YarraWaterAnalysis

The YarraWaterAnalysis package provides tools for analyzing and visualizing streamflow data from the Yarra River at McMahons monitoring site.

Installation

You can install the development version of YarraWaterAnalysis from GitHub with:

# install.packages("devtools")
# devtools::install_github("ETC5523-2025/assignment-4-packages-and-shiny-apps-ChenLiu0123")

Example

This is a basic example which shows you how to solve a common problem:

library(YarraWaterAnalysis)
## basic example code

Calculate Flow Statistics 

calculate_flow_stats(yarra_water_data)

You can use this function to calculate basic statistics for Yarra River streamflow data

analyze_seasonal_patterns 

# Analyze seasonal patterns
seasonal_stats <- analyze_seasonal_patterns(yarra_water_data)
print(seasonal_stats)
#> # A tibble: 4 × 6
#>   season avg_flow median_flow max_flow min_flow n_observations
#>   <chr>     <dbl>       <dbl>    <dbl>    <dbl>          <int>
#> 1 Autumn     248.          75    14085        0           3347
#> 2 Summer     272.         102     6131        0           3535
#> 3 Winter     922.         283    17245        0           3407
#> 4 Spring    1023.         366    18687        0           3823

You can use this function to analyze seasonal patterns

detect_high_flow_events 

#> # A tibble: 6 × 18
#>   site_id site_name   datetime            data_type parameter_id parameter value
#>     <dbl> <chr>       <dttm>              <chr>            <dbl> <chr>     <dbl>
#> 1  229143 YARRA @ CH… 1975-10-26 23:59:59 Quantity          142. Streamfl… 18687
#> 2  229142 YARRA @ TE… 1975-10-26 23:59:59 Quantity          142. Streamfl… 17399
#> 3  229143 YARRA @ CH… 1977-07-01 23:59:59 Quantity          142. Streamfl… 17245
#> 4  229143 YARRA @ CH… 1975-10-27 23:59:59 Quantity          142. Streamfl… 16014
#> 5  229143 YARRA @ CH… 1977-06-20 23:59:59 Quantity          142. Streamfl… 15849
#> 6  229143 YARRA @ CH… 1977-06-19 23:59:59 Quantity          142. Streamfl… 15164
#> # ℹ 11 more variables: unit <chr>, quality <dbl>, resolution <chr>,
#> #   date <date>, year <dbl>, month <ord>, day <int>, season <chr>,
#> #   flow_category <chr>, log_flow <dbl>, flow_7day_avg <dbl>

You can use this function to detect high flow event

Interactive Exploration

Launch the Shiny application for interactive data exploration:

# Launch the interactive app
# launch_app()

The Shiny app provides:

  • Interactive time series visualization
  • Seasonal pattern analysis
  • Flow duration curves
  • Statistical summaries
  • Data filtering capabilities