The File Drilldown page provides a detailed analysis of individual audio recording files. When a specific file is selected, it displays comprehensive information including all bird observations detected within the file, weather conditions during and around the recording time, and temporal patterns of detections. This tab combines tabular data presentation with weather visualization plots, showing wind speed and temperature trends with the audio file recording period highlighted. This section serves as the detailed follow-up to the File Analysis page, allowing users to examine the specific contents and environment context of individual audio recordings.
User Inputs
User inputs are found on the sidebar for this tab.
| Input Name | Input Choices | Description |
| Model-Match Scale
‘Model Proximity’ or ‘Model Adherence’ |
Numerical value or range of values between 0 and 1. | This input allows you to filter that output on this tab based on BirdNET-Analyzer’s ‘Confidence’ attribute. We have renamed this input to ‘BirdNET Happiness Scale’ because BirdNET’s confidence isn’t actually a probability and is not related to confidence intervals. Read more about that here. |
| File Lookup | Categorical selection based on files present in the dataset. Single selection only. | This controls what file information is displayed on the page. |
| Plot Settings | Weather Time Range with two choices:
1. 24 Hour Context 2. 1 Hour Context |
This input controls how the weather plots are displayed. If 24 hour context is selected, the plots will display the 12 hours before and after the audio file. If 1 hour context is selected, plots will display only the hour that audio was recorded. |
Output Explanation
Weather Plots
- `file_wind_plot` and `file_temp_plot` create weather visualizations by querying a SQLite weather database. The time range for plotting is determined by user input `input$weather_context`: either a 24-hour window (12 hours before and after the recording) or just the 1-hour recording period.
- Database queries are executed using `dbConnect` and `dbGetQuery` to retrieve wind speed and temperature data from separate tables, filtering by Unix timestamp ranges converted from the audio file’s datetime. The data converts Unix timestamps back to POSIXct for ggplot compatibility.
- Weather plots us ggplot with geom_line to show temporal trends and include a highlighted rectangular regions `geom_rect` that marks the exact 1-hour audio file recording period with a blue overlay.
File Scatterplot
- The scatterplot visualization is created using ggplot showing the temporal distribution of bird detections within the selected audio file. Points are positioned by detection time (calculated as `Date.Time + Begin.Time..s.` offset) on the x-axis and confidence level on the y-axis.
- Points are colored by species (`Common.Name`) to visually distinguish different bird species detected within the same audio file, allowing users to see species diversity and detection patterns over time.
- The ggplot object is converted to an interactive Plotly object using ggplotly for enhanced interaction.
File Details Table
- The detailed data table displays all observations from the selected file using the `file_details_data` reactive value.
- The table is formatted to include top filtering for searching within the file’s observations, fixed headers for navigation, and vertical and horizontal scrolling to accommodate the data.
