The Thames in Motion: How Flow Shapes Water Quality and Pollution at FRBC

Analysis of water quality measurements and cleanup data at Fulham Reach Boat Club, 2023-2024

MakeoverMonday

2025

Exploring how water flow in the Thames influences pollution accumulation, nutrient levels, and water clarity at Fulham Reach Boat Club through analysis of water quality measurements and cleanup data.

Author

Steven Ponce

Published

March 2, 2025

Original

The original visualization River Water Quality comes from data.world.org

Makeover

Figure 1: The Thames in Motion” visualization shows three charts analyzing water quality at Fulham Reach Boat Club. Top left: Negative correlation between water flow and rubbish collected. Top right: Turbidity peaks at moderate flow rates with blue-green algae common at low flows. Bottom: Seasonal nitrate and phosphate relationships across four seasons with theoretical thresholds. All charts demonstrate how river flow fundamentally impacts pollution accumulation, water clarity, and nutrient dynamics.

Steps to Create this Graphic

1. Load Packages & Setup

Show code

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
pacman::p_load(
     tidyverse,      # Easily Install and Load the 'Tidyverse'
    ggtext,         # Improved Text Rendering Support for 'ggplot2'
    showtext,       # Using Fonts More Easily in R Graphs
    janitor,        # Simple Tools for Examining and Cleaning Dirty Data
    skimr,          # Compact and Flexible Summaries of Data
    scales,         # Scale Functions for Visualization
    glue,           # Interpreted String Literals
    here,           # A Simpler Way to Find Your Files
    lubridate,      # Make Dealing with Dates a Little Easier
    ggpubr,         # 'ggplot2' Based Publication Ready Plots
    patchwork,      # The Composer of Plots
    camcorder       # Record Your Plot History 
)
})

### |- figure size ----
gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  =  12,
    height =  12,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))

2. Read in the Data

Show code

#' The raw data for the week MakeoverMonday challenge can be downloaded 
#' here: https://data.world/makeovermonday/2025-week-10-river-water-quality/workspace/project-summary?agentid=makeovermonday&datasetid=2025-week-10-river-water-quality
#' 

frbc_fresh_water_raw <- read_csv(
  here::here('data/FRBC FreshWater Watch - Form responses 1.csv')) |> 
  clean_names()

frbc_river_cleanups_raw  <- read_csv(
  here::here('data/FRBC River Clean ups - Form responses 1.csv')) |> 
  clean_names()

3. Examine the Data

Show code

glimpse(frbc_fresh_water_raw)
skim(frbc_fresh_water_raw)

glimpse(frbc_river_cleanups_raw)
skim(frbc_river_cleanups_raw)

4. Tidy Data

Show code

### |-  tidy data ----
# Clean the water quality dataset
clean_frbc_water <- function(frbc_fresh_water_raw) {
  frbc_clean <- frbc_fresh_water_raw |>
    # Convert date to proper format
    mutate(
      sample_date = dmy(sample_date),
      # Create date_time column
      date_time = if_else(
        !is.na(sample_date) & !is.na(sample_time),
        paste(sample_date, sample_time),
        NA_character_
      ),
      date_time = as.POSIXct(date_time, format = "%Y-%m-%d %H:%M:%S")
    ) |>
    # Clean column names for easier reference
    rename(
      participants = total_number_of_participants,
      organisation = organisation_representing_participants_e_g_school_company,
      surface_condition = is_there_any_of_the_following_on_the_water_surface,
      algae_type = what_best_describes_the_dominant_form_of_algae_present,
      litter_presence = can_you_see_any_litter_including_litter_caught_in_vegetation,
      plastic_pollution = which_of_the_following_sources_of_plastic_pollution_can_you_see_select_all_that_apply,
      pollution_sources = are_there_any_water_pollution_sources_in_the_immediate_surroundings_select_all_that_apply,
      aquatic_life = what_aquatic_life_is_there_evidence_of_select_all_that_apply,
      recent_rain = has_there_been_any_rain_during_the_last_24_hrous,
      air_temp_c = air_temperature_degrees_celsius_to_nearest_whole_number,
      water_temp_c = water_temperature_degrees_celsius_to_nearest_whole_number,
      flow_ms = water_flow_measurement_m_s,
      nitrate_fww_mgl = fresh_water_watch_nitrate_test_mg_l,
      phosphate_fww_mgl = fresh_water_watch_phosphate_mg_l,
      turbidity_ntu = turbidity_secchi_tube_ntu,
      water_color = estimate_the_water_colour,
      nitrate_strip_mgl = test_strip_nitrate_mg_l_ppm,
      nitrite_strip_mgl = test_strip_nitrite_mg_l_ppm,
      chlorine_mgl = test_strip_chlorine_mg_l_ppm,
      hardness_mgl = test_strip_total_hardness_mg_l_ppm,
      carbonate_mgl = test_strip_carbonate_mg_l_ppm,
      ph = test_strip_p_h,
      phosphate_strip_mgl = test_strip_phosphate_mg_l_ppm_precision_laboratories,
      ecoli_cfu = alert_one_e_coli_reading_cfu_per_100ml
    ) |>
    # Extract year, month for easier analysis
    mutate(
      year = year(sample_date),
      month = month(sample_date),
      month_name = month(sample_date, label = TRUE),
      season = case_when(
        month %in% c(12, 1, 2) ~ "Winter",
        month %in% c(3, 4, 5) ~ "Spring",
        month %in% c(6, 7, 8) ~ "Summer",
        month %in% c(9, 10, 11) ~ "Autumn",
        TRUE ~ NA_character_
      )
    ) |>
    # Handle missing values appropriately
    mutate(across(where(is.numeric), ~if_else(is.na(.), NA_real_, .))) |>
    # Create a binary rain indicator
    mutate(rain_binary = ifelse(recent_rain == "None", 0, 1))
  
  return(frbc_clean)
}

# Clean the river cleanups dataset
clean_frbc_cleanups <- function(frbc_river_cleanups_raw) {     
  frbc_cleanups_clean <- frbc_river_cleanups_raw |>
    # Convert date to proper format
    mutate(
      sample_date = dmy(sample_date),
      # Create date_time column
      date_time = if_else(
        !is.na(sample_date) & !is.na(sample_time),
        paste(sample_date, sample_time),
        NA_character_
      ),
      date_time = as.POSIXct(date_time, format = "%Y-%m-%d %H:%M:%S")
    ) |>
    # Clean column names for easier reference
    rename(
      participants = total_number_of_participants,
      organisation = organisation_representing_participants_e_g_school_company,
      rubbish_sources = which_of_the_following_sources_of_rubbish_can_you_see_select_all_that_apply,
      rubbish_types = rubbish_types_collected_during_the_clean_up_select_all_that_apply,
      rubbish_weight_kg = estimated_weight_of_rubbish_collected_in_kilograms_kg
    ) |>
    # Extract year, month for easier analysis
    mutate(
      year = year(sample_date),
      month = month(sample_date),
      month_name = month(sample_date, label = TRUE),
      season = case_when(
        month %in% c(12, 1, 2) ~ "Winter",
        month %in% c(3, 4, 5) ~ "Spring",
        month %in% c(6, 7, 8) ~ "Summer",
        month %in% c(9, 10, 11) ~ "Autumn",
        TRUE ~ NA_character_
      )
    ) |>
    # Calculate rubbish per participant
    mutate(rubbish_per_participant = rubbish_weight_kg / participants)
  
  return(frbc_cleanups_clean)
}

# Function to join the datasets for integrated analysis
join_frbc_datasets <- function(frbc_water_clean, frbc_cleanups_clean) {
  # Create a date-based lookup to find closest water quality measurement for each cleanup
  frbc_cleanups_clean <- frbc_cleanups_clean |>
    mutate(cleanup_id = row_number())
  
  frbc_water_clean <- frbc_water_clean |>
    mutate(water_id = row_number())
  
  # Extract relevant columns from each dataset with their proper names
  cleanup_dates <- frbc_cleanups_clean |>
    select(cleanup_id, cleanup_date = sample_date, year, month)
  
  water_data <- frbc_water_clean |>
    select(
      water_id, 
      water_date = sample_date, 
      water_temp_c, 
      flow_ms, 
      nitrate_fww_mgl, 
      phosphate_fww_mgl, 
      turbidity_ntu, 
      ph
    )
  
  # Create all combinations of cleanup events and water measurements
  all_combinations <- expand_grid(
    cleanup_id = cleanup_dates$cleanup_id,
    water_id = water_data$water_id
  )
  
  # Calculate time differences
  closest_measurements <- all_combinations |>
    left_join(cleanup_dates, by = "cleanup_id") |>
    left_join(water_data, by = "water_id") |>
    mutate(days_diff = abs(as.numeric(difftime(cleanup_date, water_date, units = "days")))) |>
    group_by(cleanup_id) |>
    slice_min(order_by = days_diff, n = 1) |>
    ungroup()
  
  # Join back to cleanup data
  joined_data <- frbc_cleanups_clean |>
    left_join(
      closest_measurements |>
        select(
          cleanup_id, 
          water_id, 
          days_diff, 
          water_date,
          water_temp_c, 
          flow_ms, 
          nitrate_fww_mgl, 
          phosphate_fww_mgl, 
          turbidity_ntu, 
          ph
        ),
      by = "cleanup_id"
    )
  
  return(joined_data)
}

# Cleaned and joined datasets
frbc_water_clean <- clean_frbc_water(frbc_fresh_water_raw) 
frbc_cleanups_clean <- clean_frbc_cleanups(frbc_river_cleanups_raw) 
joined_data <- join_frbc_datasets(frbc_water_clean, frbc_cleanups_clean)

5. Visualization Parameters

Show code

### |-  plot aesthetics ----
# Get base colors with custom palette
colors <- get_theme_colors(palette = c())
  
### |-  titles and caption ----
title_text <- str_glue("The Thames in Motion: How Flow Shapes Water Quality and Pollution at FRBC")
subtitle_text <- str_glue("Analysis of water quality measurements and cleanup data at Fulham Reach Boat Club, 2023-2024")

# Create caption
caption_text <- create_mm_caption(
    mm_year = 2025,
    mm_week = 10,
    source_text = "River Water Quality"
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # Weekly-specific modifications
    legend.position = "top",
    legend.title = element_text(size = rel(0.79)),
    legend.text = element_text(size = rel(0.71)),
    
    axis.title = element_text(size = rel(1.14)),  
    axis.text = element_text(size = rel(0.86)),  
    axis.text.y = element_blank(),
    axis.title.y = element_blank(),
    axis.ticks.y = element_blank(),
    axis.ticks.x = element_line(color = "gray", size = 0.5), 
    axis.ticks.length = unit(0.2, "cm"), 
    
    strip.text.y = element_text(size = rel(0.7), angle = 0), 
    
    panel.border = element_blank(),
    panel.grid = element_blank(),
    panel.spacing = unit(1, "lines"),  
    panel.spacing.y = unit(0, "lines"),
    
  )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code

### |-  Plot  ----
# P1. River Flow Influences Pollution Accumulation ----
flow_rubbish_plot <- joined_data |>
  filter(!is.na(flow_ms), !is.na(rubbish_weight_kg)) |>  
  ggplot(aes(x = flow_ms, y = rubbish_weight_kg, color = season)) +
  # Geoms
  geom_point(size = 3.5, alpha = 0.85) +
  geom_smooth(method = "lm", se = TRUE, color = "black", 
              linetype = "dashed", fill = "gray80", alpha = 0.4) +
  # Scale
  scale_x_continuous(
    breaks = seq(-0.3, 0.5, by = 0.1),
    labels = function(x) sprintf("%.1f", x)
  ) +
  scale_y_continuous(breaks = seq(0, 120, by = 20)) +
  scale_color_brewer(palette = "Set1") +
  # Labs
  labs(
    title = "River Flow Influences Pollution Accumulation",
    subtitle = "Negative correlation shows higher rubbish collection at lower flow rates",
    x = "Water Flow (m/s)",
    y = "Rubbish Collected (kg)",
    color = "Season"
  ) +
  # Theme
  theme_minimal() +
  theme(
    text = element_text(family = "sans", size = 12),
    plot.title = element_text(face = "bold", size = 16),
    plot.subtitle = element_text(size = 12, color = "gray30"),
    legend.position = "bottom",
    legend.title = element_text(face = "bold"),
    panel.grid.minor = element_line(color = "gray95"),
    panel.grid.major = element_line(color = "gray90"),
    axis.title = element_text(face = "bold"),
    plot.margin = margin(20, 20, 20, 20)
  )

# Add annotation for statistical significance
# Get the linear model to extract statistics
flow_model <- lm(rubbish_weight_kg ~ flow_ms, data = joined_data)
model_p_value <- summary(flow_model)$coefficients[2,4]
model_r_squared <- summary(flow_model)$r.squared

# Format p-value and R-squared for display
p_value_text <- ifelse(model_p_value < 0.001, "p < 0.001", paste("p =", round(model_p_value, 3)))
r_squared_text <- paste("R² =", round(model_r_squared, 2))

# Add the annotation in upper right corner with improved formatting
flow_rubbish_plot <- flow_rubbish_plot +
  annotate(
    "label", 
    x = 0.3, 
    y = 110,
    label = paste(r_squared_text, p_value_text, sep = "\n"),
    hjust = 1, 
    size = 4,
    fontface = "bold",
    color = "black",
    fill = "white",
    alpha = 0.7
  )


# P2. Seasonal Nutrient Dynamics in Thames Water ----
nutrient_scatter_plot <- frbc_water_clean |>
  filter(!is.na(nitrate_fww_mgl), !is.na(phosphate_fww_mgl)) |>
  ggplot(aes(x = nitrate_fww_mgl, y = phosphate_fww_mgl, color = season)) +
  # Geoms
  geom_point(size = 3.5, alpha = 0.85) +
  geom_smooth(method = "lm", se = FALSE, linewidth = 1.2) +
  # Scale
  scale_x_continuous(
    limits = c(0, 10.5),
    breaks = seq(0, 10, by = 2.5)
  ) +
  scale_y_continuous(
    limits = c(0, 1),
    breaks = seq(0, 1, by = 0.25)
  ) +
  scale_color_brewer(palette = "Set1") +
  # Labs 
  labs(
    title = "Seasonal Nutrient Dynamics in Thames Water",
    subtitle = "Correlated nutrient levels suggest common sources vary by season",
    x = "Nitrate (mg/L)",
    y = "Phosphate (mg/L)",
    color = "Season"
  ) +
  # Facet 
  facet_wrap(~ season, scales = "fixed") +
  # Theme
  theme_minimal() +
  theme(
    text = element_text(family = "sans", size = 12),
    plot.title = element_text(face = "bold", size = 16, hjust = 0.5),
    plot.subtitle = element_text(size = 12, color = "gray30", hjust = 0.5),
    legend.position = "none", 
    panel.grid.minor = element_line(color = "gray95"),
    panel.grid.major = element_line(color = "gray90"),
    axis.title = element_text(face = "bold"),
    strip.text = element_text(face = "bold", size = 12), 
    plot.margin = margin(20, 20, 20, 20)
  )

# Create a dataframe with R² values for each season
r2_values <- tibble(season = character(), r2 = numeric())

for(current_season in unique(frbc_water_clean$season)) {
  # Skip if there's no data for this season
  season_data <- frbc_water_clean |> 
    filter(season == current_season, !is.na(nitrate_fww_mgl), !is.na(phosphate_fww_mgl))
  
  if(nrow(season_data) < 3) {
    # Add placeholder if not enough data
    r2_values <- r2_values |> add_row(season = current_season, r2 = NA)
    next
  }
  
  # Calculate linear model for this season
  season_model <- lm(phosphate_fww_mgl ~ nitrate_fww_mgl, data = season_data)
  r2_values <- r2_values |> add_row(
    season = current_season, 
    r2 = summary(season_model)$r.squared
  )
}

# Add a data frame with annotation positions for each season
annotation_positions <- tibble(
  season = unique(frbc_water_clean$season),
  x = rep(2, length(unique(frbc_water_clean$season))),
  y = rep(0.7, length(unique(frbc_water_clean$season)))
)

# Join R² values with positions
annotation_data <- annotation_positions |>
  left_join(r2_values, by = "season") |>
  filter(!is.na(r2))

# Add annotations programmatically with one per facet
nutrient_scatter_plot <- nutrient_scatter_plot +
  geom_label(
    data = annotation_data,
    aes(x = x, y = y, label = sprintf("R² = %.2f", r2)),
    inherit.aes = FALSE,
    hjust = 0,
    size = 3.5,
    fontface = "bold",
    fill = "white",
    alpha = 0.7
  )

# Add reference line for theoretical threshold
nutrient_scatter_plot <- nutrient_scatter_plot +
  geom_hline(
    yintercept = 0.1, 
    linetype = "dashed", 
    color = "darkred", 
    alpha = 0.6
  ) +
  annotate(
    "text",
    x = 9.5,
    y = 0.16,
    label = "Theoretical threshold (0.1 mg/L)",
    hjust = 1,
    size = 3,
    fontface = "italic",
    color = "darkred"
  )


# P3. Turbidity vs Water Flow with Algae Type 
turbidity_flow_plot <- frbc_water_clean |>
  filter(!is.na(flow_ms), !is.na(turbidity_ntu)) |>
  # Clean up algae type values for better display
  mutate(
    algae_type = case_when(
      is.na(algae_type) ~ "No data",
      algae_type == "" ~ "No data",
      TRUE ~ algae_type
    )
  ) |>
  # Handle extreme outliers if present
  filter(turbidity_ntu <= 100) |>  
  ggplot(aes(x = flow_ms, y = turbidity_ntu, color = algae_type)) +
  # Geoms
  geom_point(size = 3.5, alpha = 0.85) +
  geom_smooth(
    method = "loess", 
    se = TRUE, 
    color = "black", 
    linetype = "dashed", 
    fill = "gray80", 
    alpha = 0.4,
    aes(group = 1)
  ) +
  # Scale
  scale_x_continuous(
    limits = c(-0.3, 0.6),
    breaks = seq(-0.3, 0.6, by = 0.1),
    labels = function(x) sprintf("%.1f", x)
  ) +
  scale_y_continuous(
    breaks = seq(0, 100, by = 20)
  ) +
  scale_color_brewer(palette = "Dark2") +
  # Labs
  labs(
    title = "Water Flow Affects Clarity and Algal Growth",
    subtitle = "Different algae types dominate at specific flow conditions",
    x = "Water Flow (m/s)",
    y = "Turbidity (NTU)",
    color = "Algae Type"
  ) +
  # Theme
  theme_minimal() +
  theme(
    text = element_text(family = "sans", size = 12),
    plot.title = element_text(face = "bold", size = 16),
    plot.subtitle = element_text(size = 12, color = "gray30"),
    legend.position = "bottom",
    legend.title = element_text(face = "bold"),
    panel.grid.minor = element_line(color = "gray95"),
    panel.grid.major = element_line(color = "gray90"),
    axis.title = element_text(face = "bold"),
    plot.margin = margin(20, 20, 20, 20),
    legend.key.size = unit(0.8, "lines"),
    legend.text = element_text(size = 10)
  ) +
  # Make the legend more readable with wrapped labels
  guides(color = guide_legend(nrow = 2, byrow = TRUE))

# Check if there are any blue-green scum algae points
blue_green_points <- frbc_water_clean |>
  filter(!is.na(flow_ms), !is.na(turbidity_ntu)) |>
  filter(algae_type == "Blue-green scum")

# Only add the annotation if blue-green points exist
if(nrow(blue_green_points) > 0) {
  # Find average position of blue-green scum points
  avg_x <- mean(blue_green_points$flow_ms)
  avg_y <- mean(blue_green_points$turbidity_ntu)
  
  # Add an annotation at that position
  turbidity_flow_plot <- turbidity_flow_plot +
    geom_label(
      data = data.frame(x = avg_x, y = avg_y + 5),
      aes(x = x, y = y, label = "Blue-green algae common\nat these flow rates"),
      inherit.aes = FALSE,
      size = 3,
      fontface = "bold",
      color = "black",
      fill = "white",
      alpha = 0.7
    )
}

# Add an annotation about how flow affects turbidity
turbidity_flow_plot <- turbidity_flow_plot +
  annotate(
    "label",
    x = 0.3,
    y = 80,
    label = "Moderate flow rates (around 0.3 m/s)\nshow peak turbidity with lower values\n at both slow and fast flows",
    size = 3,
    fontface = "bold",
    color = "black",
    fill = "white",
    alpha = 0.7
  )

# Combined Plots ----
plot1 <- flow_rubbish_plot 
plot2 <- nutrient_scatter_plot 
plot3 <- turbidity_flow_plot 

combined_plot <- (plot1 + plot3) / plot2 +
  plot_layout(heights = c(1, 1)) +
  plot_annotation(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    theme = theme(
      plot.title = element_text(
        size   = rel(1.8),
        family = fonts$title,
        face   = "bold",
        color  = colors$title,
        lineheight = 1.1,
        margin = margin(t = 5, b = 5)
      ),
      plot.subtitle = element_text(
        size   = rel(0.95),
        family = fonts$subtitle,
        color  = colors$subtitle,
        lineheight = 1.2,
        margin = margin(t = 5, b = 5)
      ),
      plot.caption = element_markdown(
        size   = rel(0.75),
        family = fonts$caption,
        color  = colors$caption,
        hjust  = 0.5,
        margin = margin(t = 10)
      ),
      plot.margin = margin(t = 20, r = 10, b = 20, l = 10),
    )
  )

7. Save

Show code

### |-  plot image ----  
save_plot_patchwork(
  combined_plot, 
  type = "makeovermonday", 
  year = 2025,
  week = 10,
  width = 12, 
  height = 12
  )

8. Session Info

Expand for Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] camcorder_0.1.0 patchwork_1.3.0 ggpubr_0.6.0    here_1.0.1     
 [5] glue_1.8.0      scales_1.3.0    skimr_2.1.5     janitor_2.2.0  
 [9] showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2   
[13] lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4    
[17] purrr_1.0.2     readr_2.1.5     tidyr_1.3.1     tibble_3.2.1   
[21] ggplot2_3.5.1   tidyverse_2.0.0

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1   farver_2.1.2       fastmap_1.2.0      pacman_0.5.1      
 [5] digest_0.6.37      timechange_0.3.0   lifecycle_1.0.4    rsvg_2.6.1        
 [9] magrittr_2.0.3     compiler_4.4.0     rlang_1.1.4        tools_4.4.0       
[13] utf8_1.2.4         yaml_2.3.10        knitr_1.49         ggsignif_0.6.4    
[17] htmlwidgets_1.6.4  bit_4.5.0          curl_6.0.0         RColorBrewer_1.1-3
[21] xml2_1.3.6         repr_1.1.7         abind_1.4-8        withr_3.0.2       
[25] grid_4.4.0         fansi_1.0.6        colorspace_2.1-1   cli_3.6.3         
[29] rmarkdown_2.29     crayon_1.5.3       generics_0.1.3     rstudioapi_0.17.1 
[33] tzdb_0.4.0         commonmark_1.9.2   splines_4.4.0      parallel_4.4.0    
[37] ggplotify_0.1.2    yulab.utils_0.1.8  base64enc_0.1-3    vctrs_0.6.5       
[41] Matrix_1.7-0       jsonlite_1.8.9     carData_3.0-5      car_3.1-3         
[45] gridGraphics_0.5-1 hms_1.1.3          bit64_4.5.2        rstatix_0.7.2     
[49] Formula_1.2-5      systemfonts_1.1.0  magick_2.8.5       gifski_1.32.0-1   
[53] codetools_0.2-20   stringi_1.8.4      gtable_0.3.6       munsell_0.5.1     
[57] pillar_1.9.0       htmltools_0.5.8.1  R6_2.5.1           rprojroot_2.0.4   
[61] lattice_0.22-6     vroom_1.6.5        evaluate_1.0.1     markdown_1.13     
[65] backports_1.5.0    gridtext_0.1.5     broom_1.0.7        snakecase_0.11.1  
[69] renv_1.0.3         Rcpp_1.0.13-1      nlme_3.1-164       svglite_2.1.3     
[73] mgcv_1.9-1         xfun_0.49          fs_1.6.5           pkgconfig_2.0.3

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in mm_2025_10.qmd.

For the full repository, click here.

10. References

Expand for References

Data:

Makeover Monday 2025 Week 10: River Water Quality

--- title: "The Thames in Motion: How Flow Shapes Water Quality and Pollution at FRBC" subtitle: "Analysis of water quality measurements and cleanup data at Fulham Reach Boat Club, 2023-2024" description: "Exploring how water flow in the Thames influences pollution accumulation, nutrient levels, and water clarity at Fulham Reach Boat Club through analysis of water quality measurements and cleanup data." author: "Steven Ponce" date: "2025-03-02" categories: ["MakeoverMonday", "2025"] tags: [ "Water Quality", "River Thames", "Pollution", "Environmental Analysis", "ggplot2", "Tidyverse", "Patchwork", "Nutrient Dynamics", "Turbidity", "Flow Rate", "Fulham Reach Boat Club", "Water Pollution", "Cleanup Data", "Seasonal Patterns", "Urban Rivers" ] image: "thumbnails/mm_2025_10.png" format: html: toc: true toc-depth: 5 code-link: true code-fold: true code-tools: true code-summary: "Show code" editor_options: chunk_output_type: inline execute: freeze: true cache: true error: false message: false warning: false eval: true # filters: # - social-share # share: # permalink: "https://stevenponce.netlify.app/data_visualizations/MakeoverMonday/2025/mm_2025_10.html" # description: "How does river flow affect water quality and pollution in the Thames? This visualization reveals the relationships between flow rates, rubbish collection, turbidity, and nutrient levels at Fulham Reach Boat Club. #DataViz #MakeoverMonday" # twitter: true # linkedin: true # email: true # facebook: false # reddit: false # stumble: false # tumblr: false # mastodon: true # bsky: true --- ### Original The original visualization __River Water Quality__ comes from [data.world.org](https://data.world/makeovermonday/2025-week-10-river-water-quality/workspace/project-summary?agentid=makeovermonday&datasetid=2025-week-10-river-water-quality) ![Original visualization](https://raw.githubusercontent.com/poncest/MakeoverMonday/master/2025/Week_10/original_chart.png) ### Makeover ![The Thames in Motion" visualization shows three charts analyzing water quality at Fulham Reach Boat Club. Top left: Negative correlation between water flow and rubbish collected. Top right: Turbidity peaks at moderate flow rates with blue-green algae common at low flows. Bottom: Seasonal nitrate and phosphate relationships across four seasons with theoretical thresholds. All charts demonstrate how river flow fundamentally impacts pollution accumulation, water clarity, and nutrient dynamics.](mm_2025_10.png){#fig-1} ### <mark> **Steps to Create this Graphic** </mark> #### 1. Load Packages & Setup ```{r} #| label: load #| warning: false #| message: false #| results: "hide" ## 1. LOAD PACKAGES & SETUP ---- suppressPackageStartupMessages({ pacman::p_load( tidyverse, # Easily Install and Load the 'Tidyverse' ggtext, # Improved Text Rendering Support for 'ggplot2' showtext, # Using Fonts More Easily in R Graphs janitor, # Simple Tools for Examining and Cleaning Dirty Data skimr, # Compact and Flexible Summaries of Data scales, # Scale Functions for Visualization glue, # Interpreted String Literals here, # A Simpler Way to Find Your Files lubridate, # Make Dealing with Dates a Little Easier ggpubr, # 'ggplot2' Based Publication Ready Plots patchwork, # The Composer of Plots camcorder # Record Your Plot History ) }) ### |- figure size ---- gg_record( dir = here::here("temp_plots"), device = "png", width = 12, height = 12, units = "in", dpi = 320 ) # Source utility functions suppressMessages(source(here::here("R/utils/fonts.R"))) source(here::here("R/utils/social_icons.R")) source(here::here("R/utils/image_utils.R")) source(here::here("R/themes/base_theme.R")) ``` #### 2. Read in the Data ```{r} #| label: read #| include: true #| eval: true #| warning: false #' The raw data for the week MakeoverMonday challenge can be downloaded #' here: https://data.world/makeovermonday/2025-week-10-river-water-quality/workspace/project-summary?agentid=makeovermonday&datasetid=2025-week-10-river-water-quality #' frbc_fresh_water_raw <- read_csv( here::here('data/FRBC FreshWater Watch - Form responses 1.csv')) |> clean_names() frbc_river_cleanups_raw <- read_csv( here::here('data/FRBC River Clean ups - Form responses 1.csv')) |> clean_names() ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(frbc_fresh_water_raw) skim(frbc_fresh_water_raw) glimpse(frbc_river_cleanups_raw) skim(frbc_river_cleanups_raw) ``` #### 4. Tidy Data ```{r} #| label: tidy #| warning: false ### |- tidy data ---- # Clean the water quality dataset clean_frbc_water <- function(frbc_fresh_water_raw) { frbc_clean <- frbc_fresh_water_raw |> # Convert date to proper format mutate( sample_date = dmy(sample_date), # Create date_time column date_time = if_else( !is.na(sample_date) & !is.na(sample_time), paste(sample_date, sample_time), NA_character_ ), date_time = as.POSIXct(date_time, format = "%Y-%m-%d %H:%M:%S") ) |> # Clean column names for easier reference rename( participants = total_number_of_participants, organisation = organisation_representing_participants_e_g_school_company, surface_condition = is_there_any_of_the_following_on_the_water_surface, algae_type = what_best_describes_the_dominant_form_of_algae_present, litter_presence = can_you_see_any_litter_including_litter_caught_in_vegetation, plastic_pollution = which_of_the_following_sources_of_plastic_pollution_can_you_see_select_all_that_apply, pollution_sources = are_there_any_water_pollution_sources_in_the_immediate_surroundings_select_all_that_apply, aquatic_life = what_aquatic_life_is_there_evidence_of_select_all_that_apply, recent_rain = has_there_been_any_rain_during_the_last_24_hrous, air_temp_c = air_temperature_degrees_celsius_to_nearest_whole_number, water_temp_c = water_temperature_degrees_celsius_to_nearest_whole_number, flow_ms = water_flow_measurement_m_s, nitrate_fww_mgl = fresh_water_watch_nitrate_test_mg_l, phosphate_fww_mgl = fresh_water_watch_phosphate_mg_l, turbidity_ntu = turbidity_secchi_tube_ntu, water_color = estimate_the_water_colour, nitrate_strip_mgl = test_strip_nitrate_mg_l_ppm, nitrite_strip_mgl = test_strip_nitrite_mg_l_ppm, chlorine_mgl = test_strip_chlorine_mg_l_ppm, hardness_mgl = test_strip_total_hardness_mg_l_ppm, carbonate_mgl = test_strip_carbonate_mg_l_ppm, ph = test_strip_p_h, phosphate_strip_mgl = test_strip_phosphate_mg_l_ppm_precision_laboratories, ecoli_cfu = alert_one_e_coli_reading_cfu_per_100ml ) |> # Extract year, month for easier analysis mutate( year = year(sample_date), month = month(sample_date), month_name = month(sample_date, label = TRUE), season = case_when( month %in% c(12, 1, 2) ~ "Winter", month %in% c(3, 4, 5) ~ "Spring", month %in% c(6, 7, 8) ~ "Summer", month %in% c(9, 10, 11) ~ "Autumn", TRUE ~ NA_character_ ) ) |> # Handle missing values appropriately mutate(across(where(is.numeric), ~if_else(is.na(.), NA_real_, .))) |> # Create a binary rain indicator mutate(rain_binary = ifelse(recent_rain == "None", 0, 1)) return(frbc_clean) } # Clean the river cleanups dataset clean_frbc_cleanups <- function(frbc_river_cleanups_raw) { frbc_cleanups_clean <- frbc_river_cleanups_raw |> # Convert date to proper format mutate( sample_date = dmy(sample_date), # Create date_time column date_time = if_else( !is.na(sample_date) & !is.na(sample_time), paste(sample_date, sample_time), NA_character_ ), date_time = as.POSIXct(date_time, format = "%Y-%m-%d %H:%M:%S") ) |> # Clean column names for easier reference rename( participants = total_number_of_participants, organisation = organisation_representing_participants_e_g_school_company, rubbish_sources = which_of_the_following_sources_of_rubbish_can_you_see_select_all_that_apply, rubbish_types = rubbish_types_collected_during_the_clean_up_select_all_that_apply, rubbish_weight_kg = estimated_weight_of_rubbish_collected_in_kilograms_kg ) |> # Extract year, month for easier analysis mutate( year = year(sample_date), month = month(sample_date), month_name = month(sample_date, label = TRUE), season = case_when( month %in% c(12, 1, 2) ~ "Winter", month %in% c(3, 4, 5) ~ "Spring", month %in% c(6, 7, 8) ~ "Summer", month %in% c(9, 10, 11) ~ "Autumn", TRUE ~ NA_character_ ) ) |> # Calculate rubbish per participant mutate(rubbish_per_participant = rubbish_weight_kg / participants) return(frbc_cleanups_clean) } # Function to join the datasets for integrated analysis join_frbc_datasets <- function(frbc_water_clean, frbc_cleanups_clean) { # Create a date-based lookup to find closest water quality measurement for each cleanup frbc_cleanups_clean <- frbc_cleanups_clean |> mutate(cleanup_id = row_number()) frbc_water_clean <- frbc_water_clean |> mutate(water_id = row_number()) # Extract relevant columns from each dataset with their proper names cleanup_dates <- frbc_cleanups_clean |> select(cleanup_id, cleanup_date = sample_date, year, month) water_data <- frbc_water_clean |> select( water_id, water_date = sample_date, water_temp_c, flow_ms, nitrate_fww_mgl, phosphate_fww_mgl, turbidity_ntu, ph ) # Create all combinations of cleanup events and water measurements all_combinations <- expand_grid( cleanup_id = cleanup_dates$cleanup_id, water_id = water_data$water_id ) # Calculate time differences closest_measurements <- all_combinations |> left_join(cleanup_dates, by = "cleanup_id") |> left_join(water_data, by = "water_id") |> mutate(days_diff = abs(as.numeric(difftime(cleanup_date, water_date, units = "days")))) |> group_by(cleanup_id) |> slice_min(order_by = days_diff, n = 1) |> ungroup() # Join back to cleanup data joined_data <- frbc_cleanups_clean |> left_join( closest_measurements |> select( cleanup_id, water_id, days_diff, water_date, water_temp_c, flow_ms, nitrate_fww_mgl, phosphate_fww_mgl, turbidity_ntu, ph ), by = "cleanup_id" ) return(joined_data) } # Cleaned and joined datasets frbc_water_clean <- clean_frbc_water(frbc_fresh_water_raw) frbc_cleanups_clean <- clean_frbc_cleanups(frbc_river_cleanups_raw) joined_data <- join_frbc_datasets(frbc_water_clean, frbc_cleanups_clean) ``` #### 5. Visualization Parameters ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- # Get base colors with custom palette colors <- get_theme_colors(palette = c()) ### |- titles and caption ---- title_text <- str_glue("The Thames in Motion: How Flow Shapes Water Quality and Pollution at FRBC") subtitle_text <- str_glue("Analysis of water quality measurements and cleanup data at Fulham Reach Boat Club, 2023-2024") # Create caption caption_text <- create_mm_caption( mm_year = 2025, mm_week = 10, source_text = "River Water Quality" ) ### |- fonts ---- setup_fonts() fonts <- get_font_families() ### |- plot theme ---- # Start with base theme base_theme <- create_base_theme(colors) # Add weekly-specific theme elements weekly_theme <- extend_weekly_theme( base_theme, theme( # Weekly-specific modifications legend.position = "top", legend.title = element_text(size = rel(0.79)), legend.text = element_text(size = rel(0.71)), axis.title = element_text(size = rel(1.14)), axis.text = element_text(size = rel(0.86)), axis.text.y = element_blank(), axis.title.y = element_blank(), axis.ticks.y = element_blank(), axis.ticks.x = element_line(color = "gray", size = 0.5), axis.ticks.length = unit(0.2, "cm"), strip.text.y = element_text(size = rel(0.7), angle = 0), panel.border = element_blank(), panel.grid = element_blank(), panel.spacing = unit(1, "lines"), panel.spacing.y = unit(0, "lines"), ) ) # Set theme theme_set(weekly_theme) ``` #### 6. Plot ```{r} #| label: plot #| warning: false ### |- Plot ---- # P1. River Flow Influences Pollution Accumulation ---- flow_rubbish_plot <- joined_data |> filter(!is.na(flow_ms), !is.na(rubbish_weight_kg)) |> ggplot(aes(x = flow_ms, y = rubbish_weight_kg, color = season)) + # Geoms geom_point(size = 3.5, alpha = 0.85) + geom_smooth(method = "lm", se = TRUE, color = "black", linetype = "dashed", fill = "gray80", alpha = 0.4) + # Scale scale_x_continuous( breaks = seq(-0.3, 0.5, by = 0.1), labels = function(x) sprintf("%.1f", x) ) + scale_y_continuous(breaks = seq(0, 120, by = 20)) + scale_color_brewer(palette = "Set1") + # Labs labs( title = "River Flow Influences Pollution Accumulation", subtitle = "Negative correlation shows higher rubbish collection at lower flow rates", x = "Water Flow (m/s)", y = "Rubbish Collected (kg)", color = "Season" ) + # Theme theme_minimal() + theme( text = element_text(family = "sans", size = 12), plot.title = element_text(face = "bold", size = 16), plot.subtitle = element_text(size = 12, color = "gray30"), legend.position = "bottom", legend.title = element_text(face = "bold"), panel.grid.minor = element_line(color = "gray95"), panel.grid.major = element_line(color = "gray90"), axis.title = element_text(face = "bold"), plot.margin = margin(20, 20, 20, 20) ) # Add annotation for statistical significance # Get the linear model to extract statistics flow_model <- lm(rubbish_weight_kg ~ flow_ms, data = joined_data) model_p_value <- summary(flow_model)$coefficients[2,4] model_r_squared <- summary(flow_model)$r.squared # Format p-value and R-squared for display p_value_text <- ifelse(model_p_value < 0.001, "p < 0.001", paste("p =", round(model_p_value, 3))) r_squared_text <- paste("R² =", round(model_r_squared, 2)) # Add the annotation in upper right corner with improved formatting flow_rubbish_plot <- flow_rubbish_plot + annotate( "label", x = 0.3, y = 110, label = paste(r_squared_text, p_value_text, sep = "\n"), hjust = 1, size = 4, fontface = "bold", color = "black", fill = "white", alpha = 0.7 ) # P2. Seasonal Nutrient Dynamics in Thames Water ---- nutrient_scatter_plot <- frbc_water_clean |> filter(!is.na(nitrate_fww_mgl), !is.na(phosphate_fww_mgl)) |> ggplot(aes(x = nitrate_fww_mgl, y = phosphate_fww_mgl, color = season)) + # Geoms geom_point(size = 3.5, alpha = 0.85) + geom_smooth(method = "lm", se = FALSE, linewidth = 1.2) + # Scale scale_x_continuous( limits = c(0, 10.5), breaks = seq(0, 10, by = 2.5) ) + scale_y_continuous( limits = c(0, 1), breaks = seq(0, 1, by = 0.25) ) + scale_color_brewer(palette = "Set1") + # Labs labs( title = "Seasonal Nutrient Dynamics in Thames Water", subtitle = "Correlated nutrient levels suggest common sources vary by season", x = "Nitrate (mg/L)", y = "Phosphate (mg/L)", color = "Season" ) + # Facet facet_wrap(~ season, scales = "fixed") + # Theme theme_minimal() + theme( text = element_text(family = "sans", size = 12), plot.title = element_text(face = "bold", size = 16, hjust = 0.5), plot.subtitle = element_text(size = 12, color = "gray30", hjust = 0.5), legend.position = "none", panel.grid.minor = element_line(color = "gray95"), panel.grid.major = element_line(color = "gray90"), axis.title = element_text(face = "bold"), strip.text = element_text(face = "bold", size = 12), plot.margin = margin(20, 20, 20, 20) ) # Create a dataframe with R² values for each season r2_values <- tibble(season = character(), r2 = numeric()) for(current_season in unique(frbc_water_clean$season)) { # Skip if there's no data for this season season_data <- frbc_water_clean |> filter(season == current_season, !is.na(nitrate_fww_mgl), !is.na(phosphate_fww_mgl)) if(nrow(season_data) < 3) { # Add placeholder if not enough data r2_values <- r2_values |> add_row(season = current_season, r2 = NA) next } # Calculate linear model for this season season_model <- lm(phosphate_fww_mgl ~ nitrate_fww_mgl, data = season_data) r2_values <- r2_values |> add_row( season = current_season, r2 = summary(season_model)$r.squared ) } # Add a data frame with annotation positions for each season annotation_positions <- tibble( season = unique(frbc_water_clean$season), x = rep(2, length(unique(frbc_water_clean$season))), y = rep(0.7, length(unique(frbc_water_clean$season))) ) # Join R² values with positions annotation_data <- annotation_positions |> left_join(r2_values, by = "season") |> filter(!is.na(r2)) # Add annotations programmatically with one per facet nutrient_scatter_plot <- nutrient_scatter_plot + geom_label( data = annotation_data, aes(x = x, y = y, label = sprintf("R² = %.2f", r2)), inherit.aes = FALSE, hjust = 0, size = 3.5, fontface = "bold", fill = "white", alpha = 0.7 ) # Add reference line for theoretical threshold nutrient_scatter_plot <- nutrient_scatter_plot + geom_hline( yintercept = 0.1, linetype = "dashed", color = "darkred", alpha = 0.6 ) + annotate( "text", x = 9.5, y = 0.16, label = "Theoretical threshold (0.1 mg/L)", hjust = 1, size = 3, fontface = "italic", color = "darkred" ) # P3. Turbidity vs Water Flow with Algae Type turbidity_flow_plot <- frbc_water_clean |> filter(!is.na(flow_ms), !is.na(turbidity_ntu)) |> # Clean up algae type values for better display mutate( algae_type = case_when( is.na(algae_type) ~ "No data", algae_type == "" ~ "No data", TRUE ~ algae_type ) ) |> # Handle extreme outliers if present filter(turbidity_ntu <= 100) |> ggplot(aes(x = flow_ms, y = turbidity_ntu, color = algae_type)) + # Geoms geom_point(size = 3.5, alpha = 0.85) + geom_smooth( method = "loess", se = TRUE, color = "black", linetype = "dashed", fill = "gray80", alpha = 0.4, aes(group = 1) ) + # Scale scale_x_continuous( limits = c(-0.3, 0.6), breaks = seq(-0.3, 0.6, by = 0.1), labels = function(x) sprintf("%.1f", x) ) + scale_y_continuous( breaks = seq(0, 100, by = 20) ) + scale_color_brewer(palette = "Dark2") + # Labs labs( title = "Water Flow Affects Clarity and Algal Growth", subtitle = "Different algae types dominate at specific flow conditions", x = "Water Flow (m/s)", y = "Turbidity (NTU)", color = "Algae Type" ) + # Theme theme_minimal() + theme( text = element_text(family = "sans", size = 12), plot.title = element_text(face = "bold", size = 16), plot.subtitle = element_text(size = 12, color = "gray30"), legend.position = "bottom", legend.title = element_text(face = "bold"), panel.grid.minor = element_line(color = "gray95"), panel.grid.major = element_line(color = "gray90"), axis.title = element_text(face = "bold"), plot.margin = margin(20, 20, 20, 20), legend.key.size = unit(0.8, "lines"), legend.text = element_text(size = 10) ) + # Make the legend more readable with wrapped labels guides(color = guide_legend(nrow = 2, byrow = TRUE)) # Check if there are any blue-green scum algae points blue_green_points <- frbc_water_clean |> filter(!is.na(flow_ms), !is.na(turbidity_ntu)) |> filter(algae_type == "Blue-green scum") # Only add the annotation if blue-green points exist if(nrow(blue_green_points) > 0) { # Find average position of blue-green scum points avg_x <- mean(blue_green_points$flow_ms) avg_y <- mean(blue_green_points$turbidity_ntu) # Add an annotation at that position turbidity_flow_plot <- turbidity_flow_plot + geom_label( data = data.frame(x = avg_x, y = avg_y + 5), aes(x = x, y = y, label = "Blue-green algae common\nat these flow rates"), inherit.aes = FALSE, size = 3, fontface = "bold", color = "black", fill = "white", alpha = 0.7 ) } # Add an annotation about how flow affects turbidity turbidity_flow_plot <- turbidity_flow_plot + annotate( "label", x = 0.3, y = 80, label = "Moderate flow rates (around 0.3 m/s)\nshow peak turbidity with lower values\n at both slow and fast flows", size = 3, fontface = "bold", color = "black", fill = "white", alpha = 0.7 ) # Combined Plots ---- plot1 <- flow_rubbish_plot plot2 <- nutrient_scatter_plot plot3 <- turbidity_flow_plot combined_plot <- (plot1 + plot3) / plot2 + plot_layout(heights = c(1, 1)) + plot_annotation( title = title_text, subtitle = subtitle_text, caption = caption_text, theme = theme( plot.title = element_text( size = rel(1.8), family = fonts$title, face = "bold", color = colors$title, lineheight = 1.1, margin = margin(t = 5, b = 5) ), plot.subtitle = element_text( size = rel(0.95), family = fonts$subtitle, color = colors$subtitle, lineheight = 1.2, margin = margin(t = 5, b = 5) ), plot.caption = element_markdown( size = rel(0.75), family = fonts$caption, color = colors$caption, hjust = 0.5, margin = margin(t = 10) ), plot.margin = margin(t = 20, r = 10, b = 20, l = 10), ) ) ``` #### 7. Save ```{r} #| label: save #| warning: false ### |- plot image ---- save_plot_patchwork( combined_plot, type = "makeovermonday", year = 2025, week = 10, width = 12, height = 12 ) ``` #### 8. Session Info ::: {.callout-tip collapse="true"} ##### Expand for Session Info ```{r, echo = FALSE} #| eval: true #| warning: false sessionInfo() ``` ::: #### 9. GitHub Repository ::: {.callout-tip collapse="true"} ##### Expand for GitHub Repo The complete code for this analysis is available in [`mm_2025_10.qmd`](https://github.com/poncest/personal-website/blob/master/data_visualizations/MakeoverMonday/2025/mm_2025_10.qmd). For the full repository, [click here](https://github.com/poncest/personal-website/). ::: #### 10. References ::: {.callout-tip collapse="true"} ##### Expand for References 1. Data: - Makeover Monday 2025 Week 10: [River Water Quality](https://data.world/makeovermonday/2025-week-10-river-water-quality) :::