coo <- kah %>%
group_by(disposition, zone) %>%
filter(disposition == "COO") %>%
add_count(disposition) %>%
mutate(avgCOO = mean(n))
coo <- coo %>%
group_by(survey_month, zone) %>%
mutate(sqm = n_distinct(site_id)*10,
coosqm= n() / sqm
) %>%
ungroup()
levels(coo$zone) <- c("Control Zone", "Restoration Zone")
COO_year = coo %>%
ungroup() %>%
count(survey_month, zone) %>%
mutate(survey_month = factor(month.abb[survey_month], levels = month.abb)) %>%
ggplot(aes(x = survey_month, y = n, fill = zone)) +
geom_col(position = "dodge") +
scale_fill_manual(values = zoneCols) +
labs(x = "Month", y = "COO Count", fill = "Zone")
coosqmPlot = coo %>%
ungroup() %>%
mutate(survey_month = factor(month.abb[survey_month], levels = month.abb)) %>% #makes the survey month turn into words on the plot
ggplot(aes(x = survey_month, y = coosqm, fill = zone)) + #these are the values the plot will use
geom_col(position = "dodge") + #makes the plot a bar graph
geom_text(aes(label = round(coosqm, 2)),
position = position_dodge(width = 0.9),
vjust = -0.5, size = 3) + # this adds the numbers above each of the bars
scale_fill_manual(values = zoneCols) + #colors the bars with our colors in the setup chunk
labs(x = "Month", y = "# of COO per square meter", fill = "Zone") + #gives the bar graph labels
scale_y_continuous(limits = c(-0.01, 1.2),
breaks = seq(0, 1.2, by = 0.25)) #changes the bounds of the graph
coo_z2 <- coo %>%
filter(zone == "Control Zone")
coo_z5 <- coo %>%
filter(zone == "Restoration Zone")
spCOO_z2 = coo_z2 %>%
ungroup() %>%
count(survey_month, live_tissue_code) %>%
mutate(survey_month = factor(month.abb[survey_month], levels = month.abb)) %>%
ggplot(aes(x = survey_month, y = n, color = live_tissue_code)) +
geom_point(size = 4) +
scale_color_manual(values = healthCols, labels = healthLabs) +
labs(x = "Month", y = "# of COO", color = "Live Tissue Code")
spCOO_z5 = coo_z5 %>%
ungroup() %>%
count(survey_month, live_tissue_code) %>%
mutate(survey_month = factor(month.abb[survey_month], levels = month.abb)) %>%
ggplot(aes(x = survey_month, y = n, color = live_tissue_code)) +
geom_point(size = 4) +
scale_color_manual(values = healthCols, labels = healthLabs) +
labs(x = "Month", y = "# of COO", color = "Live Tissue Code")
kah_clean <- kah %>%
drop_na(health, size_cm)
health <- kah_clean %>%
mutate(health = factor(health, levels = 5:0)) %>%
ggplot(aes(x = health, y = size_cm, fill = health)) +
geom_boxplot() +
labs(x = "Health Score", y = "Size (cm)") +
scale_y_continuous(limits = c(-5, 150),
breaks = seq(0, 150, by = 10))
kruskal.test(size_cm ~ factor(health), data = kah_clean) #with a p-value that is lower than 0.05, this tells us that size significantly differs across the amount of health problems
##
## Kruskal-Wallis rank sum test
##
## data: size_cm by factor(health)
## Kruskal-Wallis chi-squared = 584.17, df = 5, p-value < 2.2e-16
kah_clean %>%
dunn_test(size_cm ~ health, p.adjust.method = "bonferroni") #this tells us that generally the smaller the coral, the healthier it is. And as the corals get larger in size, they develop more health problems.
## # A tibble: 15 × 9
## .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 size_cm 0 1 2583 1860 13.4 6.49e-41 9.73e-40 ****
## 2 size_cm 0 2 2583 978 20.9 5.95e-97 8.93e-96 ****
## 3 size_cm 0 3 2583 264 13.3 1.20e-40 1.79e-39 ****
## 4 size_cm 0 4 2583 50 6.32 2.68e-10 4.02e- 9 ****
## 5 size_cm 0 5 2583 1 0.0572 9.54e- 1 1 e+ 0 ns
## 6 size_cm 1 2 1860 978 9.55 1.31e-21 1.97e-20 ****
## 7 size_cm 1 3 1860 264 6.92 4.47e-12 6.70e-11 ****
## 8 size_cm 1 4 1860 50 3.45 5.59e- 4 8.39e- 3 **
## 9 size_cm 1 5 1860 1 -0.350 7.26e- 1 1 e+ 0 ns
## 10 size_cm 2 3 978 264 1.13 2.60e- 1 1 e+ 0 ns
## 11 size_cm 2 4 978 50 0.809 4.18e- 1 1 e+ 0 ns
## 12 size_cm 2 5 978 1 -0.727 4.67e- 1 1 e+ 0 ns
## 13 size_cm 3 4 264 50 0.255 7.99e- 1 1 e+ 0 ns
## 14 size_cm 3 5 264 1 -0.804 4.21e- 1 1 e+ 0 ns
## 15 size_cm 4 5 50 1 -0.836 4.03e- 1 1 e+ 0 ns
kah %>%
drop_na(outplantable_substrate) %>%
leaflet() %>%
addProviderTiles(providers$Esri.WorldImagery) %>%
addPolygons(
lng = c(-155.96863, -155.96794, -155.9681, -155.96871),
lat = c(19.58052, 19.58043, 19.57979, 19.57986 ),
color = "#B084CC", fill = FALSE, weight = 4,
label = "Control Zone"
) %>%
addPolygons(
lng = c(-155.96871, -155.96893, -155.96829, -155.9681),
lat = c(19.57986, 19.57923, 19.57913, 19.57979),
color = "#190933", fill = FALSE, weight = 4,
label = "Restoration Zone"
) %>%
addHeatmap(
lng = ~long, lat = ~lat,
intensity = ~outplantable_substrate,
blur = 25, max = 95, radius = 15
)
pal <- colorNumeric("YlOrRd", domain = kah$outplantable_substrate, na.color = "transparent")
kah %>%
drop_na(outplantable_substrate) %>%
leaflet() %>%
addProviderTiles(providers$Esri.WorldImagery) %>%
addCircleMarkers(
lng = ~long, lat = ~lat,
color = ~pal(outplantable_substrate),
radius = 5, stroke = FALSE, fillOpacity = 0.8,
popup = ~paste("Coordinates:", lat, long, "<br>",
"Outplantable Substrate:", outplantable_substrate, "%")
) %>%
addLegend(pal = pal, values = ~outplantable_substrate,
title = "Outplantable<br>Substrate (%)")