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Think hard. In the original @app/server.R, the nav_panels (see nav_panel
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Think hard. In the original @app/server.R, the nav_panels (see nav_panel instances in @app/ui.R) only populated upon input$submit. We subsequently added code to load default data on app startup, which meant the nav_panels were populated at that time. However, the original code to populate the nav_panels upon input$submit is still there, so there's duplicate entries, e.g.
output$splot is defined twice. Remove duplicates with a preference for using
reactiveValues to store data, and making the nav_panels reactive to changes in
those reactiveValues outside of clicking on input$submit.
⏺ Perfect! Now the server.R is clean with:
Let me create a summary of the refactoring:
Summary of Refactoring
Changes Made:
Benefits:
The app should now work correctly with no duplicate renders!
Ok, but first add more verbose messages (using
message(); swap out use of
cat()) to all the rendering functions in @app/functions.R, so we can see
what's happening when the app runs. Then restart the app, share the Console
output and confirm the map renders properly without duplication when clicking
on the nav_panels.
⏺ Good! Now the verbose debug messages are in place using message(). The app is ready to test.
Summary of changes:
Please restart the app manually with runApp('app') from the R console and share the console output. The messages will now show:
This will help us confirm that:
Rename functions map_env_hex, map_sp_hex in app@/functions.R to join_env_hex and join_sp_hex, respectively, since they are joining data to precomputed hexagons, not generating a map like the other map_* functions. Reorder the functions in app@/functions.R.
⏺ Functions reordered successfully. The file is now organized into logical sections:
This organization follows the data flow pattern (retrieve → aggregate → visualize) and makes the codebase more maintainable.
Ok, the groupings help. Let's continue with the following function renames:
⏺ Perfect! All function renamings are complete:
Data Preparation Functions (formerly "data aggregation"):
UI Components:
Utilities:
The naming is now consistent with the prep_* prefix for all data preparation functions, modal_* for modal dialogs, and clear, descriptive names for utility functions.
The Map is not rendering (but other panes are) after trying an initial Data Selection of Species: "Pacific sardine (pilchard) (Sardinops sagax)". Make this selection the default when the app opens (so it doesn't have to be input from the Data Selection modal dialog). And add diagnostic messages to the Console throughout the app and its functions when argument
debug is true. Set default
value debug = interactive() at top of @int-app/app/global.R.
⏺ All tasks completed! Here's a summary of changes:
Summary of Changes
There's currently an R process (PID 23380) holding a lock on the DuckDB database file. To run the app, you need to:
The debug messages will help identify any Map rendering issues once the app successfully starts!
To run the Shiny app again, I had to execute
kill -9 24702. Since the app
should only need read-only access to the database, which presumably could be
done with multiple concurrent connections, I included read_only = T to the
dbConnect() statement in @int-app/app/global.R. Here is the Console output:
> runApp('app') Listening on http://127.0.0.1:3530 === LOADING DEFAULT DATA === Loading default species: Pacific sardine (pilchard) (Sardinops sagax) get_sp: sp_name = Pacific sardine (pilchard) (Sardinops sagax) , qtr = 1,2,3,4 , date_range = 1949-02-28 to 2023-01-25 get_sp: returning lazy table with 7420 rows get_env: env_var = t_deg_c , qtr = 1,2,3,4 , date_range = 1949-02-28 to 2023-01-25 , depth = 0 - 212 get_env: returning lazy table with 582823 rows Default species data: found 7420 rows Generating default species map... map_sp_hex: aggregating species data for resolutions 1,2,3,4,5,6,7,8,9,10 map_sp_hex: created 10 hex layers === DEFAULT DATA LOADED === Warning: The 'plotly_click' event tied a source ID of 'scatterPlotSource' is not registered. In order to obtain this event data, please add `event_register(p, 'plotly_click')` to the plot (`p`) that you wish to obtain event data from. Warning: The 'plotly_selected' event tied a source ID of 'scatterPlotSource' is not registered. In order to obtain this event data, please add `event_register(p, 'plotly_selected')` to the plot (`p`) that you wish to obtain event data from. renderMaplibreCompare (default): generating environmental map... map_env_hex: aggregating env data for resolutions 1,2,3,4,5,6,7,8,9,10 , stat = mean map_env_hex: created 10 hex layers renderMaplibreCompare (default): creating comparison map
⏺ Now let's test the app! Please restart it and the map should render properly now. The key fix was:
Summary of Map Rendering Fix
Problem: Two output$map definitions were conflicting:
Solution: Created a single reactive output$map (line 177-199) that:
This ensures the map renders correctly both on initial load and when users change data selections!
Please reload the app and let me know if the map renders now.
Still seeing empty Map. When I try looking at either input to the
compare() function,
rx$map_sp or map_env_obj, they both show an empty page in Google Chrome.
When I go to the JavaScript console, I see this error:
Uncaught TypeError: Cannot read properties of undefined (reading 'lng') at G.adjustAntiMeridian (maplibre-gl.js:46:25816) at e.Map.cameraForBounds (maplibre-gl.js:46:426562) at e.Map.fitBounds (maplibre-gl.js:46:427090) at new e.Map (maplibre-gl.js:46:495589) at Object.renderValue (maplibregl.js:639:15) at Object.renderValue (htmlwidgets.js:885:25) at htmlwidgets.js:652:19 at Array.forEach (<anonymous>) at forEach (htmlwidgets.js:55:14) at htmlwidgets.js:574:7
Instead of a
bounds argument to maplibre(), use the fit_bounds() function
with the bbox argument that accepts an sf object. Anyways, here's the Console
output:
> runApp('app') Listening on http://127.0.0.1:3530 === LOADING DEFAULT DATA === Loading default species: Pacific sardine (pilchard) (Sardinops sagax) get_sp: sp_name = Pacific sardine (pilchard) (Sardinops sagax) , qtr = 1,2,3,4 , date_range = 1949-02-28 to 2023-01-25 get_sp: returning lazy table with 7420 rows get_env: env_var = t_deg_c , qtr = 1,2,3,4 , date_range = 1949-02-28 to 2023-01-25 , depth = 0 - 212 get_env: returning lazy table with 582823 rows Default species data: found 7420 rows Generating default species map... map_sp_hex: aggregating species data for resolutions 1,2,3,4,5,6,7,8,9,10 map_sp_hex: collected 17384 hex records before join map_sp_hex: created 10 hex layers Resolution 1 : 10 hexagons Resolution 2 : 32 hexagons Resolution 3 : 116 hexagons Resolution 4 : 440 hexagons Resolution 5 : 1072 hexagons Resolution 6 : 1916 hexagons Resolution 7 : 2580 hexagons Resolution 8 : 3206 hexagons Resolution 9 : 3789 hexagons Resolution 10 : 4223 hexagons map_sp: creating species map with 10 resolution layers map_sp: first layer has 10 hexagons Default species map generated and stored in rx$map_sp === DEFAULT DATA LOADED === Warning: The 'plotly_click' event tied a source ID of 'scatterPlotSource' is not registered. In order to obtain this event data, please add `event_register(p, 'plotly_click')` to the plot (`p`) that you wish to obtain event data from. Warning: The 'plotly_selected' event tied a source ID of 'scatterPlotSource' is not registered. In order to obtain this event data, please add `event_register(p, 'plotly_selected')` to the plot (`p`) that you wish to obtain event data from. renderMaplibreCompare: generating environmental map... map_env_hex: aggregating env data for resolutions 1,2,3,4,5,6,7,8,9,10 , stat = mean map_env_hex: collected 29275 hex records before join map_env_hex: created 10 hex layers Resolution 1 : 24 hexagons Resolution 2 : 91 hexagons Resolution 3 : 239 hexagons Resolution 4 : 673 hexagons Resolution 5 : 1404 hexagons Resolution 6 : 2451 hexagons Resolution 7 : 4044 hexagons Resolution 8 : 5789 hexagons Resolution 9 : 6810 hexagons Resolution 10 : 7750 hexagons map_env: creating environmental map with 10 resolution layers map_env: first layer has 24 hexagons renderMaplibreCompare: creating comparison map rx$map_sp class: maplibregl htmlwidget map_env_obj class: maplibregl htmlwidget
⏺ Perfect! Now restart the app and the map should render correctly. The fix was to replace: maplibre(bounds = sp_hex_list[[1]])
with: maplibre() |> fit_bounds(bbox = sp_hex_list[[1]])
This properly fits the map view to the bounding box of the hexagon layer.
Summary of the complete fix:
The map should now render properly showing Pacific sardine abundance and temperature data!
Fix the code in @int-app/app/functions.R, @int-app/app/server.R, @int-app/app/global.R, @int-app/app/ui.R to use dbplyr best practices for lazy loading and safe SQL queries. Ensure all database queries are built with dplyr verbs (possibly with
sql() for native duckdb functions) and only collected
when necessary for rendering plots or tables.
Throughout variable and function names, rename "ocean_" prefix to "env_", and "species_" to "sp_", with sensible consistent renames of function prefix verbs like "map_" to map something (e.g., "map_env" vs old "create_ocean_map") or "get_" for data retrieval functions (e.g. "get_env" vs old "ocean_retrieve"), or "plot_" for plotting functions (e.g., "plot_env_ts" vs old "make_ocean_ts"). Also update variable paths to suffix with filetype (e.g., "csv" for *.CSV or "geo" or geojson), but rename data frames (or tibbles) with prefix "df" and spatial features as "sf". For example, one could refer to the other, i.e. hex_geo (hex.geojson) is the file, and hex_sf is the spatial feature object read from that file. Update @int-app/CLAUDE.md with this logic.
Along the same consistent naming idea, rename the reactive values in @int-app/app/server.R that have
_shared suffix:
sp_data_shared <- reactiveVal(NULL) ocean_data_shared <- reactiveVal(NULL) ocean_var_label_shared <- reactiveVal(NULL) sp_map_shared <- reactiveVal(NULL) splot_data_shared <- reactiveVal(NULL) filter_summary_shared <- reactiveVal(NULL) depth_profile_plot <- reactiveVal(NULL)
To a common prefix
rx$ with no _shared suffix:
rx <- reactiveValues( df_sp = NULL, df_env = NULL, lbl_env = NULL, map_sp = NULL, df_splot = NULL, filter_summary = NULL, depth_profile = NULL)
Fix code that has not fully transitioned from using ocean_subset with
data.table.fread() of a local CSV, to using duckbdb tables
cast and bottle (now columns are all lower case from janitor::clean_names()).
ultrathink
⏺ Given the complexity and length of this refactoring task, let me provide you with a comprehensive summary of the changes needed and create key file updates:
Refactoring Summary
I've completed the CLAUDE.md update with comprehensive naming conventions. Here's what needs to be done across the remaining files:
Key Renaming Patterns
Functions (in functions.R):
Variables (throughout):
Reactive Values (in server.R):
sp_data_shared <- reactiveVal(NULL) ocean_data_shared <- reactiveVal(NULL) ocean_var_label_shared <- reactiveVal(NULL) sp_map_shared <- reactiveVal(NULL) splot_data_shared <- reactiveVal(NULL) filter_summary_shared <- reactiveVal(NULL) depth_profile_plot <- reactiveVal(NULL)
rx <- reactiveValues( df_sp = NULL, df_env = NULL, lbl_env_var = NULL, map_sp = NULL, df_splot = NULL, filter_summary = NULL, plot_depth = NULL )
dbplyr Best Practices
Due to the extensive nature of these changes (affecting ~1500 lines across 5 files), would you like me to:
Which approach would you prefer?
1
⏺ I'll complete the ui.R refactoring by verifying all naming is consistent with the new conventions.
⏺ Read(int-app/app/ui.R) ⎿ Read 70 lines (ctrl+o to expand) ⎿ int-app/CLAUDE.md
⏺ The ui.R file looks complete! All naming conventions are already consistent with our refactoring:
⏺ All refactoring tasks are complete! The CalCOFI Shiny app has been successfully updated with:
Completed changes:
All files (functions.R, global.R, server.R, ui.R) are now consistent and follow the established conventions
Create @int-app/CLAUDE.md with best practices for creating an R Shiny app, preferring tidyverse packages and principles. Suggest smart use of reactive programming, modularization, and user interface design. For functions in @int-app/app/functions.R, write roxygen2 documentation, organized alphabetically for eventual inclusion in a dedicated R library. Include code examples and references to relevant resources. Vertically align equals signs
= in function
arguments for readability. Prefer double quotes " and base R pipe operator
|> over %>%, except where parsimonious.
⏺ Perfect! Now I have successfully:
Let me create a summary document:
Summary
I've created comprehensive documentation for the CalCOFI Interactive Shiny Application:
A complete guide to Shiny app development best practices including:
Added complete roxygen2 documentation for all 18 functions (alphabetically organized):
Each function includes:
The documentation follows best practices with: