Sydney Beaches: Water Quality Reliability

Reliability Index: % of samples meeting ‘good’ standards (≤ 40 CFU/100ml). 80% reliability is the recommended threshold for safe swimming. Showing top and bottom 5 beaches by type

An analysis of water quality reliability across Sydney’s beaches, based on data from the NSW State Government Beachwatch program. This visualization highlights which beaches consistently meet safety standards for swimming and which ones struggle with water quality issues. The stark contrast between ocean beaches (mostly meeting standards) and harbor/river locations (frequently below threshold) reveals important patterns in Sydney’s water quality management.

Author

Steven Ponce

Published

May 18, 2025

Figure 1: Sydney Beaches: Water Quality Reliability chart showing the percentage of water samples meeting good standards (≤40 CFU/100ml) across different beach types. The chart is divided into three sections: Harbor/Bay/River Locations, Ocean Beaches, and Other Swimming Locations. Most ocean beaches exceed the 80% reliability threshold (shown by a dashed line), with Avalon reaching 97%. Harbor locations show mixed results, with many falling below the safe swimming threshold. The visualization highlights the contrast between consistently clean beaches and those that struggle to meet water quality standards.

Steps to Create this Graphic

1. Load Packages & Setup

Show code

```{r}
#| label: load
#| warning: false
#| message: false
#| results: "hide"

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
if (!require("pacman")) install.packages("pacman")
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
  scales,        # Scale Functions for Visualization
  glue           # Interpreted String Literals
  )
})

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  =  8,
  height =  10,
  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

```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false

tt <- tidytuesdayR::tt_load(2025, week = 20)

water_quality_raw <- tt$water_quality |> clean_names()
weather_raw <- tt$weather |> clean_names()

tidytuesdayR::readme(tt)
rm(tt)
```

3. Examine the Data

Show code

```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(water_quality_raw)
glimpse(weather_raw)
skimr::skim(water_quality_raw)
skimr::skim(weather_raw)
```

4. Tidy Data

Show code

```{r}
#| label: tidy
#| warning: false

### |-  tidy data ----
# Process and categorize raw water quality data
water_quality_processed <- water_quality_raw |>
    mutate(
        bacteria_category = case_when(
            enterococci_cfu_100ml <= 40 ~ "Good (<= 40 CFU)",
            enterococci_cfu_100ml > 40 & enterococci_cfu_100ml <= 200 ~ "Moderate (41-200 CFU)",
            enterococci_cfu_100ml > 200 ~ "Poor (> 200 CFU)",
            TRUE ~ "Unknown"
        ),
        bacteria_category = factor(
            bacteria_category,
            levels = c("Good (<= 40 CFU)", "Moderate (41-200 CFU)", "Poor (> 200 CFU)", "Unknown")
        )
    )

# Summarize water quality at the beach level
beach_reliability <- water_quality_processed |>
    group_by(swim_site, region) |>
    summarise(
        total_samples = n(),
        good_samples = sum(enterococci_cfu_100ml <= 40, na.rm = TRUE),
        reliability_index = good_samples / total_samples * 100,
        .groups = "drop"
    ) |>
    filter(total_samples >= 50) |>
    mutate(
        reliability_rating = case_when(
            is.na(reliability_index) ~ "Unknown",
            reliability_index >= 90 ~ "Excellent (≥90%)",
            reliability_index >= 80 ~ "Very Good (80-89%)",
            reliability_index >= 70 ~ "Good (70-79%)",
            reliability_index >= 60 ~ "Moderate (60-69%)",
            reliability_index < 60 ~ "Needs Improvement (<60%)"
        ),
        reliability_rating = factor(
            reliability_rating,
            levels = c(
                "Excellent (≥90%)", "Very Good (80-89%)", "Good (70-79%)",
                "Moderate (60-69%)", "Needs Improvement (<60%)"
            )
        )
    )

# Classify beaches and select top/bottom per type
beach_by_type <- beach_reliability |>
    mutate(
        beach_type = case_when(
            grepl("Harbour|Harbor|Bay|River|Cove", swim_site) ~ "Harbor/Bay/River",
            grepl("Beach|Ocean", swim_site) ~ "Ocean Beach",
            TRUE ~ "Other"
        )
    ) |>
    group_by(beach_type) |>
    mutate(rank_in_type = min_rank(desc(reliability_index))) |>
    filter(rank_in_type <= 5 | rank_in_type > n() - 5) |>
    mutate(
        status = if_else(reliability_index >= 80, "Meeting Standard", "Below Standard"),
        short_name = swim_site |>
            str_replace(" Beach$", "") |>
            str_replace(" Harbour$", "") |>
            str_replace(" Bay$", "") |>
            str_replace(" Baths$", "") |>
            str_replace(" Reserve$", "") |>
            str_replace(" Pool$", ""),
        short_name = reorder(short_name, reliability_index)
    )
```

5. Visualization Parameters

Show code

```{r}
#| label: params
#| include: true
#| warning: false

### |-  plot aesthetics ----
colors <- get_theme_colors(
    palette = c(
        "Meeting Standard" = "#1b9e77", 
        "Below Standard" = "#d95f02"
    )
)

### |-  titles and caption ----
title_text <- str_glue("Sydney Beaches: Water Quality Reliability")

subtitle_text <- str_glue(
    "Reliability Index: % of samples meeting 'good' standards (≤ 40 CFU/100ml)\n",
    "80% reliability is the recommended threshold for safe swimming\n",
    "Showing top and bottom 5 beaches by type"
)

# Create caption
caption_text <- create_social_caption(
  tt_year = 2025,
  tt_week = 20,
  source_text =  "BeachwatchNSW, Open-Meteo"
)

### |-  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(
    # Axis elements
    axis.title = element_text(color = colors$text, face = "bold", size = rel(0.8)),
    axis.text = element_text(color = colors$text, size = rel(0.7)),

    # Grid elements
    panel.grid.minor = element_blank(),
    panel.grid.major.y = element_blank(),
    panel.grid.major.x = element_line(color = "red", linewidth = 0.05),

    # Legend elements
    legend.position = "plot",
    legend.direction = "horizontal",
    legend.title = element_text(family = fonts$text, size = rel(0.8), face = "bold"),
    legend.text = element_text(family = fonts$text, size = rel(0.7)),

    # Style facet labels
    strip.text = element_text(size = rel(0.75), face = "bold",
                              color = colors$title, margin = margin(b = 5, t = 5)
    ),

    # Add spacing
    panel.spacing = unit(1.1, "lines"),
    strip.background = element_rect(fill = "#e0e0e0", color = NA),

    # Plot margins
    plot.margin = margin(t = 15, r = 15, b = 15, l = 15),
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

Show code

```{r}
#| label: plot
#| warning: false

# Final plot -----
p <- ggplot(beach_by_type, aes(x = reliability_index, y = short_name)) +
  # Geoms
  geom_col(aes(fill = status), width = 0.7) +
  geom_text(aes(label = sprintf("%d%%", round(reliability_index))),
    hjust = -0.2,
    size = 3.5
  ) +
  geom_text(
      data = tibble(
          beach_type = "Harbor/Bay/River",
          status = c("Meeting Standard", "Below Standard"),
          x = 95,
          y = c(7, 4),  
          label = c("Meeting Standard", "Below Standard")
      ),
      aes(x = x, y = y, label = label, color = status),
      hjust = 0,
      size = 4,
      fontface = "bold",
      inherit.aes = FALSE
  ) +
  geom_vline(xintercept = 80, linetype = "dashed", color = "gray40", linewidth = 0.3) +

  # Scales
  scale_fill_manual(values = colors$palette) +
  scale_color_manual(values = colors$palette) +
  scale_x_continuous(
    limits = c(0, 125),
    breaks = c(0, 20, 40, 60, 80, 100),
    labels = c("0%", "20%", "40%", "60%", "80%", "100%")
  ) +
  # Facets
  facet_wrap(~beach_type,
    scales = "free_y", ncol = 1,
    labeller = labeller(beach_type = c(
      "Harbor/Bay/River" = "Harbor/Bay/River Locations",
      "Ocean Beach" = "Ocean Beaches",
      "Other" = "Other Swimming Locations"
    ))
  ) +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = NULL,
    y = NULL,
    fill = "Status",
  ) +
  # 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 = 15)
    ),
    plot.subtitle = element_text(
      size = rel(0.85),
      family = fonts$subtitle,
      color = alpha(colors$subtitle, 0.9),
      lineheight = 1.2,
      margin = margin(t = 5, b = 20)
    ),
    plot.caption = element_markdown(
      size = rel(0.6),
      family = fonts$caption,
      color = colors$caption,
      hjust = 0.5,
      margin = margin(t = 10)
    ),
    legend.key = element_rect(fill = NA),
    strip.text = element_markdown(
      lineheight = 1.2,
      padding = margin(5, 5, 5, 5)
    )
  ) 
```

7. Save

Show code

```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot(
  plot = p, 
  type = "tidytuesday", 
  year = 2025, 
  week = 20, 
  width = 8,
  height = 10
)
```

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] here_1.0.1      glue_1.8.0      scales_1.3.0    janitor_2.2.0  
 [5] showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2   
 [9] lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4    
[13] purrr_1.0.2     readr_2.1.5     tidyr_1.3.1     tibble_3.2.1   
[17] ggplot2_3.5.1   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.49          httr2_1.0.6        htmlwidgets_1.6.4 
 [5] gh_1.4.1           tzdb_0.5.0         vctrs_0.6.5        tools_4.4.0       
 [9] generics_0.1.3     parallel_4.4.0     curl_6.0.0         gifski_1.32.0-1   
[13] fansi_1.0.6        pkgconfig_2.0.3    skimr_2.1.5        lifecycle_1.0.4   
[17] farver_2.1.2       compiler_4.4.0     textshaping_0.4.0  munsell_0.5.1     
[21] repr_1.1.7         codetools_0.2-20   snakecase_0.11.1   htmltools_0.5.8.1 
[25] yaml_2.3.10        crayon_1.5.3       pillar_1.9.0       camcorder_0.1.0   
[29] magick_2.8.5       commonmark_1.9.2   tidyselect_1.2.1   digest_0.6.37     
[33] stringi_1.8.4      rsvg_2.6.1         rprojroot_2.0.4    fastmap_1.2.0     
[37] grid_4.4.0         colorspace_2.1-1   cli_3.6.4          magrittr_2.0.3    
[41] base64enc_0.1-3    utf8_1.2.4         withr_3.0.2        rappdirs_0.3.3    
[45] bit64_4.5.2        timechange_0.3.0   rmarkdown_2.29     tidytuesdayR_1.1.2
[49] gitcreds_0.1.2     bit_4.5.0          ragg_1.3.3         hms_1.1.3         
[53] evaluate_1.0.1     knitr_1.49         markdown_1.13      rlang_1.1.6       
[57] gridtext_0.1.5     Rcpp_1.0.13-1      xml2_1.3.6         renv_1.0.3        
[61] vroom_1.6.5        svglite_2.1.3      rstudioapi_0.17.1  jsonlite_1.8.9    
[65] R6_2.5.1           systemfonts_1.1.0

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in tt_2025_20.qmd.

For the full repository, click here.

10. References

Expand for References

Data Sources:

TidyTuesday 2025 Week 20: Water Quality at Sydney Beaches

--- title: "Sydney Beaches: Water Quality Reliability" subtitle: "Reliability Index: % of samples meeting 'good' standards (≤ 40 CFU/100ml). 80% reliability is the recommended threshold for safe swimming. Showing top and bottom 5 beaches by type" description: "An analysis of water quality reliability across Sydney's beaches, based on data from the NSW State Government Beachwatch program. This visualization highlights which beaches consistently meet safety standards for swimming and which ones struggle with water quality issues. The stark contrast between ocean beaches (mostly meeting standards) and harbor/river locations (frequently below threshold) reveals important patterns in Sydney's water quality management." author: "Steven Ponce" date: "2025-05-18" categories: ["TidyTuesday", "Data Visualization", "R Programming", "2025"] tags: [ "water quality", "Sydney", "beaches", "environmental data", "public health", "swimming safety", "enterococci", "bacteria levels", "environmental monitoring", "coastal management", "water pollution", "recreation safety", "harbor pollution", "ocean beaches", "urban waterways" ] image: "thumbnails/tt_2025_20.png" format: html: toc: true toc-depth: 5 code-link: true code-fold: true code-tools: true code-summary: "Show code" self-contained: true theme: light: [flatly, assets/styling/custom_styles.scss] dark: [darkly, assets/styling/custom_styles_dark.scss] 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/TidyTuesday/2025/tt_2025_20.html" # description: "#TidyTuesday week 20: Exploring Sydney beaches water quality reliability - see which beaches are consistently safe for swimming and which fall below standards" # # twitter: true # linkedin: true # email: true # facebook: false # reddit: false # stumble: false # tumblr: false # mastodon: true # bsky: true --- ![Sydney Beaches: Water Quality Reliability chart showing the percentage of water samples meeting good standards (≤40 CFU/100ml) across different beach types. The chart is divided into three sections: Harbor/Bay/River Locations, Ocean Beaches, and Other Swimming Locations. Most ocean beaches exceed the 80% reliability threshold (shown by a dashed line), with Avalon reaching 97%. Harbor locations show mixed results, with many falling below the safe swimming threshold. The visualization highlights the contrast between consistently clean beaches and those that struggle to meet water quality standards.](tt_2025_20.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({ if (!require("pacman")) install.packages("pacman") 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 scales, # Scale Functions for Visualization glue # Interpreted String Literals ) }) ### |- figure size ---- camcorder::gg_record( dir = here::here("temp_plots"), device = "png", width = 8, height = 10, 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 tt <- tidytuesdayR::tt_load(2025, week = 20) water_quality_raw <- tt$water_quality |> clean_names() weather_raw <- tt$weather |> clean_names() tidytuesdayR::readme(tt) rm(tt) ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(water_quality_raw) glimpse(weather_raw) skimr::skim(water_quality_raw) skimr::skim(weather_raw) ``` #### 4. Tidy Data ```{r} #| label: tidy #| warning: false ### |- tidy data ---- # Process and categorize raw water quality data water_quality_processed <- water_quality_raw |> mutate( bacteria_category = case_when( enterococci_cfu_100ml <= 40 ~ "Good (<= 40 CFU)", enterococci_cfu_100ml > 40 & enterococci_cfu_100ml <= 200 ~ "Moderate (41-200 CFU)", enterococci_cfu_100ml > 200 ~ "Poor (> 200 CFU)", TRUE ~ "Unknown" ), bacteria_category = factor( bacteria_category, levels = c("Good (<= 40 CFU)", "Moderate (41-200 CFU)", "Poor (> 200 CFU)", "Unknown") ) ) # Summarize water quality at the beach level beach_reliability <- water_quality_processed |> group_by(swim_site, region) |> summarise( total_samples = n(), good_samples = sum(enterococci_cfu_100ml <= 40, na.rm = TRUE), reliability_index = good_samples / total_samples * 100, .groups = "drop" ) |> filter(total_samples >= 50) |> mutate( reliability_rating = case_when( is.na(reliability_index) ~ "Unknown", reliability_index >= 90 ~ "Excellent (≥90%)", reliability_index >= 80 ~ "Very Good (80-89%)", reliability_index >= 70 ~ "Good (70-79%)", reliability_index >= 60 ~ "Moderate (60-69%)", reliability_index < 60 ~ "Needs Improvement (<60%)" ), reliability_rating = factor( reliability_rating, levels = c( "Excellent (≥90%)", "Very Good (80-89%)", "Good (70-79%)", "Moderate (60-69%)", "Needs Improvement (<60%)" ) ) ) # Classify beaches and select top/bottom per type beach_by_type <- beach_reliability |> mutate( beach_type = case_when( grepl("Harbour|Harbor|Bay|River|Cove", swim_site) ~ "Harbor/Bay/River", grepl("Beach|Ocean", swim_site) ~ "Ocean Beach", TRUE ~ "Other" ) ) |> group_by(beach_type) |> mutate(rank_in_type = min_rank(desc(reliability_index))) |> filter(rank_in_type <= 5 | rank_in_type > n() - 5) |> mutate( status = if_else(reliability_index >= 80, "Meeting Standard", "Below Standard"), short_name = swim_site |> str_replace(" Beach$", "") |> str_replace(" Harbour$", "") |> str_replace(" Bay$", "") |> str_replace(" Baths$", "") |> str_replace(" Reserve$", "") |> str_replace(" Pool$", ""), short_name = reorder(short_name, reliability_index) ) ``` #### 5. Visualization Parameters ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- colors <- get_theme_colors( palette = c( "Meeting Standard" = "#1b9e77", "Below Standard" = "#d95f02" ) ) ### |- titles and caption ---- title_text <- str_glue("Sydney Beaches: Water Quality Reliability") subtitle_text <- str_glue( "Reliability Index: % of samples meeting 'good' standards (≤ 40 CFU/100ml)\n", "80% reliability is the recommended threshold for safe swimming\n", "Showing top and bottom 5 beaches by type" ) # Create caption caption_text <- create_social_caption( tt_year = 2025, tt_week = 20, source_text = "BeachwatchNSW, Open-Meteo" ) ### |- 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( # Axis elements axis.title = element_text(color = colors$text, face = "bold", size = rel(0.8)), axis.text = element_text(color = colors$text, size = rel(0.7)), # Grid elements panel.grid.minor = element_blank(), panel.grid.major.y = element_blank(), panel.grid.major.x = element_line(color = "red", linewidth = 0.05), # Legend elements legend.position = "plot", legend.direction = "horizontal", legend.title = element_text(family = fonts$text, size = rel(0.8), face = "bold"), legend.text = element_text(family = fonts$text, size = rel(0.7)), # Style facet labels strip.text = element_text(size = rel(0.75), face = "bold", color = colors$title, margin = margin(b = 5, t = 5) ), # Add spacing panel.spacing = unit(1.1, "lines"), strip.background = element_rect(fill = "#e0e0e0", color = NA), # Plot margins plot.margin = margin(t = 15, r = 15, b = 15, l = 15), ) ) # Set theme theme_set(weekly_theme) ``` #### 6. Plot ```{r} #| label: plot #| warning: false # Final plot ----- p <- ggplot(beach_by_type, aes(x = reliability_index, y = short_name)) + # Geoms geom_col(aes(fill = status), width = 0.7) + geom_text(aes(label = sprintf("%d%%", round(reliability_index))), hjust = -0.2, size = 3.5 ) + geom_text( data = tibble( beach_type = "Harbor/Bay/River", status = c("Meeting Standard", "Below Standard"), x = 95, y = c(7, 4), label = c("Meeting Standard", "Below Standard") ), aes(x = x, y = y, label = label, color = status), hjust = 0, size = 4, fontface = "bold", inherit.aes = FALSE ) + geom_vline(xintercept = 80, linetype = "dashed", color = "gray40", linewidth = 0.3) + # Scales scale_fill_manual(values = colors$palette) + scale_color_manual(values = colors$palette) + scale_x_continuous( limits = c(0, 125), breaks = c(0, 20, 40, 60, 80, 100), labels = c("0%", "20%", "40%", "60%", "80%", "100%") ) + # Facets facet_wrap(~beach_type, scales = "free_y", ncol = 1, labeller = labeller(beach_type = c( "Harbor/Bay/River" = "Harbor/Bay/River Locations", "Ocean Beach" = "Ocean Beaches", "Other" = "Other Swimming Locations" )) ) + # Labs labs( title = title_text, subtitle = subtitle_text, caption = caption_text, x = NULL, y = NULL, fill = "Status", ) + # Theme theme( plot.title = element_text( size = rel(1.8), family = fonts$title, face = "bold", color = colors$title, lineheight = 1.1, margin = margin(t = 5, b = 15) ), plot.subtitle = element_text( size = rel(0.85), family = fonts$subtitle, color = alpha(colors$subtitle, 0.9), lineheight = 1.2, margin = margin(t = 5, b = 20) ), plot.caption = element_markdown( size = rel(0.6), family = fonts$caption, color = colors$caption, hjust = 0.5, margin = margin(t = 10) ), legend.key = element_rect(fill = NA), strip.text = element_markdown( lineheight = 1.2, padding = margin(5, 5, 5, 5) ) ) ``` #### 7. Save ```{r} #| label: save #| warning: false ### |- plot image ---- save_plot( plot = p, type = "tidytuesday", year = 2025, week = 20, width = 8, height = 10 ) ``` #### 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 [`tt_2025_20.qmd`](https://github.com/poncest/personal-website/blob/master/data_visualizations/TidyTuesday/2025/tt_2025_20.qmd). For the full repository, [click here](https://github.com/poncest/personal-website/). ::: #### 10. References ::: {.callout-tip collapse="true"} ##### Expand for References 1. Data Sources: - TidyTuesday 2025 Week 20: [Water Quality at Sydney Beaches](https://github.com/rfordatascience/tidytuesday/blob/main/data/2025/2025-05-20) :::