility_rate),
            exploration_rate_explored_div_by_total = mean(exploration_rate_explored_div_by_total),
            total_distance_mm = mean(total_distance_mm)
  )

Data spread:

data %>% select(tadpole_ID, week) %>% table
##           week
## tadpole_ID 1 2 3 4 5 6 7
##        T5  4 4 4 4 4 0 0
##        T6  4 4 4 4 4 0 0
##        T7  4 4 4 4 4 0 0
##        T8  4 4 4 4 4 4 4
##        T9  4 4 4 4 4 0 0
##        T10 4 4 4 4 0 0 0
##        T11 4 4 4 4 0 0 0
##        T12 4 4 4 4 0 0 0
##        T13 4 4 4 4 0 0 0
##        T14 4 4 4 4 0 0 0
##        T15 4 4 4 4 0 0 0
##        T16 4 4 4 4 0 0 0
##        T17 4 4 0 0 0 0 0
data %>% select(tadpole_ID, gosner_stage) %>% table
##           gosner_stage
## tadpole_ID 26 30 31 36 37 41 42 46
##        T5   3 16  0  0  0  0  0  0
##        T6   3  0  0  4  0  2  2  8
##        T7   4  4  0  0  0  4  4  4
##        T8   8 16  4  0  0  0  0  0
##        T9   4  4  0  0  0  4  4  4
##        T10  0  4  2  0  2  4  0  4
##        T11  0  8  2  0  0  2  4  0
##        T12  0  6  2  0  2  0  6  0
##        T13  0 16  0  0  0  0  0  0
##        T14  0  6  2  0  0  4  4  0
##        T15  4  4  0  0  0  4  4  0
##        T16  4  4  0  0  0  6  2  0
##        T17  4  2  0  0  0  0  0  0

Comprehensive boxplots

Average speed

ggplot(data,
       aes(x = tadpole_ID,
           y = avg_speed_mm,
           fill = tadpole_ID) )+
  geom_boxplot(aes(alpha = as.factor(week)), position = position_dodge(width = 0.8)) +
  geom_point(aes(group = as.factor(week)),
    position = position_dodge(width = 0.8),
             alpha = 0.5, size = 1) +
  
  scale_fill_manual(values = hcl.colors(13, palette = "Dark3"), guide = "none") +
  scale_alpha_manual(
    values = c("1" = 1, "2" = 0.8, "3" = 0.6, "4" = 0.4, "5" = 0.2, "6" = 0),
    name = "Week",
    guide = guide_legend(override.aes = list(fill = "grey40"))  # make legend boxes visible
    )+
  
  labs(x = "Tadpole ID", y = "Average Speed (mm/sec)", alpha = "Week") +
  theme_classic(base_size = 20) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Average speed when moving

ggplot(data,
       aes(x = tadpole_ID,
           y = mob_avg_speed_mm,
           fill = tadpole_ID) )+
  geom_boxplot(aes(alpha = as.factor(week)), position = position_dodge(width = 0.8)) +
  geom_point(aes(group = as.factor(week)),
    position = position_dodge(width = 0.8),
             alpha = 0.5, size = 1) +
  
  scale_fill_manual(values = hcl.colors(13, palette = "Dark3"), guide = "none") +
  scale_alpha_manual(
    values = c("1" = 1, "2" = 0.8, "3" = 0.6, "4" = 0.4, "5" = 0.2, "6" = 0),
    name = "Week",
    guide = guide_legend(override.aes = list(fill = "grey40"))  # make legend boxes visible
    )+
  
  labs(x = "Tadpole ID", y = "Average Speed when moving (mm/sec)", alpha = "Week") +
  theme_classic(base_size = 20) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Proportion time moving

ggplot(data,
       aes(x = tadpole_ID,
           y = mobility_rate,
           fill = tadpole_ID) )+
  geom_boxplot(aes(alpha = as.factor(week)), position = position_dodge(width = 0.8)) +
  geom_point(aes(group = as.factor(week)),
    position = position_dodge(width = 0.8),
             alpha = 0.5, size = 1) +
  
  scale_fill_manual(values = hcl.colors(13, palette = "Dark3"), guide = "none") +
  scale_alpha_manual(
    values = c("1" = 1, "2" = 0.8, "3" = 0.6, "4" = 0.4, "5" = 0.2, "6" = 0),
    name = "Week",
    guide = guide_legend(override.aes = list(fill = "grey40"))  # make legend boxes visible
    )+
  scale_y_continuous(labels = scales::percent) +
  labs(x = "Tadpole ID", y = "Proportion time moving (%)", alpha = "Week") +
  theme_classic(base_size = 20) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Proportion area explored

ggplot(data,
       aes(x = tadpole_ID,
           y = exploration_rate_explored_div_by_total,
           fill = tadpole_ID) )+
  geom_boxplot(aes(alpha = as.factor(week)), position = position_dodge(width = 0.8)) +
  geom_point(aes(group = as.factor(week)),
    position = position_dodge(width = 0.8),
             alpha = 0.5, size = 1) +
  
  scale_fill_manual(values = hcl.colors(13, palette = "Dark3"), guide = "none") +
  scale_alpha_manual(
    values = c("1" = 1, "2" = 0.8, "3" = 0.6, "4" = 0.4, "5" = 0.2, "6" = 0),
    name = "Week",
    guide = guide_legend(override.aes = list(fill = "grey40"))  # make legend boxes visible
    )+
  scale_y_continuous(labels = scales::percent) +
  labs(x = "Tadpole ID", y = "Proportion area explored (%)", alpha = "Week") +
  theme_classic(base_size = 20) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Total distance traveled

ggplot(data,
       aes(x = tadpole_ID,
           y = total_distance_mm,
           fill = tadpole_ID) )+
  geom_boxplot(aes(alpha = as.factor(week)), position = position_dodge(width = 0.8)) +
  geom_point(aes(group = as.factor(week)),
    position = position_dodge(width = 0.8),
             alpha = 0.5, size = 1) +
  
  scale_fill_manual(values = hcl.colors(13, palette = "Dark3"), guide = "none") +
  scale_alpha_manual(
    values = c("1" = 1, "2" = 0.8, "3" = 0.6, "4" = 0.4, "5" = 0.2, "6" = 0),
    name = "Week",
    guide = guide_legend(override.aes = list(fill = "grey40"))  # make legend boxes visible
    )+
  
  labs(x = "Tadpole ID", y = "Total distance traveled (mm)", alpha = "Week") +
  theme_classic(base_size = 20) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Repeatability within each week

make a function to calculate repeatability of each week and combine to a data frame

rpt_by_wk <- function(data, var) {
  
  data %>%
    filter(week %in% 1:5) %>%
    
    group_by(week) %>%
    
    group_map(~ { #apply to each group
      rpt_result <- rpt(
        reformulate("1 | tadpole_ID", response = var),
        grname = "tadpole_ID",
        data = .x,
        datatype = "Gaussian",
        nboot = 1000,
        npermut = 1000)
      rpt_summary <- data.frame(week = .y$week, summary(rpt_result)$rpt, row.names = NULL)
      return(rpt_summary)
      }) %>%
    
    bind_rows()
}

Average speed

rpt_avg_speed <- rpt_by_wk(data, "avg_speed_mm")
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rpt_avg_speed %>% kable(digit = 3)
week R SE X2.5. X97.5. P_permut LRT_P
1 0.324 0.163 0.000 0.608 0.024 1
2 0.499 0.156 0.121 0.727 0.001 1
3 0.675 0.132 0.318 0.838 0.001 1
4 0.940 0.039 0.819 0.974 0.001 1
5 0.949 0.092 0.652 0.985 0.001 1

Average speed when moving

rpt_avg_mobspeed <- rpt_by_wk(data, "mob_avg_speed_mm")
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rpt_avg_mobspeed %>% kable(digit = 3)
week R SE X2.5. X97.5. P_permut LRT_P
1 0.507 0.158 0.122 0.721 0.001 1
2 0.427 0.160 0.079 0.676 0.002 1
3 0.485 0.153 0.126 0.713 0.001 1
4 0.883 0.067 0.698 0.943 0.001 1
5 0.905 0.143 0.429 0.973 0.001 1

Proportion time moving

rpt_avg_mobprop <- rpt_by_wk(data, "mobility_rate")
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rpt_avg_mobprop %>% kable(digit = 3)
week R SE X2.5. X97.5. P_permut LRT_P
1 0.647 0.138 0.298 0.824 0.001 0
2 0.542 0.152 0.161 0.760 0.001 0
3 0.861 0.070 0.675 0.935 0.001 0
4 0.807 0.100 0.527 0.912 0.001 0
5 0.792 0.197 0.173 0.937 0.001 0

Proportion area explored

rpt_exploration_prop <- rpt_by_wk(data, "exploration_rate_explored_div_by_total")
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rpt_exploration_prop  %>% kable(digit = 3)
week R SE X2.5. X97.5. P_permut LRT_P
1 0.829 0.094 0.540 0.922 0.001 0.000
2 0.384 0.163 0.013 0.660 0.005 0.000
3 0.551 0.154 0.185 0.755 0.001 0.017
4 0.833 0.088 0.587 0.933 0.001 0.000
5 0.959 0.081 0.738 0.988 0.001 0.000

Total distance traveled

rpt_dist <- rpt_by_wk(data, "total_distance_mm")
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rpt_dist %>% kable(digit = 3)
week R SE X2.5. X97.5. P_permut LRT_P
1 0.322 0.162 0.000 0.602 0.012 1
2 0.501 0.159 0.118 0.742 0.001 1
3 0.679 0.125 0.350 0.836 0.001 1
4 0.939 0.039 0.827 0.975 0.001 1
5 0.945 0.085 0.672 0.986 0.001 1

Figure: repeatability changes over time

combine all results in to one dataframe

rpt_all <- bind_rows(
  mutate(rpt_avg_speed, variable = "Average speed"),
  mutate(rpt_avg_mobspeed, variable = "Speed when moving"),
  mutate(rpt_avg_mobprop, variable = "Proportion time moving"),
  mutate(rpt_exploration_prop, variable = "Proportion area explored"),
  mutate(rpt_dist, variable = "Total distance traveled")
)
ggplot(rpt_all, aes(x = week, y = R, color = variable)) +
  geom_line(size = 1, position = position_dodge(width = 0.3)) +
  geom_point(size = 2, position = position_dodge(width = 0.3)) +
  geom_errorbar(aes(ymin = R - SE, ymax = R + SE), 
                width = 0.3, alpha = 0.5, size = 0.8,
                position = position_dodge(width = 0.3)) +
  scale_color_manual(values = hcl.colors(6, palette = "Mako"))+
  labs(x = "Week", y = "Repeatability (R)", color = "Behavior") +
  theme_classic(base_size = 14)

ggsave("graph.png", width = 8, height = 4, dpi = 300)

Repeatability across development

make function for repeatability of different variables

rpt_multi_vars <- function(data, vars) {
  
  vars %>%
    purrr::map_df(~ {   #map + row binding
      rpt_result <- rpt(
        reformulate("1 | tadpole_ID", response = .x),
        grname = "tadpole_ID",
        data = data,
        datatype = "Gaussian",
        nboot = 1000,
        npermut = 1000
      )
      
      rpt_summary <- data.frame(variable = .x,
                                summary(rpt_result)$rpt,
                                row.names = NULL)
      
      return(rpt_summary)
    })
}

Analysis: overall repeatability

rpt_overall_summary <- rpt_multi_vars(data, c("avg_speed_mm", "mob_avg_speed_mm","mobility_rate","exploration_rate_explored_div_by_total","total_distance_mm"))
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rpt_overall_summary %>% kable(digit = 3)
variable R SE X2.5. X97.5. P_permut LRT_P
avg_speed_mm 0.349 0.101 0.144 0.529 0.001 0
mob_avg_speed_mm 0.315 0.102 0.112 0.496 0.001 0
mobility_rate 0.334 0.102 0.124 0.525 0.001 0
exploration_rate_explored_div_by_total 0.305 0.102 0.116 0.494 0.001 0
total_distance_mm 0.348 0.102 0.136 0.543 0.001 0

Analysis: week averages

rpt_wk_avg_summary <- rpt_multi_vars(data_wk_avg, c("avg_speed_mm", "mob_avg_speed_mm","mobility_rate","exploration_rate_explored_div_by_total","total_distance_mm"))
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rpt_wk_avg_summary %>% kable(digit = 3)
variable R SE X2.5. X97.5. P_permut LRT_P
avg_speed_mm 0.380 0.157 0.037 0.654 0.003 0.003
mob_avg_speed_mm 0.319 0.154 0.000 0.590 0.005 0.010
mobility_rate 0.278 0.145 0.000 0.539 0.011 0.020
exploration_rate_explored_div_by_total 0.219 0.144 0.000 0.519 0.044 0.045
total_distance_mm 0.380 0.159 0.028 0.652 0.004 0.003

Figure: Spaghetti plots

fig_spg_speed <- 
  ggplot(data_wk_avg, 
       aes(x = week, y = avg_speed_mm, group = tadpole_ID, color = tadpole_ID)) +
  geom_line(alpha = 0.9, size = 1) +
  labs(x = "Week", y = "Average Speed (mm/sec)") +
  scale_color_manual(values = hcl.colors(15, palette = "Mako"),
                     name = "tadpole_ID") +
  theme_classic(base_size = 15)

fig_spg_speed_mov <- 
  ggplot(data_wk_avg, 
       aes(x = week, y = mob_avg_speed_mm, group = tadpole_ID, color = tadpole_ID)) +
  geom_line(alpha = 0.9, size = 1) +
  labs(x = "Week", y = "Average Speed when moving (mm/sec)") +
  scale_color_manual(values = hcl.colors(15, palette = "Mako"),
                     guide = "none") +
  theme(legend.position = "none") + 
  theme_classic(base_size = 15)

fig_spg_mob_rate <- 
  ggplot(data_wk_avg, 
       aes(x = week, y = mobility_rate, group = tadpole_ID, color = tadpole_ID)) +
  geom_line(alpha = 0.9, size = 1) +
  labs(x = "Week", y = "Proportion time moving (%)") +
  scale_color_manual(values = hcl.colors(15, palette = "Mako"),
                     guide = "none") +
  scale_y_continuous(labels = scales::percent) +
  theme(legend.position = "none") + 
  theme_classic(base_size = 15)

fig_spg_area_explored <- 
  ggplot(data_wk_avg, 
       aes(x = week, y = exploration_rate_explored_div_by_total, group = tadpole_ID, color = tadpole_ID)) +
  geom_line(alpha = 0.9, size = 1) +
  labs(x = "Week", y = "Proportion area explored (%)") +
  scale_color_manual(values = hcl.colors(15, palette = "Mako"),
                     guide = "none") +
  scale_y_continuous(labels = scales::percent) +
  theme(legend.position = "none") + 
  theme_classic(base_size = 15)

fig_spg_dist <- 
  ggplot(data_wk_avg, 
       aes(x = week, y = total_distance_mm, group = tadpole_ID, color = tadpole_ID)) +
  geom_line(alpha = 0.9, size = 1) +
  labs(x = "Week", y = "Total distance traveled (mm)") +
  scale_color_manual(values = hcl.colors(15, palette = "Mako"), 
                     guide = "none") +
  theme(legend.position = "none") + 
  theme_classic(base_size = 15)
egg::ggarrange(fig_spg_speed, fig_spg_speed_mov, fig_spg_mob_rate, fig_spg_area_explored, fig_spg_dist,
               nrow = 5)

Figure: averages

fig_avg_speed <- 
  data_wk_avg %>% 
  filter(week %in% 1:5) %>%
  ggplot(aes(x = as.factor(week), y = avg_speed_mm, group = tadpole_ID)) +
  geom_line(color = "grey80", size = 1, alpha = 0.8) +
  stat_summary(aes(group = 1), fun = mean, geom = "line", color = "#0072B2", size = 1.5) +
  stat_summary(aes(group = 1), fun = mean, geom = "point", color = "#0072B2", size = 2.5) +
  stat_summary(aes(group = 1), fun.data = mean_se, geom = "errorbar", color = "#0072B2", width = 0.2) +
  labs(x = "Week", y = "Average Speed (mm/sec)") +
  theme_classic(base_size = 15)

fig_avg_speed_mov <- 
  data_wk_avg %>% 
  filter(week %in% 1:5) %>%
  ggplot(aes(x = as.factor(week), y = mob_avg_speed_mm, group = tadpole_ID)) +
  geom_line(color = "grey80", size = 1, alpha = 0.8) +
  stat_summary(aes(group = 1), fun = mean, geom = "line", color = "#0072B2", size = 1.5) +
  stat_summary(aes(group = 1), fun = mean, geom = "point", color = "#0072B2", size = 2.5) +
  stat_summary(aes(group = 1), fun.data = mean_se, geom = "errorbar", color = "#0072B2", width = 0.2) +
  labs(x = "Week", y = "Average Speed when moving (mm/sec)") +
  theme_classic(base_size = 15)

fig_avg_mob_rate <- 
  data_wk_avg %>% 
  filter(week %in% 1:5) %>%
  ggplot(aes(x = as.factor(week), y = mobility_rate, group = tadpole_ID)) +
  geom_line(color = "grey80", size = 1, alpha = 0.8) +
  stat_summary(aes(group = 1), fun = mean, geom = "line", color = "#0072B2", size = 1.5) +
  stat_summary(aes(group = 1), fun = mean, geom = "point", color = "#0072B2", size = 2.5) +
  stat_summary(aes(group = 1), fun.data = mean_se, geom = "errorbar", color = "#0072B2", width = 0.2) +
  labs(x = "Week", y = "Proportion time moving (%)") +
  scale_y_continuous(labels = scales::percent) +
  theme_classic(base_size = 15)

fig_avg_area_explored <- 
  data_wk_avg %>% 
  filter(week %in% 1:5) %>%
  ggplot(aes(x = as.factor(week), y = exploration_rate_explored_div_by_total, group = tadpole_ID)) +
  geom_line(color = "grey80", size = 1, alpha = 0.8) +
  stat_summary(aes(group = 1), fun = mean, geom = "line", color = "#0072B2", size = 1.5) +
  stat_summary(aes(group = 1), fun = mean, geom = "point", color = "#0072B2", size = 2.5) +
  stat_summary(aes(group = 1), fun.data = mean_se, geom = "errorbar", color = "#0072B2", width = 0.2) +
  labs(x = "Week", y = "Proportion area explored (%)") +
  scale_y_continuous(labels = scales::percent) +
  theme_classic(base_size = 15)

fig_avg_dist <- 
  data_wk_avg %>% 
  filter(week %in% 1:5) %>%
  ggplot(aes(x = as.factor(week), y = total_distance_mm, group = tadpole_ID)) +
  geom_line(color = "grey80", size = 1, alpha = 0.8) +
  stat_summary(aes(group = 1), fun = mean, geom = "line", color = "#0072B2", size = 1.5) +
  stat_summary(aes(group = 1), fun = mean, geom = "point", color = "#0072B2", size = 2.5) +
  stat_summary(aes(group = 1), fun.data = mean_se, geom = "errorbar", color = "#0072B2", width = 0.2) +
  labs(x = "Week", y = "Total distance traveled (mm)") +
  theme_classic(base_size = 15)
egg::ggarrange(fig_avg_speed, fig_avg_speed_mov, fig_avg_mob_rate, fig_avg_area_explored, fig_avg_dist,
               nrow = 5)

Just the two parameters used in the poster

egg::ggarrange(fig_avg_speed,  fig_avg_area_explored,
               nrow = 2)

Figure: trial sequence averages

Just for fun/exploration of graphing

  data %>%
  filter(trial_seq %in% 1:20) %>%
  ggplot(aes(x = as.factor(trial_seq), y = avg_speed_mm, group = tadpole_ID)) +
  geom_line(color = "grey80", size = 1, alpha = 0.8) +
  stat_summary(aes(group = 1), fun = mean, geom = "line", color = "#0072B2", size = 1.5) +
  stat_summary(aes(group = 1), fun = mean, geom = "point", color = "#0072B2", size = 2.5) +
  stat_summary(aes(group = 1), fun.data = mean_se, geom = "errorbar", color = "#0072B2", width = 0.2) +
  labs(x = "Trial number", y = "Average Speed (mm/sec)") +
  theme_classic(base_size = 15)

Developmental changes

Do behavioral tendencies change as tadpoles develop?

Short Summary

  • no changing tendencies for moving speed and distance traveled
  • as tadpoles develop, they spent less time moving and explore overall less area

Average speed

mod <- lmer(avg_speed_mm ~ week + (1 | tadpole_ID), data = data)
summary(mod)$coefficients %>% kable(digits = 3)
Estimate Std. Error t value
(Intercept) 9.079 1.217 7.460
week -0.778 0.253 -3.069 
Anova(mod) %>% kable(digits = 3)
Chisq Df Pr(>Chisq)
week 9.42 1 0.002

Average speed when moving

mod <- lmer(mob_avg_speed_mm ~ week + (1 | tadpole_ID), data = data)
summary(mod)$coefficients %>% kable(digits = 3)
Estimate Std. Error t value
(Intercept) 12.619 1.214 10.398
week -0.896 0.265 -3.381 
Anova(mod) %>% kable(digits = 3)
Chisq Df Pr(>Chisq)
week 11.432 1 0.001

Proportion time moving

mod <- lmer(mobility_rate ~ week + (1 | tadpole_ID), data = data)
summary(mod)$coefficients %>% kable(digits = 3)
Estimate Std. Error t value
(Intercept) 0.723 0.049 14.652
week -0.050 0.010 -4.937 
Anova(mod) %>% kable(digits = 3)
Chisq Df Pr(>Chisq)
week 24.373 1 0

Proportion area explored

mod <- lmer(exploration_rate_explored_div_by_total ~ week + (1 | tadpole_ID), data = data)
summary(mod)$coefficients %>% kable(digits = 3)
Estimate Std. Error t value
(Intercept) 0.711 0.047 15.173
week -0.057 0.010 -5.888 
Anova(mod) %>% kable(digits = 3)
Chisq Df Pr(>Chisq)
week 34.666 1 0

Total distance traveled

mod <- lmer(total_distance_mm ~ week + (1 | tadpole_ID), data = data)
summary(mod)$coefficients %>% kable(digits = 3)
Estimate Std. Error t value
(Intercept) 5514.262 734.515 7.507
week -462.670 153.154 -3.021 
Anova(mod) %>% kable(digits = 3)
Chisq Df Pr(>Chisq)
week 9.126 1 0.003

Developmental rate

ggplot(data, 
       aes(x = age, y = gosner_stage, group = tadpole_ID, color = tadpole_ID)) +
  geom_line(alpha = 0.9, size = 1) +
  labs(x = "Age (days)", y = "Gosner stage") +
  scale_color_manual(values = hcl.colors(13, palette = "Mako")) +
  theme_classic(base_size = 15)