In liaaaaran/mcrcoral:

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(mcrcoral)
library(janitor)
library(ggplot2)
library(tidyverse)
library(dplyr)

# clean dataset using janitor package
mcr_coral <- mcrcoral::mcr_coral %>%
  clean_names() %>%
  select(-notes) %>%
  mutate(coral_genus = recode(
    coral_genus,
    "ACR" = "Acropora",
    "POC" = "Pocillopora"
  )) %>%
  mutate(region = recode(
    site,
    "LTER_1" = "North",
    "LTER_2" = "North",
    "LTER_3" = "SouthEast",
    "LTER_4" = "SouthEast",
    "LTER_5" = "SouthWest",
    "LTER_6" = "SouthWest"
  ),
  year = as.factor(year)) %>%
  na.omit()

Mean Live Coral Compared to Predictors

North shore has highest recovery rates (site 1&2), while East shore has lowest initial recovery rates (sites 3&4)

mcr_coral %>%
#  select(year, percent_alive) %>%
  group_by(year, site) %>%
  summarise(avg_live = mean(percent_alive, na.rm = TRUE)) %>%
  ungroup %>%
  ggplot(aes(year, avg_live)) +
  geom_point() + 
#  geom_path(aes(year, avg_live)) +
  facet_wrap(~site) +
#  geom_bar(stat = 'identity', fill = colors()[128]) + 
  scale_y_continuous() + 
  theme_bw() + 
  labs(title = "Average Percent of Live Coral Per Year", 
        x = "Year", 
        y = "Percentage of Live Coral")  

#annual_live

This plot further confirms what the above shows us: North shore has the highest recovery rates. NOTE: There may be biases due to inconsistencies in the observations collected.

# mcr_coral %>%
#   na.omit() %>%
#   filter(year == 2006 | year == 2011) %>%
# #  select(region, percent_alive) %>%
#   group_by(year, region) %>%
#   summarize(avg_live = mean(percent_alive, na.rm = TRUE)) %>%
#   ggplot(aes(region, avg_live)) +
# #  geom_point() +
#   geom_bar(stat = 'identity', fill = colors()[128]) + 
#   facet_wrap(~year) + 
#   scale_y_continuous() + 
#   theme_bw() + 
#   labs(title = "Percent Average of Live Coral By Region", 
#         x = "Region", 
#         y = "Average Live Coral")

Coral Footprint by Coral Genus

Plotted average area of different coral to observe how area was affected by cyclone. As we can see, Acropora remained about the same, while the Pocillopora. Both of these are branching corals, so maybe we can identify a difference in branch morphology.

genus <- mcr_coral %>%
  filter(!is.na(coral_genus), coral_genus != '?' , area_cm2 < 750) %>%
  ggplot(aes(x = year, y = area_cm2)) +
  geom_boxplot(aes(x = coral_genus, y = area_cm2, color = coral_genus), alpha = 0.8, width = 0.5) +
  theme_minimal() + 
#  facet_wrap(~year) +
  labs(title = "Coral Footprint by Coral Genus",
       y = "Area of Coral Footprint (cm)",
       x = "Coral Genus")
genus

mcr_coral1 <- mcr_coral1 %>%
  clean_names() %>%
  separate(date, sep="-", into = c("year", "month"))

Based on research, we know Pocillopora has the greatest recovery rate, and the most live coral. From the graph shown, we can see that there is almost a complete loss of coral life after the cyclone in 2010. The most rapid recovery seems to be the north shore (sites 1 & 2), with the least rapid recovery on the east shore (sites 3 & 4)

poc_coral <- mcr_coral1 %>%
  na.omit() %>%
  filter(taxonomy_substrate_functional_group == 'Pocillopora')

Recovery Rates of Coral Genus

Using a new dataset, we now can explore the recovery rates:: this data goes up till 2015. The trend still holds true: North shore has highest recovery rates, East shore has lowest recovery rates. This plot combines what the previous 2 plots showed us in a morecohesive manner, showing us how data visualization can be intuitive once domain context is applied.

rec_rate <- mcr_coral1 %>%
  na.omit() %>%
  filter(taxonomy_substrate_functional_group == 'Pocillopora' | taxonomy_substrate_functional_group == 'Acropora') %>%
  group_by(year, site, taxonomy_substrate_functional_group) %>%
  summarise(cover = mean(percent_cover)) %>%
#  ungroup %>%
  ggplot(aes(x=year, y = cover)) +
  geom_point(aes(col = taxonomy_substrate_functional_group)) +
#  geom_segment() +
  facet_wrap(~site) + 
  theme_bw() +
  labs(title = "Recovery Rate of Coral Genus by Site",
       ylab = "Percent of Coverage")
rec_rate
#rec_rate + theme(legend.title = "Coral Genus")