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Water Quality at Sydney Beaches

This week we're exploring the water quality of Sydney's iconic beaches. The data is available at the New South Wales State Government Beachwatch website.

Beachwatch and our partners monitor water quality at swim sites to ensure that recreational water environments are managed as safely as possible so that as many people as possible can benefit from using the water.

Sydney beaches were in the news this summer with high rainfall causing concerns about the safety of the water.

The dataset this week includes both water quality and historical weather data from 1991 until 2025.

  • Has the water quality declined over this period?
  • How does rainfall impact E-coli bacteria levels?
  • Are some swimming sites particularly prone to high bacteria levels following rain?

Thank you to Jen Richmond (R-Ladies Sydney) for curating this week's dataset.

The Data

# Using R
# Option 1: tidytuesdayR R package 
## install.packages("tidytuesdayR")

tuesdata <- tidytuesdayR::tt_load('2025-05-20')
## OR
tuesdata <- tidytuesdayR::tt_load(2025, week = 20)

water_quality <- tuesdata$water_quality
weather <- tuesdata$weather

# Option 2: Read directly from GitHub

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')
# Using Python
# Option 1: pydytuesday python library
## pip install pydytuesday

import pydytuesday

# Download files from the week, which you can then read in locally
pydytuesday.get_date('2025-05-20')

# Option 2: Read directly from GitHub and assign to an object

water_quality = pandas.read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-05-20/water_quality.csv')
weather = pandas.read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-05-20/weather.csv')
# Using Julia
# Option 1: TidierTuesday.jl library
## Pkg.add(url="https://github.com/TidierOrg/TidierTuesday.jl")

using TidierTuesday

# Download files from the week, which you can then read in locally
download_dataset('2025-05-20')

# Option 2: Read directly from GitHub and assign to an object with TidierFiles

water_quality = read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-05-20/water_quality.csv")
weather = read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-05-20/weather.csv")

# Option 3: Read directly from Github and assign without Tidier dependencies
water_quality = CSV.read("https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-05-20/water_quality.csv", DataFrame)
weather = CSV.read("https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-05-20/weather.csv", DataFrame)

How to Participate

  • Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data. There are various moderating variables that affect all data, many of which might not have been captured in these datasets. As such, our suggestion is to use the data provided to practice your data tidying and plotting techniques, and to consider for yourself what nuances might underlie these relationships.
  • Create a visualization, a model, a Quarto report, a shiny app, or some other piece of data-science-related output, using R, Python, or another programming language.
  • Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.
  • Submit your own dataset!

PydyTuesday: A Posit collaboration with TidyTuesday

  • Exploring the TidyTuesday data in Python? Posit has some extra resources for you! Have you tried making a Quarto dashboard? Find videos and other resources in Posit's PydyTuesday repo.
  • Share your work with the world using the hashtags #TidyTuesday and #PydyTuesday so that Posit has the chance to highlight your work, too!
  • Deploy or share your work however you want! If you'd like a super easy way to publish your work, give Connect Cloud a try.

Data Dictionary

water_quality.csv

variable class description
region character Area of Sydney City
council character City council responsible for water quality
swim_site character Name of beach/swimming location
date date Date
time time Time of day
enterococci_cfu_100ml integer Enterococci bacteria levels in colony forming units (CFU) per 100 millilitres of water
water_temperature_c integer Water temperature in degrees Celsius
conductivity_ms_cm integer Conductivity in microsiemens per centimetre
latitude double Latitude
longitude double Longitude

weather.csv

variable class description
date date Date
max_temp_C double Maximum temperature in degrees Celsius
min_temp_C double Minimum temperature in degrees Celsius
precipitation_mm double Rainfall in millimetres
latitude double Latitude
longitude double Longitude

Cleaning Script

library(tidyverse)
library(here)
library(janitor)

# Historical weather data for Sydney provided by https://open-meteo.com/ API. 

weather <- readr::read_csv(here::here("data_raw", "open-meteo-33.85S151.20E51m.csv")) |>
  dplyr::select(date = latitude, 
         max_temp_C = longitude, 
         min_temp_C  = elevation, 
         precipitation_mm = utc_offset_seconds) |>
  dplyr::slice(-(1:2)) |>
  dplyr::mutate(date = ymd(date)) |>
  dplyr::mutate(latitude = -33.848858, 
         longitude = 151.19551) 
  
# Water quality data for Sydney beaches provided by https://www.beachwatch.nsw.gov.au/waterMonitoring/waterQualityData

water_quality <- readr::read_csv(here::here("data_raw", "Water quality-1746064496936.csv")) |>
  janitor::clean_names() |>
  rename(enterococci_cfu_100ml = enterococci_cfu_100m_l, conductivity_ms_cm = conductivity_m_s_cm) |>
  dplyr::mutate(date = dmy(date)) |>
  dplyr::mutate(
    dplyr::across(
      c("enterococci_cfu_100ml", "water_temperature_c", "conductivity_ms_cm"),
      as.integer
    )
  )