Introduction | Interactive Input Functions | Yes/No Questions | ORCID Identifiers | ROR Identifiers | Email Addresses | URLs | Language Codes | Licenses | Menu Selection | Validation Functions | ORCID Validation | ROR Validation | Email Validation | URL Validation | Language Code Validation | Integration with Interactive Functions | Non-Interactive Behaviour | Error Handling | Best Practices | Common Patterns | Validating User Input | Cleaning Existing Data | Optional Fields | Batch Processing | Related Functions

Why bother setting up an org_list? | The org_item class | Roles: rightsholder, funder, and publisher | The org_list class | Creating an org_list programmatically | Creating an org_list interactively | Getting the default org_list for a project | Adding an org_item to an existing org_list | The built-in INBO organisation list | FAQ

Introduction | Why Use Individual Helper Functions? | Storing Individual Information | Selecting and Reusing Individuals | Converting Individual Information | To Person Objects | To Badge Format | To Data Frames | Adding Individuals to Files | Role Mapping for Different File Types | Helper Functions for Roles | Checking Person Roles | Selecting Roles Interactively | Typical Workflow | Working with Multiple Languages | Best Practices | Related Functions

Introduction | Basic usage | Package contents | Schema information | Important notes | CSV format required | Metadata integration | Recursive search | Use cases | Data sharing | Data validation | Data catalogs | See also

Introduction | Basic usage | Example: case conversion | Multiple columns | Use cases | Unsupported data type | Storage optimization | Data standardization | Important notes | Limitations

Introduction | Basic usage | Package contents | Schema information | Important notes | CSV format required | Metadata integration | Recursive search | Use cases | Data sharing | Data validation | Data catalogs | See also

Introduction | Basic usage | Example: case conversion | Multiple columns | Use cases | Unsupported data type | Storage optimization | Data standardization | Important notes | Limitations

Goal | Benefits of writing with bookdown | Available templates in INBOmd | Installation | Using INBOmd | Recommended way to start a new report | Legacy way to start a new document from RStudio templates | Rendering a document | Examples of INBOmd

Goal | Benefits of writing with bookdown | Available templates in INBOmd | Installation | Using INBOmd | Recommended way to start a new report | Legacy way to start a new document from RStudio templates | Rendering a document | Examples of INBOmd

New philosophy | Why bother to set an org_list object? | The org_item class | The org_list class | Using the organisations default org_list | Setting the default | Getting the default | Adding an org_item to an org_list object | Defining your organisations' coding style rules | Defining your organisations' pkgdown styling | FAQ

What is Zenodo? | What is ORCID? | Why integrate Zenodo and ORCID with GitHub? | Setup ORCID | Once | Setup Zenodo | Once per repository | What happens next? | Managing Zenodo metadata via DESCRIPTION | Contributor roles | Funding information

New philosophy | Why bother to set an org_list object? | The org_item class | The org_list class | Using the organisations default org_list | Setting the default | Getting the default | Adding an org_item to an org_list object | Defining your organisations' coding style rules | Defining your organisations' pkgdown styling | FAQ

What is Zenodo? | What is ORCID? | Why integrate Zenodo and ORCID with GitHub? | Setup ORCID | Once | Setup Zenodo | Once per repository | What happens next? | Managing Zenodo metadata via DESCRIPTION | Contributor roles | Funding information

Starting a new project from scratch | Add or update checklist tools in an existing project | To do once after activating version control

Starting a new project from scratch | Add or update checklist tools in an existing project | To do once after activating version control

Introduction | Reading metadata | Updating the optional metadata

Introduction | Data Storage | On a File System | In Git Repositories | Timings | Writing Data | Reading Data

Introduction | Setup | Structuring Git2rdata Objects Within a Project | Storing Dataframes ad Hoc into a Git Repository | First Commit | Second Commit | Third Commit | Scripted Workflow for Storing Dataframes | R Package Workflow for Storing Dataframes | Analysis Workflow with Reproducible Data | Long running analysis

Introduction | Reading metadata | Updating the optional metadata

Introduction | Data Storage | On a File System | In Git Repositories | Timings | Writing Data | Reading Data

Introduction | Setup | Structuring Git2rdata Objects Within a Project | Storing Dataframes ad Hoc into a Git Repository | First Commit | Second Commit | Third Commit | Scripted Workflow for Storing Dataframes | R Package Workflow for Storing Dataframes | Analysis Workflow with Reproducible Data | Long running analysis

Coding style | checklist default | Rules for coding style | Static code analysis checks | Using a different coding style | References

Rules for folder names | Rules for file names | Rules for graphical file names

Available checks | Extensive report on checks | Errors, warnings and notes | Changes | Session info | Making code findable and citable | Integration with Zenodo and ORCID | Bundling your code in a package | Benefits of using a package for your analysis

Starting an R package from scratch | Prepare online setup | Local setup | Finalise online setup in GitHub | Get it working with an existing package or update the checklist | Prerequisites | Online setup on GitHub | Troubleshooting | Failing check package on GitHub Actions

Coding style | checklist default | Rules for coding style | Static code analysis checks | Using a different coding style | References

Rules for folder names | Rules for file names | Rules for graphical file names

Available checks | Extensive report on checks | Errors, warnings and notes | Changes | Session info | Making code findable and citable | Integration with Zenodo and ORCID | Bundling your code in a package | Benefits of using a package for your analysis

Starting an R package from scratch | Prepare online setup | Local setup | Finalise online setup in GitHub | Get it working with an existing package or update the checklist | Prerequisites | Online setup on GitHub | Troubleshooting | Failing check package on GitHub Actions

Introduction | Palettes | inbo_palette() | vlaanderen_palette() | traffic_palette() | Flanders colours | Simulate effect of colour blindness on the perception of palettes | Named INBO colours | Named Flanders colours

Introduction | Palettes | inbo_palette() | vlaanderen_palette() | traffic_palette() | Flanders colours | Simulate effect of colour blindness on the perception of palettes | Named INBO colours | Named Flanders colours

title: "Calculating SHA1 hashes with digest() and sha1()"author: "Thierry Onkelinx and Dirk Eddelbuettel"date: "Written Jan 2016, updated Jan 2018 and Oct 2020"css: "water.css" | Short intro on hashes | Difference between digest() and sha1() | Choosing digest() or sha1() | Creating a sha1 method for other classes | How to | summary.lm | lm | Using hashes to track changes in analysis

The multimput package | Very short intro to multiple imputation | Short intro to multiple imputation | The dataset | Create the imputation model | Apply the imputation model | Aggregate the imputed dataset | Model the aggregated imputed dataset | Simple example | Return only the parameters associated with fYear | Predict a smoother for predefined values | Compare the results using different imputation models | Modelling aggregated data with glm.nb | Modelling aggregated data with inla | References

The multimput package | Very short intro to multiple imputation | Short intro to multiple imputation | The dataset | Create the imputation model | Apply the imputation model | Aggregate the imputed dataset | Model the aggregated imputed dataset | Simple example | Return only the parameters associated with fYear | Predict a smoother for predefined values | Compare the results using different imputation models | Modelling aggregated data with glm.nb | Modelling aggregated data with inla | References

Introduction | Maintaining Variable Classes | Efficiency Relative to Storage and Time | Optimizing File Storage | Optimized for Version Control | Basic Usage | Storing Optimized | Storing Verbose | Efficiency Relative to File Storage | Reading Data | Missing Values

Introduction | Setup | Assumptions | Sorting Observations | Sorting Variables | Handling Factors Optimized | Relabelling a Factor

Introduction | Maintaining Variable Classes | Efficiency Relative to Storage and Time | Optimizing File Storage | Optimized for Version Control | Basic Usage | Storing Optimized | Storing Verbose | Efficiency Relative to File Storage | Reading Data | Missing Values

Introduction | Setup | Assumptions | Sorting Observations | Sorting Variables | Handling Factors Optimized | Relabelling a Factor

Concept | Workflow | Import the relevant data | Generate the analysis objects | Fit the models | Extract the results | Traceable and auditable | Portable | Efficient | Metadata | Definition of the model | Scheme, species and location | Time-stamp | Used packages | Linked analyses | Available models | n2k_import() | n2k_inla() | n2k_inla_comparison() | n2k_composite() | n2k_hurdle_imputed() | n2k_aggregate() | n2k_modelimputed() | Manifest | Docker

A Simple, Stable and Transparent Classification of Effects | A Detailed Classification | Classification Using effectclass | Suggestion for Displaying Effects in a Tabular Format

stat_fan() | stat_effect() and scale_effect() | format_ci() and classification() | References

A Simple, Stable and Transparent Classification of Effects | A Detailed Classification | Classification Using effectclass | Suggestion for Displaying Effects in a Tabular Format

stat_fan() | stat_effect() and scale_effect() | format_ci() and classification() | References

Do not use absolute file paths | Easiest solution: use relative paths within the project | Alternative solution: use relative paths between projects | Fallback solution: ask the user to specify the path

Do not use absolute file paths | Easiest solution: use relative paths within the project | Alternative solution: use relative paths between projects | Fallback solution: ask the user to specify the path

Introduction | Important | Using the default theme from INBOtheme | Using a non-default theme from INBOtheme | Prepare the code and data | Default: theme_inbo() | theme_inbo() with default background | theme_inbo() with transparent background | theme_vlaanderen2015() | theme_vlaanderen2015() with default background | theme_vlaanderen2015() with transparent background | theme_elsevier()

Introduction | Default: theme_inbo() | theme_vlaanderen2015() | theme_elsevier()

Introduction | Important | Using the default theme from INBOtheme | Using a non-default theme from INBOtheme | Prepare the code and data | Default: theme_inbo() | theme_inbo() with default background | theme_inbo() with transparent background | theme_vlaanderen2015() | theme_vlaanderen2015() with default background | theme_vlaanderen2015() with transparent background | theme_elsevier()

Introduction | Default: theme_inbo() | theme_vlaanderen2015() | theme_elsevier()

For a simple R project | A more complex project | A project with several reports with different data and media

Configuration | Basic usage | Custom dictionary | Rd files | Quarto projects

For a simple R project | A more complex project | A project with several reports with different data and media

Configuration | Basic usage | Custom dictionary | Rd files | Quarto projects

Generate data | Overdispersion | Fitting the models | Distribution checks | Dispersion checks | Zero inflation | Underdispersion

1-D Random walks | Simulating random walks | Inspecting simulated random walks | Summarising and subsetting | Transformations | Centring

Simulating random intercepts | Inspecting simulated random intercepts | Link functions | Centring and quantiles | Priors for fixed effects

Generate data | Overdispersion | Fitting the models | Distribution checks | Dispersion checks | Zero inflation | Underdispersion

1-D Random walks | Simulating random walks | Inspecting simulated random walks | Summarising and subsetting | Transformations | Centring

Simulating random intercepts | Inspecting simulated random intercepts | Link functions | Centring and quantiles | Priors for fixed effects

Introduction | When to Split the Dataframe

Introduction | When to Split the Dataframe

Short introduction to Generalized Random Tessellation Stratified sampling (GRTS) | Quadrant recursive map | Reverse hierarchical ordering | Randomisation | Selection of the sample | Benefits of the normal hierarchical order | Benefits of the revese hierarchical order | Main functionality of grtsdb | Nested samples | Repeated samples | n-dimensional GRTS

Short introduction to Generalized Random Tessellation Stratified sampling (GRTS) | Quadrant recursive map | Reverse hierarchical ordering | Randomisation | Selection of the sample | Benefits of the normal hierarchical order | Benefits of the revese hierarchical order | Main functionality of grtsdb | Nested samples | Repeated samples | n-dimensional GRTS