# Load the packages in ----------------------------------------------------
if(!require(tidyverse)){install.packages("tidyverse"); library(tidyverse)}
if(!require(patchwork)){install.packages("patchwork"); library(patchwork)}
if(!require(ggplot2)){install.packages("ggplot2"); library(ggplot2)}
if(!require(scales)){install.packages("scales"); library(scales)}
if(!require(RColorBrewer)){install.packages("RColorBrewer"); library(RColorBrewer)}
# I stick all my styling into a CUsotm PAckage to tidy up my code and keep it consistent over the time
if(!require(CustomGGPlot2Theme)){devtools::install("CustomGGPlot2Theme"); library(CustomGGPlot2Theme)}
water_quality <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-05-20/water_quality.csv')
weather <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-05-20/weather.csv') %>%
select(-c(longitude, latitude))
water_quality$date <- as.Date(water_quality$date)
weather$date <- as.Date(weather$date)
# Remove specific years
combined <- left_join(water_quality, weather, by = "date") %>%
mutate(Year = year(date),
Month = month(date, label = T),
Day = day(date)) %>%
filter(Year >= 2000,
Year != 2025)
# Average temperature by Year and Month
stripes_data <- combined %>%
group_by(Year, Month) %>%
summarise(avg_temp = mean(max_temp_C, na.rm = TRUE),
average_bacteria = mean(enterococci_cfu_100ml, na.rm = T),
average_rain = mean(precipitation_mm, na.rm = T),.groups = "drop")
# Fix error in mean calculation
maxmin <- range(stripes_data$avg_temp, na.rm = TRUE)
md <- mean(stripes_data$avg_temp, na.rm = TRUE) # Fixed: was incorrectly referring to `stripes_data$stripes_data`
# Color palette
col_strip <- brewer.pal(11, "RdBu")
col_strip <- adjustcolor(col_strip, alpha.f = 0.5)
# Create a date column for plotting (necessary for proper x-axis handling)
# Ensure Month is a factor for correct month ordering
stripes_data$Month_num <- as.numeric(stripes_data$Month)
# Compute the original ranges for correct inverse transformation
# Compute the original ranges for correct inverse transformation
# Compute the original ranges for correct inverse transformation
rain_range <- range(stripes_data$average_rain, na.rm = TRUE)
bac_range <- range(stripes_data$average_bacteria, na.rm = TRUE)
p1 <- ggplot(stripes_data, aes(x = Month, y = 1, fill = avg_temp)) +
geom_tile(height = 0.4) + # Explicit tile height kept for visibility
scale_fill_gradientn(
colors = rev(col_strip),
values = rescale(c(maxmin[1], md, maxmin[2])),
na.value = "gray80"
) +
# Overlay bacteria as points – now with a mapping that creates a legend entry:
geom_point(
aes(
x = Month_num,
y = scales::rescale(average_bacteria, to = c(0.8, 1.2)),
color = "Average Enterococci per 100ml" # This will create a legend key labeled "Bacteria"
),
size = 0.5,
group = 1
) +
geom_segment(aes(x = Month_num, xend = Month_num, y = 0.8, yend = scales::rescale(average_bacteria, to = c(0.8, 1.2))),
color = "black", size = 0.5) +
# Overlay rainfall as a line – now with a mapping that creates a legend entry:
geom_line(
aes(
x = Month_num,
y = scales::rescale(average_rain, to = c(0.8, 1.2)),
color = "Average Monthly Rainfall (mm)" # This will create a legend key labeled "Rainfall"
),
size = 0.5,
group = 1
) +
labs(
title = str_wrap("Relationship between Average Enterococci per 100ml, Average Temperature, and Average Rainfall", 70),
caption = "Tidy Tuesday 2025 Week 20",
x = "Month",
y = NULL,
fill = "Avg Temp (°C)"
) +
Custom_Style() + # Use your pre-defined theme_strip, if available
theme(
panel.grid = element_blank(),
plot.title = element_text(hjust = 0.5),
# Make facet label strips transparent:
strip.background = element_rect(fill = "transparent", color = NA)
) +
# Dual y-axis: left for rainfall, right for bacteria
scale_y_continuous(
name = "Average Monthly Rainfall (mm)",
limits = c(0.8, 1.2),
breaks = seq(0.8, 1.2, length.out = 5),
labels = function(x) {
# Inverse transformation for rainfall:
round((x - 0.8) / 0.4 * diff(rain_range) + rain_range[1], digits = 1)
},
sec.axis = sec_axis(
trans = ~ (. - 0.8) / 0.4 * diff(bac_range) + bac_range[1],
name = "Average Enterococci per 100ml"
)
) +
scale_color_manual(
name = "",
values = c("Average Enterococci per 100ml" = "black", "Average Monthly Rainfall (mm)" = "blue"),
guide = guide_legend(
override.aes = list(
linetype = c(0, 1),
shape = c(16, NA)
)
)
) +
facet_wrap(~ Year)
Water Quality :::