Overview | Prerequisites | Step 1: Initialize a surveillance system | Step 2: Define database tables | Step 3: Implement task functions | Data selector function | Action function | Step 4: Configure the task | Step 5: Execute the task | Step 6: Monitor and debug tasks | Advanced task features | Parallel processing | Dynamic plan generation | Best practices | Error handling | Data validation | Modular design | Summary | See also

Starting from the example package | 00_env_and_namespace.R | 01_definitions.R | 02_surveillance_systems.R | 03_tables.R | 04_tasks.R | 05_deliverables.R | 10_onLoad.R | 11_onAttach.R | Task files | weather_download_and_import_rawdata.R | weather_clean_data.R | weather_export_weather_plots.R | Notes | Next steps

Installation options | CRAN installation (limited functionality) | Full installation (database-driven surveillance) | Database setup | PostgreSQL installation | Option 1: Docker (recommended) | Option 2: System installation | Database schema setup | Environment variable configuration | Setting up .Renviron | Variable descriptions | Package behavior without database configuration | Docker | Moving from CRAN to full setup | Verification and troubleshooting | Verify your setup | Common issues | Next steps

What is CS9? | When to use CS9 | Key concepts and definitions | Surveillance system architecture | Example workflow | Plan-heavy or analysis-heavy tasks? | Other vignettes | Example | Surveillance system setup | Database table definition | Task configuration | for_each_plan | plnr::expand_list | for_each_analysis | universal_argset | upsert_at_end_of_each_plan | insert_at_end_of_each_plan | action_fn_name | data_selector_fn_name | schema | data_selector_fn | action_fn | Run the task | Common task patterns | Importing data | Analysis | Exporting multiple sets of results | Exporting combined results | Deployment | Getting started | Next steps

Overview | Themes | Color palettes | Number formatting | Norwegian number format | Date formatting | Norwegian date format | Journal formatting | Number format comparison | Date format comparison | Log scale transformations | Utility functions | Pretty breaks | Every nth label | Further reading

Overview | DBConnection_v9 | DBTable_v9

Location | Ages | Sex

National trend | Reference tables

Why org? | Installation | Quick start | Concept | Code requirements | Results requirements | Data requirements | Project structure | Core components | 1. org::initialize_project | 2. Run.R | 3. R/ directory | Example project structure | Research article versioning | Team collaboration | Best practices | Recommended structure | Alternative structures | RMarkdown project | Single folder project | Path naming conventions | Function reference | Core functions | File operations | Common workflows | Setting up a new analysis | Working with existing projects | Environment management | Path construction and cross-platform compatibility | Troubleshooting | Common issues | Path issues | Sourcing problems | Getting help

Introduction | Core Concepts | Broad technical terms | Different types of plans | Plan Examples | Basic Usage | Advanced Features | Data Management | Debugging Tools | Function Naming | Hash-based Caching | Best Practices | Next Steps

set_csfmt_rts_data_v1

csfmt_rts_data_v2 | Style | Ordering of variables | Time | Approved events | Location | Ages | Sex | Unified columns | Smart assignment | Context-specific columns | Examples | In action | Expand time to | Time series | Summary | Identifying data structure of one column | Reference (Location)

csfmt_rts_data_v2 | Smart assignment | Summary | Identifying data structure of one column

Colored maps | Automatic coloring by location_code | Customized coloring with external information | Labeled maps | Enrich plot with additional data | Add county name and population to Norway map | Add location name for ward and population for Oslo map

Naming scheme | Scope | Geogranularity | Type | Border | R class | Norway: default | Norway: split | Norway: insert oslo | Oslo ward: default

Core Concepts | Broad technical terms | Different types of plans | Plan Examples | Single-function plan | Multiple strata | Multiple variables | Multi-function plan

Single location | Covid-19 Hospitalization data | Data in cstidy format | Weekly observations | Multiple locations

sf for interactive maps

Splitting | Unnesting data.frames | Describing lists | Apply a function via hash table

Which year/week is this date? | Get isoyear/isoweek from a isoyearweek string

Introduction | Data example | Fitting using fit_attrib | County level | National level | Using the sim function | Estimating attributable mortality using est_attrib | Compare the national data to data aggregated from county to national level. | Aggregate from county/weekly to national/seasonal | Aggregating the national model per season | Calculate simulation quantiles. | Plot to compare the national with the aggregated county to national model | Comparing cumulative sums over seasons | Incident rate ratio