2025_WCP_site_data_exploration
2025-07-17
EXPLORING WCP DATA ONLY
Importing 2025 Data
See the full code for the break down for the importing and cleaning open the documents of the same name.
Create the R files if needed.
First, I need to retrieve only the R code from the cleaning files. These files also include text descriptions of what cleaning is done, which is not needed for importing the code to do the data cleaning here.
# purl("cleaning_2025_health_form_data.Rmd",
# output =
# paste("purl/cleaning_2025_health_form_data",
# Sys.Date(),".R"))Run the R files to get cleaned data
Next, we can run those extracted R files to actually import and clean the data.
## 2025 data
source(paste("purl/cleaning_2025_health_form_data", Sys.Date(), ".R"))Select only the WCP data
health_assess_2025 <- health_assess_2025 %>% slice(1:66)Data exploration
- Need to include girdled circum
Proportion of seedlings
- NAs are from the first few days where we didn’t include the seedling question yet; we could adjust these
bar_seedlings <- health_assess_2025 %>% ggplot(aes(x = seedling_y_n)) +
geom_bar(aes(fill = seedling_y_n)) +
xlab("2025 WCP Seedlings Y/N")
bar_seedlings
Plant Height
** Would love to fix the seedling/adult coloring
All individual distribution
library(patchwork)
# Plant Height (ft)
mean_plant_height <- mean(health_assess_2025$plant_height_ft, na.rm = TRUE)
median_plant_height <- median(health_assess_2025$plant_height_ft, na.rm = TRUE)
print(mean_plant_height)## [1] 7.333965hist_plant_height <- health_assess_2025 %>% ggplot(aes(x = health_assess_2025$plant_height_ft)) +
geom_histogram(bins = 30, aes(fill = seedling_y_n)) +
xlab("2025 WCP All Individuals")
ylab("Number of Individuals") +
# Mean
geom_vline(
aes(xintercept = mean_plant_height),
color = "#bdbdbd",
linetype = "dashed",
size = 1
) +
annotate("text",
color = "#bdbdbd",
x = 17,
y = 15,
label = paste("Mean =", round(mean_plant_height, 2), "ft")) +
# Median
geom_vline(
aes(xintercept = median_plant_height),
color = "#636363",
linetype = "solid",
size = 1
) +
annotate("text",
color = "#636363",
x = 11,
y = 20,
label = paste("Median =", round(median_plant_height, 2), "ft")) ## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## NULL hist_plant_height## Warning: Use of `health_assess_2025$plant_height_ft` is discouraged.
## ℹ Use `plant_height_ft` instead.
Seedling subset
seedlings_2025 <- health_assess_2025 %>% filter(is.na(dbh_cm))
# Plant Height (ft)
mean_plant_height <- mean(seedlings_2025$plant_height_ft, na.rm = TRUE)
median_plant_height <- median(seedlings_2025$plant_height_ft, na.rm = TRUE)
print(mean_plant_height)## [1] 3.092262hist_plant_height_seedling <- seedlings_2025 %>% ggplot(aes(x = plant_height_ft)) +
geom_histogram(bins = 30, aes(fill = seedling_y_n)) +
xlab("2025 WCP Seedling Height (ft)")
ylab("Number of Individuals") +
# Mean
geom_vline(
aes(xintercept = mean_plant_height),
color = "#bdbdbd",
linetype = "dashed",
size = 1
) +
annotate("text",
color = "#bdbdbd",
x = 3.5,
y = 5,
label = paste("Mean =", round(mean_plant_height, 2), "ft")) +
# Median
geom_vline(
aes(xintercept = median_plant_height),
color = "#636363",
linetype = "solid",
size = 1
) +
annotate("text",
color = "#636363",
x = 3,
y = 5,
label = paste("Median =", round(median_plant_height, 2), "ft"))## NULLhist_plant_height_seedling
Adult subset
adult_2025 <- health_assess_2025 %>% filter(is.na(dbh_cm))
# Plant Height (ft)
mean_plant_height <- mean(adult_2025$plant_height_ft, na.rm = TRUE)
median_plant_height <- median(adult_2025$plant_height_ft, na.rm = TRUE)
print(mean_plant_height)## [1] 3.092262hist_plant_height_adult <- adult_2025 %>% ggplot(aes(x = plant_height_ft)) +
geom_histogram(bins = 30, aes(fill = seedling_y_n)) +
xlab("2025 WCP Adult Height (ft)")
ylab("Number of Individuals") +
# Mean
geom_vline(
aes(xintercept = mean_plant_height),
color = "#bdbdbd",
linetype = "dashed",
size = 1
) +
annotate("text",
color = "#bdbdbd",
x = 3.5,
y = 5,
label = paste("Mean =", round(mean_plant_height, 2), "ft")) +
# Median
geom_vline(
aes(xintercept = median_plant_height),
color = "#636363",
linetype = "solid",
size = 1
) +
annotate("text",
color = "#636363",
x = 3,
y = 5,
label = paste("Median =", round(median_plant_height, 2), "ft"))## NULLhist_plant_height_adult
DBH
All 2025 Individuals:
# DBH (cm)
mean_dbh <- mean(health_assess_2025$dbh_cm, na.rm = TRUE)
median_dbh <- median(health_assess_2025$dbh_cm, na.rm = TRUE)
# violin_dbh <- health_assess_2025 %>% ggplot(aes(x = dbh_cm)) +
# geom_violin()
#
# violin_dbh
hist_dbh <- health_assess_2025 %>% ggplot(aes(x = dbh_cm)) +
geom_histogram(bins = 30, aes(fill = seedling_y_n)) +
xlab("2025 All *WCP* Individuals DBH (cm)")
ylab("Number of Individuals") +
# Mean
geom_vline(
aes(xintercept = mean_dbh),
color = "#bdbdbd",
linetype = "dashed",
size = 1
) +
annotate("text",
color = "#bdbdbd",
x = 30,
y = 5,
label = paste("Mean =", round(mean_dbh, 2), "cm")) +
# Median
geom_vline(
aes(xintercept = median_dbh),
color = "#636363",
linetype = "solid",
size = 1
) +
annotate("text",
color = "#636363",
x = 20,
y = 8,
label = paste("Median =", round(median_dbh, 2), "cm")) ## NULLhist_dbh## Warning: Removed 42 rows containing non-finite outside the scale range
## (`stat_bin()`).
Adults: Most adults in WCP were saplings.
adults_2025 <- health_assess_2025 %>% filter(!is.na(dbh_cm))
# DBH (cm)
mean_dbh <- mean(adults_2025$dbh_cm, na.rm = TRUE)
median_dbh <- median(adults_2025$dbh_cm, na.rm = TRUE)
hist_dbh <- adults_2025 %>% ggplot(aes(x = dbh_cm)) +
geom_histogram(bins = 30, aes(fill = seedling_y_n)) +
xlab("2025 WCP Adult DBH (cm) [Should only be adults]")
ylab("Number of Individuals") +
# Mean
geom_vline(
aes(xintercept = mean_dbh),
color = "#bdbdbd",
linetype = "dashed",
size = 1
) +
annotate("text",
color = "#bdbdbd",
x = 30,
y = 5,
label = paste("Mean =", round(mean_dbh, 2), "cm")) +
# Median
geom_vline(
aes(xintercept = median_dbh),
color = "#636363",
linetype = "solid",
size = 1
) +
annotate("text",
color = "#636363",
x = 20,
y = 8,
label = paste("Median =", round(median_dbh, 2), "cm")) ## NULLhist_dbh
% Live Canopy
All individual distribution
mean_percent_live_canopy <- mean(health_assess_2025$percent_live_canopy, na.rm = TRUE)
median_percent_live_canopy <- median(health_assess_2025$percent_live_canopy, na.rm = TRUE)
hist_percent_live_canopy_2025 <- health_assess_2025 %>%
ggplot(aes(x = percent_live_canopy)) +
xlab("% Live Canopy in 2025") +
ylab("Number of Individuals") +
geom_histogram(bins = 30, aes(fill = seedling_y_n)) +
# Mean
geom_vline(
aes(xintercept = mean_percent_live_canopy),
color = "#bdbdbd",
linetype = "dashed",
size = 1
) +
annotate("text",
color = "#bdbdbd",
x = 70,
y = 20,
label = paste("Mean =", round(mean_percent_live_canopy, 2), "%")) +
# Median
geom_vline(
aes(xintercept = median_percent_live_canopy),
color = "#636363",
linetype = "solid",
size = 1
) +
annotate("text",
color = "#636363",
x = 85,
y = 30,
label = paste("Median =", round(median_percent_live_canopy, 2), "%"))
hist_percent_live_canopy_2025## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_bin()`).
Seedling subset
seedlings_2025 <- health_assess_2025 %>% filter(is.na(dbh_cm))
mean_percent_live_canopy <- mean(seedlings_2025$percent_live_canopy, na.rm = TRUE)
median_percent_live_canopy <- median(seedlings_2025$percent_live_canopy, na.rm = TRUE)
hist_percent_live_canopy_2025_seedling <- seedlings_2025 %>%
ggplot(aes(x = percent_live_canopy)) +
xlab("% Live Canopy in 2025 FOR SEEDLINGS") +
ylab("Number of Individuals") +
geom_histogram(bins = 30, aes(fill = seedling_y_n)) +
# Mean
geom_vline(
aes(xintercept = mean_percent_live_canopy),
color = "#bdbdbd",
linetype = "dashed",
size = 1
) +
annotate("text",
color = "#bdbdbd",
x = 70,
y = 20,
label = paste("Mean =", round(mean_percent_live_canopy, 2), "%")) +
# Median
geom_vline(
aes(xintercept = median_percent_live_canopy),
color = "#636363",
linetype = "solid",
size = 1
) +
annotate("text",
color = "#636363",
x = 85,
y = 30,
label = paste("Median =", round(median_percent_live_canopy, 2), "%"))
hist_percent_live_canopy_2025_seedling
Adult subset
adults_2025 <- health_assess_2025 %>% filter(!is.na(dbh_cm))
mean_percent_live_canopy <- mean(adults_2025$percent_live_canopy, na.rm = TRUE)
median_percent_live_canopy <- median(adults_2025$percent_live_canopy, na.rm = TRUE)
hist_percent_live_canopy_2025_adult <- adults_2025 %>%
ggplot(aes(x = percent_live_canopy)) +
xlab("% Live Canopy in 2025 FOR ADULTS") +
ylab("Number of Individuals") +
geom_histogram(bins = 30, aes(fill = seedling_y_n)) +
# Mean
geom_vline(
aes(xintercept = mean_percent_live_canopy),
color = "#bdbdbd",
linetype = "dashed",
size = 1
) +
annotate("text",
color = "#bdbdbd",
x = 70,
y = 20,
label = paste("Mean =", round(mean_percent_live_canopy, 2), "%")) +
# Median
geom_vline(
aes(xintercept = median_percent_live_canopy),
color = "#636363",
linetype = "solid",
size = 1
) +
annotate("text",
color = "#636363",
x = 85,
y = 30,
label = paste("Median =", round(median_percent_live_canopy, 2), "%"))
hist_percent_live_canopy_2025_adult## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_bin()`).
Has Canker & Callous
library(viridis)## Loading required package: viridisLitehealth_assess_2025 <- health_assess_2025 %>% mutate(
purdue_severity_canker = recode(
health_assess_2025$purdue_severity_canker,
"1. Fewer than 3 active cankers that are all smaller than 2-3 inches in length or diameter OR fewer than 3 inactive cankers." = "1",
"2. More than 3 active cankers, OR 2-5 shallow (with no dead tissue) healed over with cracks less than 7 inches long." = "2",
"3. More than 5 active OR inactive cankers cracked through the bark to the tissue below which have healed over, but you still see the level of damage." = "3",
"4. Cankers occur all over the 10-foot area, with deep cracks and both active and inactive cankers." = "4",
"5. Tree almost dead, mostly inactive cankers with deep cracks to dead tissue." = "5"
)
)
bar_has_canker_2025 <- health_assess_2025 %>% ggplot(aes(x = has_canker)) +
geom_bar( aes(fill = purdue_severity_canker))
bar_has_callous_2025 <- health_assess_2025 %>% ggplot(aes(x = has_callous)) +
geom_bar( aes(fill = purdue_severity_canker))
(bar_has_canker_2025 + bar_has_callous_2025) + plot_layout(guides="collect")
Canker Areas
mean_base_area <- mean(health_assess_2025$base_canker_area, na.rm = TRUE)
median_base_area <- median(health_assess_2025$base_canker_area, na.rm = TRUE)
hist_base_canker <- health_assess_2025 %>% ggplot(aes(x = base_canker_area)) +
geom_histogram( aes(fill = seedling_y_n)) +
ylab("Number of Individuals") +
# Mean
geom_vline(
aes(xintercept = mean_base_area),
color = "#bdbdbd",
linetype = "dashed",
size = 1
) +
annotate("text",
color = "#bdbdbd",
x = 26,
y = 10,
label = paste("Mean =", round(mean_base_area, 2), "%")) +
# Median
geom_vline(
aes(xintercept = median_base_area),
color = "#636363",
linetype = "solid",
size = 1
) +
annotate("text",
color = "#636363",
x = 10,
y = 25,
label = paste("Median =", round(median_base_area, 2), "%"))mean_trunk_area <- mean(health_assess_2025$trunk_canker_area, na.rm = TRUE)
median_trunk_area <- median(health_assess_2025$trunk_canker_area, na.rm = TRUE)
hist_trunk_canker <- health_assess_2025 %>% ggplot(aes(x = trunk_canker_area)) +
geom_histogram(aes(fill = seedling_y_n)) +
ylab("Number of Individuals") +
# Mean
geom_vline(
aes(xintercept = mean_trunk_area),
color = "#bdbdbd",
linetype = "dashed",
size = 1
) +
annotate("text",
color = "#bdbdbd",
x = 32,
y = 10,
label = paste("Mean =", round(mean_trunk_area, 2), "%")) +
# Median
geom_vline(
aes(xintercept = median_trunk_area),
color = "#636363",
linetype = "solid",
size = 1
) +
annotate("text",
color = "#636363",
x = 30,
y = 13,
label = paste("Median =", round(median_trunk_area, 2), "%"))
hist_base_canker / hist_trunk_canker## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_bin()`).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_bin()`).
density_trunk_canker <- health_assess_2025 %>% ggplot(aes(x = trunk_canker_area)) +
geom_density()
# + aes(fill = has_callous_2025))
# # Mean
# geom_vline(
# aes(xintercept = mean_trunk_area),
# color = "#bdbdbd",
# linetype = "dashed",
# size = 1
# ) +
# annotate("text",
# color = "#bdbdbd",
# x = 0.1,
# y = 0.1,
# label = paste("Mean =", round(mean_trunk_area, 2), "%")) +
#
# # Median
# geom_vline(
# aes(xintercept = median_trunk_area),
# color = "#636363",
# linetype = "solid",
# size = 1
# ) +
# annotate("text",
# color = "#636363",
# x = 0.5,
# y = 0.5,
# label = paste("Median =", round(median_trunk_area, 2), "%"))
density_trunk_canker## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_density()`).
# Linear regression model
model <- lm(base_canker_area ~ dbh_cm, data = health_assess_2025)
coefs <- coef(model)
r_squared <- summary(model)$r.squared
equation <- paste("y = ", round(coefs[2], 2), "x", " + ", round(coefs[1], 2), sep = "")
point_base_canker <- health_assess_2025 %>% ggplot(aes(x = dbh_cm, y =
base_canker_area)) +
geom_point(aes(color = seedling_y_n)) +
geom_smooth(method = lm) +
annotate(
"text",
x = 30,
y = 80,
label = equation,
hjust = 0
) +
annotate(
"text",
x = 30,
y = 65,
label = paste("R-squared = ", round(r_squared, 4)),
hjust = 0
)# Linear regression model
model <- lm(trunk_canker_area ~ dbh_cm, data = health_assess_2025)
coefs <- coef(model)
r_squared <- summary(model)$r.squared
equation <- paste("y = ", round(coefs[2], 2), "x", " + ", round(coefs[1], 2), sep = "")
point_trunk_canker <- health_assess_2025 %>% ggplot(aes(x = dbh_cm, y=trunk_canker_area)) +
geom_point(aes(color=seedling_y_n)) +
geom_smooth(method=lm) +
annotate(
"text",
x = 30,
y = 80,
label = equation,
hjust = 0
) +
annotate(
"text",
x = 30,
y = 65,
label = paste("R-squared = ", round(r_squared, 4)),
hjust = 0
)
(point_base_canker / point_trunk_canker) + plot_layout(guides="collect")## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 44 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 44 rows containing missing values or values outside the scale range
## (`geom_point()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 44 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Removed 44 rows containing missing values or values outside the scale range
## (`geom_point()`).
health_assess_2025 %>%
ggplot() +
geom_point(aes(x=dbh_cm, y=base_canker_area, colour = purdue_severity_canker)) +
theme_classic() +
facet_wrap(~purdue_severity_canker)## Warning: Removed 44 rows containing missing values or values outside the scale range
## (`geom_point()`).
Canker severity
bar_canker_severity <- health_assess_2025 %>% ggplot(aes(x = purdue_severity_canker)) +
geom_bar(aes(fill = purdue_severity_canker))
bar_canker_severity
Densiometer
North
mean_north <- mean(health_assess_2025$densio_north, na.rm = TRUE)
median_north <- median(health_assess_2025$densio_north, na.rm = TRUE)
hist_densio_north <- health_assess_2025 %>% ggplot(aes(x = densio_north)) +
geom_histogram() + # aes(fill = seedling_y_n)) +
ylab("Number of Individuals") +
# Mean
geom_vline(
aes(xintercept = mean_north),
color = "#bdbdbd",
linetype = "dashed",
size = 1
) +
annotate("text",
color = "#bdbdbd",
x = 32,
y = 10,
label = paste("Mean =", round(mean_north, 2), "%")) +
# Median
geom_vline(
aes(xintercept = median_north),
color = "#636363",
linetype = "solid",
size = 1
) +
annotate("text",
color = "#636363",
x = 30,
y = 13,
label = paste("Median =", round(median_north, 2), "%"))
hist_densio_north## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 31 rows containing non-finite outside the scale range
## (`stat_bin()`).
South
mean_south <- mean(health_assess_2025$densio_south, na.rm = TRUE)
median_south <- median(health_assess_2025$densio_south, na.rm = TRUE)
hist_densio_south <- health_assess_2025 %>% ggplot(aes(x = densio_south)) +
geom_histogram() + # aes(fill = seedling_y_n)) +
ylab("Number of Individuals") +
# Mean
geom_vline(
aes(xintercept = mean_south),
color = "#bdbdbd",
linetype = "dashed",
size = 1
) +
annotate("text",
color = "#bdbdbd",
x = 32,
y = 10,
label = paste("Mean =", round(mean_south, 2), "%")) +
# Median
geom_vline(
aes(xintercept = median_south),
color = "#636363",
linetype = "solid",
size = 1
) +
annotate("text",
color = "#636363",
x = 30,
y = 13,
label = paste("Median =", round(median_south, 2), "%"))
hist_densio_south## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 32 rows containing non-finite outside the scale range
## (`stat_bin()`).
### East
mean_east <- mean(health_assess_2025$densio_east, na.rm = TRUE)
median_east <- median(health_assess_2025$densio_east, na.rm = TRUE)
hist_densio_east <- health_assess_2025 %>% ggplot(aes(x = densio_east)) +
geom_histogram() + # aes(fill = seedling_y_n)) +
ylab("Number of Individuals") +
# Mean
geom_vline(
aes(xintercept = mean_east),
color = "#bdbdbd",
linetype = "dashed",
size = 1
) +
annotate("text",
color = "#bdbdbd",
x = 32,
y = 10,
label = paste("Mean =", round(mean_east, 2), "%")) +
# Median
geom_vline(
aes(xintercept = median_east),
color = "#636363",
linetype = "solid",
size = 1
) +
annotate("text",
color = "#636363",
x = 30,
y = 13,
label = paste("Median =", round(median_east, 2), "%"))
hist_densio_east## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 32 rows containing non-finite outside the scale range
## (`stat_bin()`).
West
mean_west <- mean(health_assess_2025$densio_east, na.rm = TRUE)
median_west <- median(health_assess_2025$densio_east, na.rm = TRUE)
hist_densio_west <- health_assess_2025 %>% ggplot(aes(x = densio_west)) +
geom_histogram() + # aes(fill = seedling_y_n)) +
ylab("Number of Individuals") +
# Mean
geom_vline(
aes(xintercept = mean_west),
color = "#bdbdbd",
linetype = "dashed",
size = 1
) +
annotate("text",
color = "#bdbdbd",
x = 32,
y = 10,
label = paste("Mean =", round(mean_west, 2), "%")) +
# Median
geom_vline(
aes(xintercept = median_west),
color = "#636363",
linetype = "solid",
size = 1
) +
annotate("text",
color = "#636363",
x = 30,
y = 13,
label = paste("Median =", round(median_west, 2), "%"))
hist_densio_west## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 32 rows containing non-finite outside the scale range
## (`stat_bin()`).
library(patchwork)
empty <- plot_spacer()
(
(empty + hist_densio_west + empty) /
(hist_densio_west + empty + hist_densio_east) /
(empty + hist_densio_south + empty))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 32 rows containing non-finite outside the scale range
## (`stat_bin()`).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 32 rows containing non-finite outside the scale range
## (`stat_bin()`).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 32 rows containing non-finite outside the scale range
## (`stat_bin()`).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 32 rows containing non-finite outside the scale range
## (`stat_bin()`).
Summary Plots
