How the pieces fit together | Starting from scratch | Prerequisites | Install Git and Docker | Set the Git user | Get the code | Environment variables | AirTable | Data directory | DNS | TLS certificate | Start the service | Cron job | Check that it works | General maintenance | Uptime alert | Weekly redeploy | Viewing, changing and checking the cron job | Restarting the service | Backing up the data and secrets | Downloading a backup | Restoring a backup | Updating the operating system | Recreating the certificate | Troubleshooting | Transferring maintenance | Operations only | Transferring credentials | Other items to update

Introduction | A working example | Limitations of repeated observations models | References

Introduction | Computation time scaling | Improving computation times | References

Introduction | A working example | References

Introduction | The generalized dynamic phylogenetic model | A working example | Available response distributions | Conclusion | References

Introduction | Integration with the Bioconductor ecosystem | Citation | Gene-environment interaction analysis | Data expectations | RAMEN's underlying assumptions | Genomic data | DNA methylation data | Exposome data | Covariates to include | Workflow overview | Install RAMEN and set up environment | Identify VML and summarize their methylation state | Note on argument: var_method | Note on argument: var_distribution | Running RAMEN::findVML() | Identify cis SNPs | Conduct variable selection on genome and exposome variables | Author's note about variables interpretation | Identify the best explanatory model (G/E/G+E/GxE) per VML | Fit and compare the models and select the best one | Remove poor performing winning models | Author's note about model interpretation | Variations to the standard workflow | Frequently Asked Questions | How can I save the RAMEN data frames that have columns with lists as observations? | How can I set up the parallel backend? | Session info

Prerequisites | 1. Google Cloud Console | 2. Authorize once, locally | 3. Send-as alias | 4. Code | 5. Environment variables | 6. Deploy credentials to the droplet | 7. Verify | 8. Transfer maintenance | Editor search endpoints | GET /send_search | GET /click/ | GET /list_searches | GET /deactivate_search | Notify email cache

Comparison of ODK related software packages (non-ODK core) | Related tools beyond R | Comparison of OData related R packages

Configure ruODK | Best practice: ru_setup | Permanent defaults: Environment variables | The hard way: Per function call | Moving across forms | Legacy support for deprecated ODK Central features

Data | Map geopoints | Mapview using sf | GGplot using sf | Leaflet using sf | Leaflet using extracted coordinate components in tbl_df | Map geotraces (lines) | Leaflet using sf and extracted coordinates | Map geoshapes (polygons) | Outlook

Automatic sound event detection | Signal detection theory applied to bioacoustics | Improving detection speed | Additional tips | References

Overview | Installation | A typical workflow | Step 1 — Find an aggregate | Step 2 — Inspect the metadata | Step 3 — Retrieve data | Specifying localities | Specifying periods | Filtering with classifications | Automatic validation | Large queries | Browsing the survey catalog | Special values

Multiple results | Better targeted results | countrycode | bounds | proximity | Confidence | Retrieve more information from the API | language | Annotations | roadinfo | Abbreviated addresses | address_only | Vectorised arguments | Further information

Setup | Authentication | Rate limit | Privacy | (Don't) show API key | Altogether now | Forward geocoding | Reverse geocoding | Caching | Further information

1. Direct calls of the update functions | 2. Update from a local CRAN mirror

df_list | json_list | geojson_list | url_only | xml | Return query text

Introduction | Prerequisites | 1. Load the data | 1.1 HPO gene-phenotype associations | 1.2 DrugBank targets | 2. Link drug targets to phenotypes | 3. Keep the informative phenotypes | 4. Group phenotypes into clinical domains | 5. Interpret the phenotypes using the direction of drug action | Bivalirudin and F2: phenotypes predict the main adverse effect | Leuprolide and the GnRH axis: phenotypes predict the therapeutic effect | Calcitriol and VDR: the direction is reversed | 6. Compare the phenotype profiles | Conclusion

Introduction | Scientific case: finding a mechanistic substitute for lepirudin | Prerequisites | 1. Parse the DrugBank records | 2. Target actions now carry the drug | Why the drug link matters | 3. Map the targets to genes | 4. Find drugs that share lepirudin's mechanism | 5. Compare the wider interaction profiles | Summary

rglobi vignette - an R interface to the aggregated biotic interaction data of GloBI (https://www.globalbioticinteractions.org) | About the package | Quick start | First, install rglobi | Find interactions involving a certain species | Find interactions in a geographic areas

Glossary | Synthesize sounds | Create master sound file for playback | Align re-recorded sounds | Manually fixing alignment | Further aligning | References

Required data structure | Setting reference sounds | Measuring degradation | Data format | Visual inspection | Blur ratio | Spectrum blur ratio | Excess attenuation | Envelope correlation | Spectrum correlation | Signal-to-noise ratio | Tail-to-signal ratio | Spectrogram correlation | Other measurements | Noise profiles | References

Introduction | Installation | Quick Start | 1. Load your site data | 2. Get lake boundary | 3. Calculate fetch | 4. Visualize | Understanding the Results | Exposure Categories | Configuration Options | NHD Integration | Working with Multiple Lakes | Exporting Results

Introduction | Installation | The main rerddap function definitions | The basic steps in finding and obtaining the desired data | Finding the data you want | Get the needed information about the dataset (rerddap::info() ) | Think about what you are going to do. | Make the request for the data (griddap() or tabledap() ). | Requesting gridded data using griddap() | Thinning the request - Strides | Requesting table data using tabledap() | tabledap() and regular expressions | More detailed examples | griddap() examples | MUR SST | VIIRS SST and Chlorophyll | Temperature at 70m in the north Pacific from the SODA model output | Irish Marine Institute | IFREMER | tabledap() examples | CalCOFI data | Parquet files | CPS Trawl Surveys | NDBC Buoys | IOOS Glider Data | Animal Telemetry Network (ATN) | California Current System Integrated Ecosystem Assessment (CCSIEA) | Additional information | Interactive plots | Passing an extract to the terra package or other packages that use "GDAL" - do so with care. | Cacheing, "last", "now", idempotency, and a gotcha | Reading data from a netCDF file.

Introduction | Setup | A working example

Overview | Setup | Fit the model | Ancestral states on the latent scale | Visualizing latent ancestral states on the tree | Joint latent-space scatterplot | Ancestral states on the response scale | Pie chart tree: response-scale probabilities | Working with raw posterior draws | Identifying nodes on the tree | Selecting specific nodes and variables

Introduction | A working example

Introduction | A working example

Introduction | A working example

Introduction | Default priors | Limitations on setting priors

Optimizing template-based detection | Detecting several templates | References

How it works | Optimizing energy-based detection | References

Citation | BibTeX and R | Conceptual differences between BibTeX and CFF | BibTeX definitions | Entries | Fields | Citation File Format | Mapping strategy | Mapping tables | Proposed crosswalk | Transformation semantics | Entry/key type crosswalk | Fields/key crosswalk | BibLaTeX | Limitations | Design decisions | Entry models | @article | @book / @inbook | @booklet | @conference / @inproceedings | @incollection | @manual | @mastersthesis / @phdthesis | @misc | @proceedings | @techreport | @unpublished | Conclusion | Appendix A: @inbook in BibTeX and BibLaTeX | Appendix B: CFF key: type values | References

Summary | Details | abstract | authors | cff-version | commit | contact | date-released | doi | identifiers | keywords | license | license-url | message | preferred-citation | references | repository | repository-artifact | repository-code | title | type | url | version | References

What is a CITATION.cff file? | Creating a CITATION.cff file for your R package | Modifying your CITATION.cff file | Adding new keys | Persons and references | Write the modified CITATION.cff file | References

1. The check function | 2. The output function | 3. Creating new checks | 3.1 New Local Checks (for package users) | 3.2 New pkgcheck Checks (for pkgcheck developers) | 3.2a Check types | 3.2b Testing new checks

vis_dat | vis_miss | vis_compare | vis_expect | vis_cor | vis_value | vis_binary | vis_guess | vis_histogram() | Interactivity | Future work

Introduction | The nyc_pull_dataset() function | The nyc_any_dataset() function | Rule of Thumb | Real World Example | Mini analysis | Summary | How to Cite

Configure ruODK | OData service document | OData metadata document | OData submission data documents | Detour: Data rectangling | DIY rectangling | Visualise data | Datatable | Map | Summarising data | Export | ruReady to ODK?

Scope | Three ways to happiness | Setup ruODK | Projects | Forms | List forms for a project | Inspect form schema | Show details of one form | Submissions | Get all submissions for one form | List submissions for one form | Get submission data | Parse submissions | Outlook

Note on NPI Downloadable Files | Run npi_search() on multiple search terms | Combine and transform saved results

Search registry | Increasing number of records returned | Obtaining more human-readable output

Installation | Connecting to a Server | Exploring Programs and Trials | Fetching Phenotypic Data | Fetching Genotypic Data | Authentication | Handling Credentials Safely | Caching and Parallel Fetching | Comparison with QBMS | Same workflow, different style | References

Instalación | Desde CRAN (versión estable) | Desde GitHub (versión de desarrollo) | Dependencias por funcionalidad | Requisitos del sistema | Windows | macOS | Linux (Ubuntu/Debian) | Linux (Fedora/RHEL/CentOS) | Configuración de la API CIE-11 (opcional) | Paso 1: Obtener credenciales | Paso 2: Guardar las credenciales | Paso 3: Verificar la configuración | Caché SQLite | Verificar la instalación | Problemas comunes | Error: "package 'ciecl' is not available" | Error de compilación en Linux | La API CIE-11 no responde | Caché corrupto | Soporte

Installation | From CRAN (stable version) | From GitHub (development version) | Dependencies by feature | System requirements | Windows | macOS | Linux (Ubuntu/Debian) | Linux (Fedora/RHEL/CentOS) | ICD-11 API Configuration (optional) | Step 1: Get credentials | Step 2: Store the credentials | Step 3: Verify the configuration | SQLite cache | Verify the installation | Troubleshooting | Error: "package 'ciecl' is not available" | Compilation error on Linux | The ICD-11 API does not respond | Corrupt cache | Support

El problema: codificar diagnósticos en Chile | Instalación | Consultas SQL directas al catálogo | Búsqueda por código conocido | Obtener descripciones para reportes | Búsqueda por texto con tolerancia a errores | Comorbilidades de Charlson y Elixhauser | Tablas formateadas para publicación | Fuente de datos oficial | Colaboración y Soporte

The problem: working with Chilean diagnostic codes in R | Installation | Direct SQL queries against the catalog | Looking up known codes | Extracting descriptions for use in tables and plots | Fuzzy search with tolerance for misspellings | Charlson and Elixhauser comorbidity indices | Formatted tables with gt | Data source | Collaboration and Support

Introduction | Step 1 — Explore the aggregate | Step 2 — Monthly IPCA for Brazil | Plot the monthly variation | Step 3 — Compare accumulation measures | Step 4 — Breakdown by product group | Step 5 — Metropolitan area comparison | Step 6 — Building a complete dataset | Large queries | Handling special values

Introduction | Core concepts | Aggregates | Variables | Classifications and categories | Geographic levels and localities | Periods and periodicities | Request limits | View modes | How ibger maps to the API | Further reading

Introduction | Load the package | Equivalence test with 2024 data | Comparing record counts and fatal counts | Using waldo for deep comparison | Working with large datasets (1979-latest) | Caching the data as Parquet | Where Parquet files live | Converting CSVs to Parquet | Reading from the cache | Keep the cache up to date

Overview | 1. Hosting and File Naming Evolution | Package Handling | 2. Nomenclature: "Accident" to "Collision" | 3. The *_historic Column Split | Longitudinal Harmonization | 4. Severity Adjustments (CRASH System Introduction) | Practical Usage | 5. Emerging Vehicles and Variable Additions | 6. Schema Generation Pipeline

Introduction | Functions | Download STATS19 data | Read STATS19 data | Crash data | Format STATS19 data | Casualties data | Vehicles data | Creating geographic crash data | Joining tables | Time series analysis | Longitudinal trends and severity adjustments | Further work | References

Live Documentation & External Posts | Recent Developments: Modernised Architecture (v4.0.0 - v4.2.0) | 1. Unified Longitudinal Schema | 2. Zero-Warning, High-Precision Parsing | 3. Native DuckDB and Parquet Support | 4. Research Tools: Costs and Vehicle Cleaning | Introduction & Package History | Short history and affiliations | What is STATS19 anyway? | The road crash data in stats19 | Note | How stats19 works | Creating geographic crash data | The big picture: road safety | Conclusion | References

Overview | Recommended Resources | 1. Reproducible Road Safety Research with R | 2. Geocomputation with R | 3. General R and Data Science | Getting Started with stats19

Introduction | Installation and Setup | Prerequisites | Launching the App | Theme Options | Step-by-Step Usage Guide | 1. File Upload | 2. Column Selection | 3. Imputation Settings | Day of Month Imputation | Month Imputation | Format Assumption | 4. Processing and Review | 5. Download Results | Advanced Features | File Format Support | Error Handling | Data Privacy | Example Workflow

What is the estimated trade matrix? | Authentication | A basic call | The World rows, and why they will ruin your totals | What is_reported actually means | Commodities | Values, dates and flows | Caveats worth carrying into your analysis | See also

General implementation | Read | Manipulate | Write | Properties implementation | $schema | profile | resources | name | id | licenses | title | description | homepage | image | version | created | keywords | contributors | sources

General implementation | Access | Read | Manipulate | Write | Properties implementation | $schema | profile | name | path | data | type | schema | dialect | title | description | format | mediatype | encoding | bytes | hash | sources | licenses | compression

What is a Data Package? | Read a Data Package | Create and edit a Data Package | Write a Data Package

General implementation | Read | Manipulate | Write | Properties implementation | $schema | header | headerRows | headerJoin | commentRows | commentChar | delimiter | lineTerminator | quoteChar | doubleQuote | escapeChar | nullSequence | skipInitialSpace

General implementation | Access | Read | Manipulate | Write | Schema properties implementation | $schema | fields | fieldsMatch | missingValues | primaryKey | uniqueKeys | foreignKeys | Field properties implementation | name | type and format | title | description | example | constraints | categories | categoriesOrdered | rdfType | Field types implementation | string | number | integer | boolean | object | array | list | datetime | date | time | year | yearmonth | duration | geopoint | geojson | any

autotest | Changes in response to autotest | goodpractice | Changes in response to goodpractice | pkgcheck | Changes in response to pkgcheck

Introduction | Fetching the data | Selecting Reference Dates | 2. Fetching and Storing Market Data | PRE Curve (DI rates) | IPCA Curve (DIC curve) | Cupom Limpo (USD - DOC curve) | Break-even Inflation: PRE vs DIC | Conclusion

Introduction | Downloading Market Data | Templates | Fetching market data | rb3.cachedir folder | Note | Accessing the data | Conclusion

Distribution Objects | Common Distribution Families | Evaluating | Enframing

User-Defined Distributions | Distribution Attributes | Example of a New Parametric Family | Network of Properties | Making your own properties | Limitations

What a Support Is | Building a Support | Continuous supports | Discrete supports | Mixed supports | The empty support | Retrieving a Support | Manipulating a Support | Giving a Distribution a Support | Why It Matters | Current Scope

Quickstart | Combining landscapes | Classifying categories | References

Legal considerations | Main package functions | oe_providers(): List providers | oe_match(): Match an input place with an OSM extract | Finding zones based on geographic inputs | Matching historical OSM extracts | oe_download(): Download OSM extracts | oe_vectortranslate(): Convert to gpkg format | layer argument | osmconf_ini and extra_tags | vectortranslate_options argument | boundary and boundary_type arguments | Other notes | oe_read(): Read-in OSM data | oe_get(): Do it all in one step | osmconf_ini | vectortranslate_options + boundary and boundary_type | query and wkt_filter arguments | Other providers | More on OpenStreetMap | Contributing to OSM

Introduction | Setup | Performing a simple search | The basics | Simple citations | Simple Taxonomic Rectification

The pkgcheck Docker container | How to get your package to pass rOpenSci's 'pkgcheck' system

autotest | Changes in response to autotest | goodpractice | Changes in response to goodpractice | pkgcheck | Changes in response to pkgcheck

The 'Makefile' | Adding new checks

More Advanced Uses | Weighted Outcome Rates | By Date | Variance Estimation

1. Introduction | 2. Elevation Data | Example usage of elevation data

1. .Rd files, example code, and the autotest workflow | 2. Internal structure of the autotest workflow | 3. autotest-ing the stats::cov function | 3.1 Listing tests without conducting them | 3.2 Conducting tests | 3.4 Controlling which tests are conducted | 4. autotest-ing your package | 4.2 Finer control over testing expectations

Integer input | Vector input | List-column conversion | Return results and documentation | Documentation of input parameters | General Procedure

Crossed Random Effects Model with Persistence Parameters | $$\begin | Multi-Trait Multi-Rater Model | $$\begin | $$\begin | References

Mixed Normal and Binomial Response | Mixed Response and Heteroscedastic Residuals | Covariate Measurement Error Model | Structural Model | Measurement Model | Disease Model | Nonlinear Predictor | Estimating the Model with galamm | Comparison to a Model with Indirect Effect Only | References

High-Level Overview | Modifying the L-BFGS-B algorithm | Optimization with the Nelder-Mead algorithm | Implementation Details | Future Improvements | References

Generalized Additive Mixed Models | Gaussian Responses | Binomial Responses | Generalized Additive Models with Factor Structures | Smooth Terms with Loadings and Factor Interactions | Mixed Gaussian and Binomial Responses | References

Data Schema | Hierarchy Terms | Synonyms | Common names | Linked Data formats | Conventions | Citation | Data Processing | Data Versioning | Archival versions

Getting started | Names and Identifiers | Synonyms | Chemical and Physical Properties | Spectral Information | Drug and Medical Information | Clinical Trials | Toxicity | Help Us Improve PubChem Access

Downloading a database | Querying the database

Who is this guide for? | The data: DEIS hospital discharges | Step 1: Normalize codes with cie_norm() | Step 2: Add the official descriptions with cie_describe() | Step 3: Find codes when you don't know the code with cie_search() | When a search returns no results | Step 4: Stratify risk with cie_comorbid() | Workflow summary | Next steps

¿Para quién es esta guía? | Los datos: egresos hospitalarios del DEIS | Paso 1: Normalizar los códigos con cie_norm() | Paso 2: Agregar las descripciones oficiales con cie_describe() | Paso 3: Encontrar códigos cuando no sabes el código con cie_search() | Cuando la búsqueda no entrega resultados | Paso 4: Estratificar riesgo con cie_comorbid() | Resumen del flujo | Para seguir aprendiendo

Supported languages | The Chilean ICD-10 dataset | Features of Chilean Spanish | Accent- and ñ-tolerant search | Chilean medical abbreviations | Multilingual ICD-11 API | Encoding and special characters | References

Revision system | Nested models | Durbin-Koopman state-space form | Estimation in reviser | Example: Euro Area GDP revisions | Other KK-family specifications

Revision decomposition | Durbin-Koopman state-space form | The reviser implementation | Measurement equation | Transition equation | Shock-loading matrix | Nested JVN specifications | Example: Euro Area GDP revisions | Other JVN specifications

1. Introduction | 1.1 Bounding boxes: the getbb() function | 2. The overpass API | 2.1. osmdata queries | 2.2 Extracting OSM data from a query | 2.3. Query example | 3. The osmdata object | 3.1. The osmdata_xml() function | 3.2. The osmdata_sf() function | 3.3. The osmdata_sp() function | 4. Recursive searching | 4.1. Example | 4.2 Relation example | 5. Additional Functionality | 6. Related Packages | 7. References

Introduction | A word of caution | Installing old packages archived on CRAN | Installing packages from GitHub | Installing local archives | Installing packages from private repositories | Installing Python packages | Installing Python packages not available via nixpkgs (impure) | Installing Julia packages | Converting from an renv.lock file | A complete example | Package installation issues

Part 1: R Packages with | Setting up the Development Environment | Initializing the Package | Workflow Basics | Using Your Package in a Nix Environment | Part 2: Python Packages with uv | Configuring pyproject.toml | Development Workflow | Local Development Loop

Introduction | Why rix and Nix? | Installing Nix | Windows prerequisites | Using the Determinate Systems installer | If you installed Nix another way | Recommended: setup_cachix() | Alternative: the cachix client | Optional: add Mischko Heming's binary cache | NixOS users | Using Mischko Heming's binary cache | A note on disk space | Installing rix | Case 1: you don't have R installed and want to install it via Nix | Case 2: you already have R installed via your OS's package manager | Configuring an IDE | Other "Nix"es

Introduction | Why rix and Nix? | Installing Nix | Recommended: setup_cachix() | Alternative: the cachix client | Installing rix | Case 1: you don't have R installed and want to install it via Nix | Case 2: you already have R installed | More macOS specificities | R support for Apple Silicon in nixpkgs | Shared libraries issue | RStudio and other development interfaces on macOS | Why do we need all these special tweaks? | Path of the Nix installation not in PATH | Configuring an IDE | Other "Nix"es

Summary | Connect | docdb_create | Identifiers | docdb_get | docdb_query | Field names | docdb_update | docdb_delete | Disconnect and shutdown

Package conventions | Convert Long to Wide Format | Convert Wide to Long Format | Handling Multiple Series with id | Extracting Releases | Visualizing Vintage Data | Analyzing Data Revisions and Releases

Model with Binomially Distributed Responses | Binomial Model with Multiple Trials | Model with Poisson Distributed Responses | References

Group-Wise Heteroscedasticity | References

Linear Mixed Model with Latent Covariates | Model Formulation | $$\begin | Model Without Interaction | Linear Interaction Between Observed and Latent Covariates | Interaction Between Latent Covariate and a Quadratic Term | Models with Additional Random Effects | Model with Smooth Terms | References

Generalized Additive Latent and Mixed Models | Response Model | Nonlinear Predictor | Structural Model | Mixed Model Representation | Maximum Marginal Likelihood Estimation | Evaluating the Marginal Likelihood | Maximizing the Marginal Likelihood | Example Models | References

Phase 1: Preparing pathogen data | Phase 2: Configuring the model | Method | Smoothing parameters | Dispersion parameters | Pathogen noise | Day of week effect | Worked example | Output | Phase 3: Fitting | Checking convergence | Phase 4: Deriving epidemiological quantities | Additional worked example: COVID-19

Get an EDI User Account | Use the "staging" Environment! | Create Tests | Requiring Authentication | Requiring Data Package Evaluation or Upload | Run Tests | Standard Mock Tests (Fast / Offline) | Live HTTP Tests Against Staging

Content | Loading DoOR | Modifying, building and updating DoOR | Importing new data with import_new_data() | Looking up InChIKeys If you do not know the InChIKeys of the odorants in | Building the complete data base with | Updating parts of the data base with | Using the heuristic approach This is the faster way to perform a merge of | Trying all permutations The more exhaustive way to update the data base is | model_response() and model_response_seq() | Merging using the heuristic with model_response() model_response() | Merging in a specific sequence with model_response_seq() | Removing a study with remove_study(){#remove_study} remove_study() will | Updating the odor information with | Helper functions | trans_id() | reset_sfr(){#reset_sfr} reset_sfr() subtracts the values of a specified | door_default_values(){#door_default_values} door_default_values() returns | get_responses(){#get_responses} get_responses() returns the response | get_normalized_responses() | count_studies(){#count_studies} count_studies() counts the number of studies | export_door_data(){#export_door_data} export_door_data() exports all or

Inside a test framework | Outside a test framework | Stubbed request based on uri only and with the default response | Stubbing requests based on method, uri and query params | Stubbing requests and set expectation of a timeout | Stubbing requests and set HTTP error expectation | httr integration | httr2 integration | Writing to disk | Many requests in a row

Installation | Basic workflow for obtaining nature media files | Obtaining metadata: the query functions | query_macaulay() | Interactive retrieval of metadata | Bypassing record limit | query_wikiaves() | query_xenocanto() | API key | Special queries | Update metadata | Combine metadata from multiple repositories | Find and remove duplicated records | Download media files | Session information

How can I get OSM objects by node/way id number? | How can I convert the segments downloaded from OSM into a street network?

Basic configuration | Customizing the language menu labels | Configure the base URL | Configure the icon of the language switch button | Translate parts in multilingual books | Using profiles for advanced configuration | Language-specific profiles | Additional profiles

Introduction | Using get_power() to fetch POWER data | Example fetching daily data for a single point | Example fetching daily data for an area | Example fetching interannual data for an area | Example fetching climatology data | Interrogating the API for available parameters | A Note on API Throttling | References

Row parallelization

Purpose | Data Sources and Processing for DAASH | Accessing DAASH via a DataSpaceConnection object | Accessing DataSpace neutralizing antibody assay data from DAASH

Workflow overview | View metadata concerning the mAb object

Browsing publications in DataSpace | Downloading Publication Data

Initiate a connection | Loading Assay Datasets | Connect to a saved group | About DataSpaceR objects

Update note from version 0.X to version 1.X | Configuration | Creating a netrc file with writeNetrc | Manually creating a netrc file | Accessing Studies and Groups | Accessing MAb and MAb Donor Data | Accessing DataSpace DAASH | Accessing Publication Data | Accessing Virus Metadata | Accessing Help Files

Purpose | Our sources | Usage caveats | Tutorial for using mapping tables | Checking the dataset for a virus name synonym | Getting standard names from virus_metadata_all | Additional notes for users processing data from the CAVIMC Neutralizing Antibody Cores | Check the harvest date | Check with the lab

Linear Mixed Models with Factor Structures | Model with Group-Wise Heteroscedasticity | Generalized Linear Mixed Models with Factor Structures | Semiparametric Model with Gaussian Responses | Semiparametric Model with Binomial Responses | Conclusion | References

Confidence intervals for extrema of a smooth function | References

One data frame | Several sheets | What is written | Everything else | Reading it back

Built-In Distribution Families | The Empirical Distribution

A chart | Naming a range | Styling | Axes | The parts of a series | The chart as a whole | A chart on a sheet of its own | Images

Overview | Setup | Helper: convert period codes to dates | 1) IPCA (7060) — Health insurance | 1.1 Discovering the right IDs | 1.2 Fetching the data | 1.3 Wide format for inspection | 1.4 Plot | 2) IPCA (7060) — Vehicle insurance | 3) PMS (8693) — Transportation and postal services | 4) PNAD Contínua (5434) — Accommodation and food services | 5) PMS (8693) — Professional and administrative services | 6) PNAD Contínua (5434) — Domestic services | 7) PIM-PF (8888) — Industrial production (selected CNAE sectors) | 8) Construction (8886) — Typical construction inputs | 9) PMC (8884 / 8757 / 8880) — Retail trade indices | 9.1 Vehicles, motorcycles, parts and accessories (8884) | 9.2 Construction materials (8757) | 9.3 Retail trade (8880) | Next steps

Formatting --- vignette | Cell content --- vignette | Worksheet features --- vignette | Printing and the sheet view --- vignette | Charts --- vignette | An image --- vignette | The workbook --- vignette | The second workbook: an embedded image | What was written

Building a format | Applying it to cells | Applying it to columns and rows | Where a format comes from | Conditional formatting | Scales, bars and icons | What is elsewhere

What a cell can hold | Formulas | Hyperlinks | Comments | Rich strings | Mixed columns | Data validation | Autofilters | Worksheet tables | Merged cells | Elsewhere

The sheet | Grouping and outlines | Protection | Silencing Excel's error indicators | The tab strip and the opening view | Printing | The workbook | Memory | Elsewhere

Non-technical intro to Nix | The Nix package manager | rix workflow | Converting from an renv.lock file | Using default.nix files

Transitioning from the old API to the new API 🔄 | The basics 📊 | The time parameter | Country Names 🌍 | Previously | Now | Searching for commodity codes 🚢📦 | Previously == Now | API search metadata 📑 | Package Data 📦 | "Polished" Column Headers 🎨

NetCDF | tidync | Data extraction | Interrogating data by dimensions | Transforms | Future improvements

Authenticate | Reserve a Data Package ID | Evaluate | Interpreting the Evaluation Report | Report Summary | Full Report | Upload | Update

Introduction to Download reports | Set up the R environment | Get the Audit Report for Downloaded Data Entities

1. Categorical data: add_osm_groups | 1.1 Hulls around groups | 1.2 Inclusive, exclusive, and bisected polygons | 1.3 Adjusting colours with adjust_colours | 1.4 Dark-on-Light Highlights | 1.5 Visualising clustering data | 1.6 The Colour Matrix: Colouring Several Regions | 1.7 Bounding areas within named highways | 2. Continuous data: add_osm_surface | 2.1 Colourbar legends for data surfaces | 2.1 Surfaces and data perimeters | 2.3 Further control of surface appearance

1. Introduction | 2. Using refsplitr | 2.1. Importing Search Results | Example | 2.2. Author address parsing and name disambiguation | 2.2.1. Accepting the results of author disambiguation without manual review | 2.2.2. Reviewing and correcting the results of disambiguation | 2.2.3. Uploading and merging the results of disambiguation | 2.3. Georeferencing author institutions | WARNINGS: | Registering with Google for an API key (NB: this is a paid service) | 2.4. Data Visualization: Productivity and Collaboration | 2.4.1. Visualization 1: Authors per country. | 2.4.2. Visualization 2: Author locations | 2.4.3. Visualization 3: Base coauthorship network | 2.4.4. Visualization 4: Mapped Coauthorships by Author Country | 2.4.5. Visualization 5: Mapped Coauthorships by Author Address | Acknowledgments | References | Appendix 1: Guide to downloading reference records from the Web of Science. | Appendix 2: Web of Science Data Field Definitions | Appendix 3: Reviewing and correcting author name assignments by authors_clean() | Appendix 4: Overview of the refsplitr author name disambiguation algorithm. | Appendix 5: Overview of the refsplitr confidence score calculation.

Publishing to project sites and other subpath-based hosting | Rendering locally vs. final website URLs | How to avoid broken language links on subpath-based sites | Option 1: Render the site using GitHub Actions (CI) | Option 2: Specify the site URL when rendering locally | CNAME | Summary

Site URL | Steps for rendering | Publishing your project | Examples

What is rcrisp? | Why is consistent delineation important? | What does it do? | Workflow | Data considerations | Example | Interpretation and next steps | References

Run time by case and step | Key findings

Installation | Lookup postcodes and outcodes | Single postcode | Multiple postcodes | Outward code lookup | Reverse geocoding | Single outcode | Generate random entries | Postcodes | Places | Postcode validation | Autocomplete postcodes | Find nearest postcodes or outcodes | Find places | Terminated postcodes

Debugging Procedure | Check that the API is online | Check log of recent requests | Check installation of system dependencies | Check system output and error logs | Manually running checks

Visualize | General raster plotting | Scaling | Binarize | Classify | Rescale | Merge

Overview | Accessing raw raster products | CHIRPS v2 | CHIRPS v3 | CHIRTS-ERA5 | CHIRTS-daily | Extracting values using terra | Accessing climate time series | Point locations | ClimateSERV | Working with terra | Datasets citation | CHIRTS | Additional resources

Installation | Installation for DEM downloads | Functions | Elevation | Slope calculation | Plotting | Helper functions | Examples | Add elevation to linestrings or points | Calculate slope | Plot elevation profile | Working with segments | By vertex (native segments of the linestring) | By fixed length (using stplanr)

Introduction | Case studies | Roads in the Netherlands | Comparison of DEM-derived and survey-grade Z values | DEM for eu: | Test it's installed: | Find location of aws cli from powershell (equivalent of which aws on Linux): | Get-Command aws | Select-Object -ExpandProperty Source | C:\Program Files\Amazon\AWSCLIV2\aws.exe | Now aws should work | Download DEM for Brussels 6 km from center | Calculate slopes with the slopes package: | Add elevation to cycle network segments | check the extents of both: | Calculate slopes for each segment | cycle_net_xyz$slope = slope_xyz(cycle_net_xyz, lonlat = TRUE, fun = slope_matrix_mean) | Convert to percentage: | Convert to factor with greater than 5 being "5+" | Drop z dimension | install cran version | Save with tmap | url of the file: | References

Determining the Structure of the Model | Estimation and Exploration | Forecasting | Estimation with Mixed Frequency | Modeling AR(1) Idiosyncratic Errors | News Decomposition | Additional Functions | Conclusion | References

Idiomas soportados | Dataset CIE-10 Chile | Características del español chileno | Búsqueda tolerante a tildes y eñes | Siglas médicas chilenas | API CIE-11 multilingüe | Encoding y caracteres especiales | Referencias

Introduction | Usage | Get available polyhedra | Retrieve a polyhedron | A demo

Downloading Climate Normals | Downloading current normals (1991-2020) | Downloading previous normals (1981-2010 and 1971-2000) | Finding stations with specific measurements | Understanding Climate Normals

General descriptions | Original names and units | 1991-2020 | 1981-2010 and 1971-2000

What are flags/codes | Flags - Weather Data | Codes | Codes - Climate Normals

Stations | Weather | Climate Normals

Packages | General usage | Data gaps | Multiple weather columns

Installation | Basic use | Forming queries | Search terms | Subject classifications | Dates and ranges of dates | Search results | Sorting results | Technical details | Metadata limitations | Limit time between search requests | Limit number of items returned | Make requests in batches | License and bugs

Performance

Why goodpractice? | Quick start | What gp() actually does | Choosing what to run | Check groups | Excluding check groups | Excluding files | Parallel preparation | Tidyverse style checks | Working with results | Custom checks

Introduction | 1. Data Quality Control | Basic Quality Checking | Why Quality Control Matters | 2. ACWR with EWMA Method | Comparing RA vs EWMA | Adding Confidence Bands | Technical Note: Bootstrap Method | 3. Cohort Reference Analysis | Multi-Athlete Setup | Calculate Reference Percentiles | Plot Individual vs Cohort | 4. Integrated Workflow Example | 5. Methodological Notes for Research | ACWR Method Selection | Confidence Bands Limitations | Cohort Reference Interpretation | 6. FAQ for Advanced Features | Session Info

Welcome to Athlytics | Prerequisites | Installation | Your Strava Data Export | Loading Your Data | Basic Loading | Understanding the Data Structure | Data Quality Checks | Filtering Your Data | Core Analyses | 1. Training Load (ACWR) | Basic ACWR Calculation | Visualizing ACWR | Descriptive ACWR Bands | Interpreting Your ACWR | Choosing Load Metrics | Important Caveats | 2. Efficiency Factor (EF) | Calculate EF | Visualizing EF Trends | Interpreting EF | 3. Cardiovascular Decoupling | Calculate Decoupling | Visualizing Decoupling | Practical Applications | 4. Personal Bests (PBs) | Calculate PBs | Visualize PB Progression | 5. Load Exposure Analysis | Calculate and Plot Exposure | Complete Workflow Example | Troubleshooting | Common Issues | "No data returned" or empty results | "Not enough data for chronic period" | "NA values in output" | Plots look strange or empty | Getting Help | Next Steps | Advanced Features | For Researchers | Citation | Session Info

Downloading HYDAT | Working with HYDAT tables

Package loading | tidyhydat package | HYDAT download | Combining validated and provisional data | Usage of HYDAT functions | Real-time functions | Meterological Service of Canada datamart - realtime_dd() | Real-time web service - realtime_ws() | Using the web service without HYDAT | Historical flows and levels via web service | Search functions | Using joins | License

Introduction | Familiarity with the tidyverse | Example 1: Basic Data Extraction and Plotting | Objective | Example 2: Distribution of Historical Data

Multimodal Bivariate Gaussian "Blobs" | Eggbox Distribution | Incorporating Data within Likelihood Functions

Bayes' Theorem and Model Evidence | Estimating Evidence with Nested Sampling | Initialization | Likelihood | Prior Distributions | Likelihood-Restricted Prior Sampler | Generating Samples | Properties of Nested Sampling Runs | Robustness | Complexity | Stopping Criteria | Uncertainty | Posterior Distributions | Conclusion

What the output of the ReLTER get_site_speciesOccurrences() is? | How to trasform external service data schemas into eLTER data-reporting template? | References

Get affiliation info of the LTER site: | Get boundaries info of the LTER site: | Get Contacts info of the LTER site: | Get list of parameters or related resources declared to the LTER site:

Overview | Why extend tidy data? | Example: gradual semantic stabilisation | Example: defining semantically rich vectors | Example: creating a dataset from a metadata-enriched data frame | Exporting to RDF | Coercing back | Summary

Purpose | Getting started | Printing and summary | Handling ambiguity | Using namespaces for coded values | Basic Usage | Subsetting and coercion | Coerce to base R types | Character coercion | Factor coercion | Numeric coercion | Logical coercion | Date coercion | POSIXct coercion | Conclusion

Introduction | A small ambiguous dataset | Creating provisional semantic assertions | Inspecting the prelabelled vector | Semantic operationalisation | A more ambiguous dataset | Incremental semantic stabilization | Semantic workspaces | From provisional semantics to formally defined semantics

Introduction | Generate Occurrence Data | Estimate Speciation Rates | Create Speciation-Area Relationship | Estimating Speciation Rates with BAMM and DR | Estimating Speciation Rates with BAMM | Estimating Speciation Rates with DR | Literature Cited

Introduction | Gathering Occurrence Data | Finding Land Mass Names and Areas | Create Species-Area Relationship | Using a Presence-Absence Matrix to Infer a SAR | Customizing Inputs to find_areas() | Custom Area Dataframes | Query a Shapefile for Areas

Download Options | User Agent String | Max Tries | Timeout Connect | Timeout | Package verbosity

Working with AAGIS Regions Spatial Data | Obtaining the AAGIS Regions Spatial Data | Obtaining AAGIS Data by Region | Merging the AAGIS Regions Spatial Data with the AAGIS Regional Data | Working With AGFD Data | Description of the Australian Farm Gridded Data | Working With the Soil Thickness Map | Soil Thickness Metadata

Abstract | Introduction and Motivation | Related Work | Design Principles | Semantic Early Binding | Attribute-Based, Not Schema-Based | Persistence Across Save/Load Cycles | Base Examples: Using the dataset Grammar | Creating Defined Vectors | Assembling a Dataset with Metadata | Provenance Tracking | Applied Example: Joining Data with Semantic Constraints | Step 1: Create a Eurostat-Compatible GDP Dataset | Step 2: Create a Dollar-based GDP Dataset | Step 3: Transform the Data and Document the Change | Full Interoperability | Export and Interoperability | Limitations and Future Work

1. Input data | 2. Setting up the network | 3. Network cleaning | 4. Visualise cleaned network

References

Introduction to | Changes to original CRU CL v. 2.0 data | Using | Creating data frames for use in R | Creating terra SpatRaster objects | Reading files downloaded outside R | CRU CL v. 2.0 reference and abstract

Purpose | Load example data | Add metadata to your dataset | Inspecting variable-level metadata | Adding dataset-level metadata | Reviewing dataset-level metadata | Joining datasets | Coercion inside dataset_df conversion | Conclusion

Outline | Making it official

Installation | Why would we do this? | Motivating example | Options within the function | dictionary | retain_unmatched | A disclaimer on handling gender responses | Contributing to this package | Acknowledgement of Country

Installation | Data sources | medRxiv data | bioRxiv data | Performing your search | Dataset description | Export records identified by your search to a .BIB file | Download PDFs for records identified by your search | Further guidance

TextReuse classes | TextReuseTextDocument | TextReuseCorpus | Tokenizers | Hash functions | Comparison functions | Parallelization

spontaneous reporting systems data mining | Analyzing FDA statin SRS data with pvEBayes | Fit the general-gamma model | Extracting results | Hyperparameter tuning | Visualization | References

Multimodal example | References

Overview | Installation | Citation | Key features and basic usage | Goodness-of-Fit Tests | Test for uniformity on the sphere | Poisson kernel-based distribution (PKBD) | Clustering Algorithm for Spherical Data | Additional Resources | References

Why this name? | Example | Multilingual! | Exercises | Recommended resources about Git

Interspecific interactions

Introduction | Setup | get_geometry | Usage | Spatial measures | Distance | Direction | Centroid | Converting from other packages | Converting from | Converting from | Converting to other packages | Converting to | Converting to | Converting to

Load awardFindR | Simple keyword search, single source | Multiple sources and keywords, specific date range

Local caching and updating for users | Data updating for developers

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

Using {weatherOz} With BOM Data | Using get_precis_forecast() | Results | Example 1: Getting a Forecast | Using get_coastal_forecast() | Example 2: Getting a Coastal Forecast | Working with BOM Image Files | Using {weatherOz} to retrieve BOM satellite imagery | Working with Satellite Imagery | Using get_available_imagery() | Example 3: Checking Available Imagery | Using get_satellite_imagery() | Example 6: Fetching Satellite Imagery and Viewing It | Using {weatherOz} With BOM Radar Imagery | Using get_available_radar() | Example 7: Getting Available Radar Imagery | Using get_radar_imagery() | Example 8: Fetching Radar Imagery | References | Appendix 1 - Output From get_precis_forecast() | Appendix 2 - Output From get_coastal_forecast()

Introduction | Example 1: Using legal land description to find the locations of archived soil samples | Scenario: | Download the data set | Search | Map | Example 2: Using coordinates to find parcels of land

Introduction | Installation | Explore the EMODnet WFS services with R | More information | References | Code

Text searches for R packages | Using search engines | Using language models | Using 'pkgmatch' | Searches based on entire packages

1. editorcheck | 2. editorcheck_contents | 3. pkgmatch | 4. mean | 5. stats_badge | 6. log | 7. clear_cache | 8. stdlogs

Introduction | A Note on API Keys | Using {weatherOz} to Fetch Daily Summary DPIRD Data | Using {weatherOz} to Fetch Patched Point SILO Data | Using {weatherOz} to Fetch 9-day forecast from METNO | Appendix 1 - Map of DPIRD Station Locations | Appendix 2 - Map of SILO Station Locations

About DPIRD Data | A Note on API Keys | Working With DPIRD Data | Getting Extreme Weather Values | Available Values for Extreme Weather | Example 1: Get All Extremes for Northam, WA | Example 2: Get Selected Extremes for Northam, WA | Getting Minute Data | Available Values for Minute Data | Example 3: Get All Minute Data for the Past 24 Hours | Example 4: Get Specific Time and Date Data for Specific Values | Getting Summary Data | Available Values for Summary Data | What You Get Back | Example 5: Get Annual Rainfall Since 2017 | Example 6: Get Monthly Rainfall Since 2017 | Example 7: Get Daily Rainfall and Wind From Beginning of 2017 to End of 2018 | Example 8: Get Hourly Rainfall and Wind From Beginning of 2022 to Current | Getting APSIM-ready Data | Example 9: Get APSIM Formatted Data for Binnu From 2022-04-01 to 2022-11-01 | Working With DPIRD Metadata | Finding Nearby Stations | Example 10: Finding Stations Nearby a Known Station | Example 11: Finding Stations Nearby a Given Longitude and Latitude | Example 12: Finding Stations in Both the DPIRD and SILO Data Sets | Example 13: Finding Stations in the Southwest Agriculture Region of Western Australia | Checking Station Uptime or Availability | Example 14: Checking Station Availability for Current Year | Example 15: Checking Station Availability for a Set Time Period | Getting Station Metadata for the DPIRD Network Stations | Example 16: Get DPIRD Station Metadata | Example 17: Get Rich DPIRD Station Metadata and Include Closed Stations

About MET Weather API Forecast Data | A Note on API Keys | Working With MET Weather API Forecast Data | Getting Hourly Forecast Data | Example 1: Get Hourly Forecast for Perth | Example 2: Understanding the Metadata | Example 3: Working With Hourly Data | Getting Daily Forecast Data | Example 4: Get 7-Day Daily Forecast | Example 5: Compare Daily Aggregations | Combining Forecast with Historical Data | Example 6: Combining MET Weather API Forecast with SILO Historical Data | Example 7: Combining MET Weather API Forecast with DPIRD Historical Data | Practical Use Case: Agricultural Planning | Example 8: Heat Stress Event Planning | Advanced Usage: Helper Functions | Example 9: Custom Aggregation with metno_resample_data_table() | Example 10: Understanding Weather Symbols | Appendix: Field Descriptions | Hourly Forecast Fields | Daily Forecast Fields | Weather Symbol Codes | Metadata Structure | References

About SILO Data | A Note on API Keys | Working With SILO Data | Available Values | Value Information | Data Codes | Getting Data Drill Interpolated Data | Example 1: Get Data Drill Data Using Defaults | Example 2: Get Data Drill Temperature for a Specific Date Range | Example 3: Get Data Drill APSIM-ready Data | Getting Patched Point Data | Example 4: Get Data Drill Data Using Defaults | Working With SILO Metadata | Finding Nearby Stations | Example 5: Finding Stations Nearby a Known Station | Example 6: Finding Stations Nearby a Given Longitude and Latitude | Example 7: Finding Stations in Both the DPIRD and SILO Data Sets | Example 8: Finding Stations in a Bounding Box for Melbourne | Example 9: Finding the Station Nearest the Centroid of a Bounding Box | Getting Station Metadata for the SILO Network Stations | Example 10: Get SILO Station Metadata | Using Metadata to Get Weather Data for a Whole State | References

Optimal Properties of Revisions | Initial Estimates as Truth Measured with Noise | The Challenge of Defining a Final Release | Iterative Approach for Identifying ( e ) | Importance of an Efficient Release | Example: Identifying an Efficient Release in GDP Data with reviser | References

Summary Statistics | Revision Size | 1. Mean Revision ("Bias (mean)") | 2. Mean Absolute Revision ("MAR") | 3. Minimum and Maximum Revisions ("Minimum", "Maximum") | 4. Percentiles of Revisions ("10Q", "Median", "90Q") | 5. Standard Deviation of Revisions ("Std. Dev.") | 6. Noise-to-Signal Ratio ("Noise/Signal") | Correlation of Revisions | 1. Correlation Between Revisions and Initial Releases ("Correlation") | 2. 1st order Autocorrelation of Revisions ("Autocorrelation (1st)") | 3. Autocorrelation of Revisions up to 1 year ("Autocorrelation up to 1yr (Ljung-Box p-value)") | Sign Switches | 1. Fraction of sign changes | 2. Fraction of sign changes in the growth rate | Hypothesis Tests | News and Noise Tests for Data Revisions | 1. The Noise Test | 2. The News Test | Test of Seasonality in Revisions | Friedman Test | Theil’s U Statistics | 1. Theil’s U1 Statistic ("Theil's U1") | 2. Theil’s U2 Statistic ("Theil's U2") | References

Introduction: Why do data revisions occur? | Incorporation of newly available or updated data | Base data revisions | Benchmark revisions (methodological changes) | Minor changes in estimation methods | Technical adjustments & error corrections | Economic events & shocks | Technological advances in data collection | Some mathematical notation | Extracting revisions with reviser | The get_revisions() function | Example usage | Example 1: Revisions Using an Interval | Example 2: Revisions Relative to a Fixed Reference Date | Example 3: Revisions to the Nth Release | Example 4: How does the growth rate of GDP change over time? | References

What does the package do? | Using an R package as input

Required Libraries | Exploring the Metadata | gutenberg_metadata | gutenberg_works() | gutenberg_subjects | gutenberg_authors | Downloading Books | Single Book | Multiple Books | Adding Metadata | Downloading from gutenberg_works() | What's Next? | Additional Resources

Required Libraries | Download the Book | Structural Analysis: Adding Chapters | Tokenization | Word Frequency Analysis | Sentiment Analysis | Overall Sentiment | By Chapter | Sentiment Progression | TF-IDF: Finding Unique Chapter Words

Choropleth maps | Using a data frame | Using atomic vectors | Numerical values | Mapping of point data | Conclusion

Usage | Spatial and temporal grouping | Installation | Package dependencies | Functions | group_times | DT | datetime format | threshold recommendations | Limitations of threshold | Columns returned by group_times | Warnings and messages | group_pts | DT | threshold | group_lines | crs | sortBy | group_polys | DT and area | hrType and hrParams | edge_dist | edge_nn | randomizations | Package design | Don't I need to reassign to save the output? | Why does a function print the result, but columns aren't added to my DT? | Summary information | Number of individuals | Temporal range | Spatial extent | spatsoc outputs

1. Command name's syntax | 2. Using base functions | 3. Spell Checker: look carefully at warnings! | 4. subc.lang() function

What is lingtypology? | 1. Installation | 2. Citing lingtyplogy

What's happening inside of gp()? | Writing custom checks | Checks without corresponding preparation steps | Including a preparation step

Installing magick | Build from source | Image IO | Read and write | Converting formats | Preview | Transformations | Cut and edit | Filters and effects | Kernel convolution | Text annotation | Combining with pipes | Image Vectors | Layers | Combining | Pages | Animation | Drawing and Graphics | Graphics device | Drawing device | Animated Graphics | Raster Images | Base R rasters | The grid package | OCR text extraction

Installing babelquarto | Starting a multilingual book | Starting a multilingual website | Previewing your multilingual project | Publishing your project | Next steps

Introduction | RSelenium with Local Fully-fledged Browsers | Firefox | Chrome | Internet Explorer | Safari | Opera | RSelenium with Local Headless Browsers | phantomjs | HtmlUnit | RSelenium with Remote Browsers and External Sites | Setting up Sauce Labs | Basic Example | Testing Multiple OS/Browsers | RSelenium with Remote Browsers and Local Sites | Sauce Connect | Setting up Sauce Connect

Introduction | Why Docker? | Preparing Docker on Windows | Installing Docker | Preparing Docker on Linux | Ubuntu 16.04 | Running without sudo | Using Selenium Docker Images | What's in an Image? | Pulling an Image | Windows 10 - Docker Toolbox | Start Your Servers! | Windows 10 Running an Image | Ubuntu 16.04 Running an Image | RSelenium and Running Containers | Utilising the Firefox Standalone Container in Windows 10 | Utilising the Firefox Standalone Container in Ubuntu 16.04 | Debugging Using VNC | Remote Logging/Debugging with Windows | Remote Logging/Debugging with Linux

Introduction | Setting up Git | Setting up your environment | Using your environment | Using GitHub Actions | For a project using | For a project that doesn't use | How to update or reproduce your project

Introduction | Build Logs: The Key to Your Pipeline's History | Cleaning Up Artifacts with rxp_gc() | 1. Targeted Deletion: Cleaning Up Old Builds | 2. Full Garbage Collection | Conclusion

Large Pipelines Become Unwieldy | Basic Usage | Visualising Sub-Pipelines | Switching Between Colour Modes | How It Works Internally | Best Practices | Conclusion

Setup | Map objects | Changesets | Notes | GPX data | Users

Installation on Linux systems | Installation on macOS systems

1. The package skeleton | 2. Enabling srr roclets for a package | 3. roxygen2 tags | 3.1 Format of srr documentation | 3.2 Locations of srrstats documentation | 3.3 Documenting standards for documentation | 3.4 @srrstatsNA tags for non-applicable standards | 4. The srr workflow

Introduction | Starting with the basics (simple but not entirely reproducible) | 100% reproducible literate programming | Conclusion

Introduction | Setting up | Where to find packages to fix | Case 1: packages that need dependencies to build | Case 2: packages that need a home, X, or simple patching | Case 3: packages that require their attributes to be overridden | Case 4: packages that need a dependency that must be overridden | Case 5: darwin-specific fixes | Case 6: an R packages requires software not in nixpkgs (or outdated in nixpkgs) | Study! Study! Study! Study! | Broken packages | Reviewing PRs

Introduction | Converting an historical renv project | Starting a new project | Caveats | Package versions are not exactly the same between the renv.lock and default.nix files | Don’t use the same folder for your Nix and {renv} projects | Mind the R version

Why bring {rix} into your package workflow? | Declaring your package environment | Committing the environment to your repository | Keeping the snapshot fresh | Using {rix} in GitHub Actions | Summary

Introduction | The {lookup} package | Caveats | Fixed commits | Duplicated packages | Authenticating to GitHub | Consider using dependencies that made it to CRAN | Custom Git hosting platforms | Conclusion

Introduction | Adding tools to an environment | Installing TexLive packages | Installing IDEs

Introduction | Using GitHub Actions to build the environment | Using your cache

Introduction | R packages available through Nix | Using bleeding and frozen edge package for your environments | Making your own cache

Introduction | Our recommendation | Recommended setup on macOS | Recommended setup on Windows | Recommended setup on Linux | RStudio | RStudio on macOS | RStudio on Linux or Windows | Positron | Configuring native Positron to interact with Nix shells | Letting Nix manage Positron on any operating system | VS Code/Codium | Configuring native VS Code/Codium to interact with Nix shells | Letting Nix manage VS Code/Codium | Emacs | Configuring native Emacs to interact with Nix shells | Letting Nix manage Emacs | Making a launcher to start your editor

Introduction | A generic Dockerfile | Dockerizing a Shiny application | NixOS

Project-specific Nix environments | Which r_ver should you choose? | Running old projects with | Running programs from an environment | Running single functions in a subshell | Nix environments are not completely isolated from your system

Getting data into the pipeline | Declaring build steps | Generating the pipeline | Defining a Reproducible Shell for Execution | Optional steps before building the pipeline | Graphical representation of the pipeline's DAG | Tracing the lineage of derivations | Building and inspecting outputs | No-op builds for individual derivations | Caveats | Conclusion

Analysing the mtcars dataset using R and Python | Building a Quarto or R Markdown document

Installing the required software | Bootstrapping a project

Introduction | Prerequisites | Workflows | Package options | Main wrapper function | Run the full OPTRAM model procedure with a single function call | Show trapezoid plot | Step by step | The same procedure as the wrapper function, but in explicit steps | Soil Moisture Estimate | Use trapezoid coefficients, VI, and STR rasters to derive soil moisture grid | Soil moisture plot

The Problem: Silent Failures | What is {chronicler}? | The Challenge with Nix Builds | The Solution: Automatic Chronicle Checking | Three-State Status System | Complete Example | Automatic Warnings | Best Practices | When to Use Chronicler | Further Reading

Introduction | Connecting to a Selenium Server | What is a Selenium Server? | Do I Need to Run a Selenium Server? | How Do I Get the Selenium Server Standalone Binary? | How Do I Run the Selenium Server? | Docker | rsDriver | Java Binary | How Do I Connect to a Running Server? | Navigating Using RSelenium | Basic Navigation | Accessing Elements in the DOM | Search by Name | Search by ID | Highlight an Element | Search by Class | Search Using CSS Selectors | Search Using XPath | Sending Events to Elements | Sending Text to Elements | Sending Key Presses to Elements | Sending Mouse Events to Elements | Injecting JavaScript | Injecting JavaScript Synchronously | Injecting JavaScript Asynchronously | Frames and Windows | Frames in Selenium | Windows in Selenium | Appendix

Overview | Analytical Validation | Test Cases | Running Analytical Validation | Literature Validation | Test Lakes | Validation Approach | Running Literature Validation | Edge Case Validation | Key Validations | Running Edge Case Validation | Methodology Alignment | Shore Protection Manual (USACE, 1984) | Mason et al. (2018) | Effective Fetch Calculation | Unit Tests | R CMD Check Status | Summary | References

Overview | Use | State plane projections

Running pipelines on GitHub Actions

Introduction | The First Response to a Failed Build: rxp_inspect() | Investigating Structural Issues with rxp_trace() | A Proactive Strategy: Isolating Derivations with noop_build | Historical Debugging: Going Back in Time | Conclusion

Introduction | Custom Encoding and Decoding in R | Cross-Language Data Transfer: R ↔ Python | Summary

Importing a single local file | Importing a single file from the internet | Importing many files from a directory | Importing files with dependencies (e.g., Shapefiles) | Conclusion

Definitions | Derivations | Dependencies of derivations | The Nix store and hermetic builds | Summary and conclusion

Generating waveshaders data using Julia and plotting it using R | Setting up the environment | Creating the pipeline | Helper functions | Data transfer between R and Julia | Conclusion

Overview | What rixpress is | What rixpress is not | Primary audience | In-scope features (current) | Out-of-scope features (not planned) | Roadmap | Near-term (next minor releases) | Maybe later | Not planned | How to propose new features | Related projects and inspiration | Links

Setting up the Environment | Setting up the pipeline | Compiling and Sampling the Model | Custom Serialisation | Summary

Context | The Mangal project | Data structure | Authentication | How to use rmangal | Overall approach | Search functions | Search and list available datasets | Finding a specific network | Search for a specific taxon | Get networks associated with a search_* object | Integrated workflow with rmangal | Batch analysis | Geolocate Mangal networks with sf | Taxonomic analysis | Network analysis with igraph | Network manipulation and visualization with tidygraph and ggraph | Creating a list references for a set of networks | References

Overview | Advanced API usage | Pipelines, workflows, and jobs | Restarting and retrying | Artifacts and logs | Environment variables | Deep integration with | Initialize CircleCI with | Customize stages | Authentication and permissions | Making vignettes robust on CI/CRAN | See also

Introduction | Authentication with GitHub | First steps | Deployment | Deployment Keys | Starting a Build | See also

Creating the Circle CI YAML configuration file | Enabling deployment from builds | Understanding the YAML file | The first build

General configuration | Setting up | Access methods | Additional configuration | More information | SAS On Demand for Academics configuration | Registration | Java installation | Local Windows installation configuration

Introduction | An example of a reproducible analytical pipeline using Nix

Testing code in evolving software dependency environments with confidence | Two operational modes of computations in environments: 'System-to-Nix' and 'Nix-to-Nix' | Case study 1: Evolution of base R | Setting up the (R) software environment with Nix | Defining and interactively testing custom R code with function(s) | Run functioned up code and investigate results produced in pure Nix Rsoftware environments | Syntax option for specifying function in expr argument of with_nix() | Comparing as.vector.data.frame() for both R versions 4.1.3 and 4.2.0 from Nixpkgs | Case study 2: Breaking changes in {stringr} 1.5.0 | Case study 3: Using a subshell to get hard to install dependencies

Combining data from OToL and other sources. | Get Open Tree IDs to match your data. | Find a tree with your taxa | Published trees | A part of the synthesis tree | Connect your data to the tips of your tree | Find external data associated with studies, trees and taxa from Open Tree | Get external data from a study | Find a OTT taxon in another taxonomic database | What next

Demonstration of a basic workflow | Step 1: Matching taxonomy to the ott_id | Step 2: Getting the tree corresponding to our taxa | FAQ | How to change the ott ids assigned to my taxa? | How do I know that the taxa I'm asking for is the correct one? | How do I get the tree for a particular taxonomic group? | How do I find trees from studies focused on my favourite taxa? | The tree returned by the API has duplicated tip labels, how can I work around it? | How do I get the higher taxonomy for a given taxa? | Why are OTT IDs discovered with rotl missing from an induced subtree? | Removing the taxa missing from the synthetic tree | Using the full taxonomic names

Phylogenetic Comparative Methods | A phylogenetic meta-analysis | Gather the data | Find the species in OTT | Get a tree | Perform the meta-analysis | What other comparative methods can I use in R?

NetCDF | Caching | ERDDAP™ servers | Install | Search | Information | griddap (gridded) data | tabledap (tabular) data

Quick start | Background | The eBird Basic Dataset | auk vs. rebird | Data access | Example data | AWK | A note on versions | Presence data | The auk_ebd object | Defining filters | Executing filters | Reading | Saving the AWK command | Group checklists | Taxonomic rollup | Zero-filled, presence-absence data | Filtering | Reading and zero-filling | Acknowledgements | References

Setting up databases | What we test | How to setup test databases locally | ☠️ What not to run ☠️ | Some of the tricky bits that dittodb uses | Recording | Using a mocked database

Introduction | The skim() function | Skimming data frames | Skimming vectors | Skimming matrices | Skimming without modification | Reshaping the results from skim() | Rendering the results of skim() | Customizing print options | Modifying skim() | Extending skimr | Solutions to common rendering problems

Introduction | 1. Loading Data | The Parsing Workflow | Loading Sample Data | 2. Analysis: The Drug Landscape | 3. Analysis: Approval Status | 4. Analysis: Molecular Mechanisms | 5. Next Steps: Integrated Pharmacovigilance

The "Hub and Spoke" Architecture | Pipeline-Ready Pharmacovigilance | New Tools for Reproducibility | A Scientific Case Study | Availability

Introduction | Prerequisites | 1. Loading and Inspecting Data | 1.1 Load DrugBank (The Hub) | 1.2 Load OnSIDES (Single-Drug Labels) | 1.3 Load TWOSIDES (Drug Interactions) | 2. Merging the Databases (Chaining) | 3. Single Drug Analysis: Leuprolide (DB00007) | 3.1 Extract Baseline Adverse Events (OnSIDES) | 4. Single Drug Analysis: Calcitriol (DB00136) | 5. Polypharmacy Analysis: Drug-Drug Interactions | 5.1 Visualization of Interaction Risks | 6. Comparative Overview | 7. Conclusion

Data preparation | Temporal grouping | Spatial grouping | Edge-list generation

Generate edge-lists | 1. Load packages and prepare data | 2. a) edge_dist | 2. b) edge_nn | Dyads | 3. dyad_id | Fusion events | 4. fusion_id

Generate gambit of the group data | 1. Load packages and prepare data | 2. a) group_pts | 2. b) group_lines | 2. c) group_polys | Build observed network | 3. get_gbi | 4. asnipe::get_network | Data stream randomization | 5. a) type = 'step' | 5. b) type = 'daily' | 5. c) type = 'trajectory' | Build random network | 6. a) type = 'step' | 6. b) type = 'daily' | 6. c) type = 'trajectory' | Network metrics | 7. Calculate observed network metrics | 8. Calculate random network metrics | 9. Compare observed and random metrics

Updating the eBird taxonomy | Adding new filters | 1. Update auk_ebd() | 2. Create filter function | 3. Update auk_filter() | 4. Unit tests | 5. Update vignette and README | 6. Build, test, check, and push to GitHub | CRAN submission

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

Import data | Validation | Investigate errors | Duplicate names | Invalid column names | Fixing the data | Summary

SMAP data products | Preparing to access SMAP data | Finding data | Downloading data | Exploring data | Extracting data | Comparing surface and soil moisture

Variables and constants definitions | Print functions | Capturing progress of model simulations | Access to intermediate results | Handling NetLogo runtime errors | Self-written NetLogo output | Temporary files management | random-seed and repetitions management | Runtime and measurements | NetLogo extensions | Parallelisation and the future concept | Parallelisation on local machine | Parallelisation on remote HPC cluster | Using the future framework | Using the clusterMQ framework

nlrx | Publication | Prerequirements | NetLogo | Java | Installation | Get started | Step by step application example | Step 1: Create a nl object: | Step 2: Attach an experiment | Step 3: Attach a simulation design | Step 4: Run simulations | Step 5: Attach results to nl and run analysis | Complete code example

arkdb | Package rationale | Installation | Tutorial | Creating an archive of an existing database | Unarchive | Pluggable text formats | A note on compression | Distributing data

Introduction | Using get_GSOD() | Find Stations in or near Toowoomba, Queensland, Australia | Download a Single Station and Year Using get_GSOD() | Using nearest_stations() to Download Multiple Stations at Once | Plot Maximum and Minimum Temperature Values | Using reformat_GSOD() | Reformat a List of Local Files | Reformat all Local Files Found in Directory | Using get_updates() | Notes | WMO Resolution 40. NOAA Policy | Appendices | Appendix 1: GSODR Final Data Format, Contents and Units | Appendix 2: Map of Current GSOD Station Locations | References

Installation | Quickstart | prism data and parameters | Downloading data | Download 30-year normal data | Download daily, monthly, and annual data | Interact with the archive and prism data | Raster plots | Single grid cell plot | leaflet map

Distributional Representations | Drawing a random sample | Properties of Distributions

rocker_ReLTER | About | Install a different branch of ReLTER package | Build the image | Usage | Preserve your work (using a Docker volume) | Precompiled image on Docker Hub

An brief introduction to the cosinor model | Introduction | cglmm() | A basic overview of cglmm() | Understanding the output | More advanced cglmm() model specification | Using summary() and testing for differences between estimates | Using predict() | Plotting cglmm objects | Large datasets | Assessing residual diagnostics of cglmm regression models using DHARMa | Differential rhythmicity | References

cglmm() | Using cglmm() | Specifying a single-component model with no grouping variable | Specifying a single-component model with a grouping variable and a shared MESOR | Specifying a single-component model with a grouping variable and an intercept (MESOR) | Specifying more complicated models using the amp_acro() function | Dispersion and zero-inflation model specification | Using summary(cglmm)

Visualizing cglmm models | Polar plots

Next steps

Determine required input datasets | Raster Data | Determine minimum data requirements | Transmission Distance | Area | Additional considerations | Temporal resolution | land cover classifiers | Example land-cover datasets | North America | Canada | Global | Reclassifying to Hazardous fuel layer | Structures/Values Datasets | Alberta | USA | References

Make and model data

Where to start | IndicatorID | AreaTypeID | Extracting the data | Plotting outputs | Other useful functions

Example

1. Main findings | 2. Pedestrian traffic and reported casualties | 3. How far do pedestrians travel? | 4. Casualty rates per mile travelled | 5. Sex and age comparisons | 6. Which vehicles are involved in collisions with pedestrians? | 7. Time of day of collisions | 8. What type of road? | 9. Vehicle movement on the road

Overview | Create a Single Object | System Metadata | Access Policy | Create a Collection of Objects | Relationships Between DataObjects | Linking a metadata file with one or more data files using cito:documents | Asserting data provenance relationships between objects | Inserting other (arbitrary) relationships | Describing The Contents of a DataPackage | Saving DataPackage Contents to a File

Setup (Windows users) | Geographic map tiles | Context | Basic example | Projected maps | Missing CRS | Coloring tiles | RGB and RGBA rasters | Non-geographic map tiles | Additional arguments | Serving map tiles | Leaflet examples using remotely hosted tiles | Local preview | Details

The models used here | Combining metrics within models

Spatial data in R | 1. Determine scale | Landscape | Local | Understanding transmission distances | Long-range embers | Short-range embers and radiant heat | 2. Determine scope | Example: Compute exposure | Example workflow | Example: Assess directional exposure | Example: Conduct an exposure assessment for a local area | Advanced Example: Validate the exposure metric in a study area | 3. Determine required data | References

Best practice | URL validity

Step 1 - Start with one or more data files | Step 2 - Fill in Templates | Metadata Files | Step 3 - Save metadata in JSON | Step 4 - Create a dataspice website

Overview | Adding mantis content | Extra requirements for interactive reports

Overview | Built-in rules | Custom rules | Applying different rules to different time series | Walkthrough

API Information | A quick example | Convenience functions | Detailed usage | Step 1: Authentication | Add it to your .Renviron file | Put it in a file | Add it interactively | Step 2: Building Queries | Querying a range of values | Querying multiple values | Query Limits: Total records per query | Query Limits: Total queries per second | Step 3: Running Queries | Step 4. Putting it all together | Lists of parameters and dealing with large queries | Iterating to reduce individual query size | Iterating over lists of parameters | Under the hood

Why this occurs | Confirm issue | Fix for only the current R session | Fix for all future R sessions

Resources | Further reading and resources

Introduction | Testing for Spatial Autocorrelation using Moran's I | References

Introduction | Package description | Data examples | Assessing model assumptions for fitting a mixture cure model | Penalized mixture cure models | Fitting penalized mixture cure models using GMIFS | Fitting penalized mixture cure models using E-M algorithm | Cross-validation | Other Package Functions | Comparison to other mixture cure modeling packages | Conclusions | References

Computational grids | Types of computational grids and their generation | par_pad_grid(): standard interface | par_pad_balanced(): focusing on getting the balanced clusters | Random points in NC | Visualize computational grids | Generate regular grid computational regions | Split the points by two 1D quantiles | Merge the grids based on the number of points | Different values in merge_max | par_make_balanced() | Common grid systems

Introduction | chopin workflow | Example data | Generating random points in North Carolina | Creating grids | Extracting values from raster | Hierarchical processing | Multiraster processing | User-defined functions | Caveats | Why parallelization is slower than the ordinary function run? | Notes on data restrictions

What rredlist is not: | Installation | Authentication | Overview of available features | Example usage | Loading the package | Search for assessments for a particular species | Search for assessments that recommend particular conservation actions | Get a list of all conservation actions | Return assessments with a particular conservation action | Advanced usage | High level vs. low level interfaces | High level interface | Low level interface | Usage best practice | Citing the IUCN Red List API | Rate Limiting | API Versioning

Introduction | Head-to-head benchmarks | 1. Get species count | 2. Lookup individual assessment | 3. Taxonomic lookup with defaults | 4. Taxonomic lookup with query (one page of results) | 5. Taxonomic lookup with query (~10 pages of results) | 6. Taxonomic lookup with query (~40 pages of results) | 7. Taxonomic lookup with query (~900 pages of results) | And the winner is... | Query breakdown | Conclusion

Introduction | How have mollusc assessments increased through time? | What is the conservation status of Australian reptiles? | What habitats do conifers and cycads occur in? | Some recent published uses of rredlist

Returned structure | Demo for Google Cloud Speech-to-Text API | Word transcripts | Custom configurations | Asynchronous calls

Returned structure | Talk Languages | Support for SSML | Effect Profiles | Browser Speech player | Using with Shiny

Language Translation | HTML support | Language Detection | Translation API limits

Demo for Entity Analysis

Google Natural Language API | Google Cloud Translation API | Google Cloud Speech API | Installation | Usage | Authentication

Key Features | NUTS Codes | Spatial interpolation in a nutshell | Usage | Workflow | Identifying NUTS version and level | Converting data between NUTS versions | Converting multiple variables simultaneously | Converting grouped data | Converting data between NUTS levels | Inconsistent versions and levels | Non-identified NUTS codes | Missing NUTS codes | Multiple NUTS levels within groups | Multiple NUTS versions within groups | Spatial interpolation in detail | Changes in administrative boundaries | Spatial interpolation and conversion tables | Conversion methods | Conversion of absolute values between versions | Conversion of relative values between versions | Conversion of absolute values between NUTS levels | Conversion of relative values between NUTS levels | Citation

Introduction | Materials and Methods | Summary | References

From tidy data to RDF triples | Creating a minimal semantically defined dataset | RDF triples enable interoperability | Clean up | Scale up

Purpose | Example Dataset Definitions For RDF | obs:1 | obs:2 | obs:3 | Machine-readable form

CRediTas | Installation | Create a template | Read a template | Generate the CRediT author statement | Do not drop authors without contributions

Purpose | What is bibrecord | Creating a bibrecord | Printing a bibrecord | Compatibility with existing infrastructure | Future extensions | Summary

Getting started | Weighted models | Fixed effects models | Interaction terms and varying slopes | AsIs and polynomial terms | Plotting the results | Adding other parameters to the model | Acknowledgements

Installation | Data Hub List | Usage | XenaData data.frame | Workflow | Browse datasets | More usages | QA | How to resume file from breakpoint | Citation | Acknowledgments

Our function mean_delays() | RMariaDB | RPostgres | RSQLite | Recording fixtures | with_mock_db() | Getting setup to use dittodb | Things to be careful about | When to call dbConnect() | Query size | Advanced uses | Specify a new path | Redacting | You, too, can write a fixture!

Multiple models | Generated quantities | More information

Understanding TRUD Subscriptions | Examples | Check your current subscriptions | Browse all available items | Get metadata for a specific item | Download an item | Working with specific releases

Introduction | Supported Languages and Formats | Fully Supported Languages | Language Detection Features | Language-Specific Examples | English | French (Français) | German (Deutsch) | Spanish (Español) | Portuguese | Russian (Русский) | Indonesian (Bahasa Indonesia) | Handling Mixed Languages | Format Detection | Roman Numeral Support (Experimental) | Best Practices | Adding New Languages

Introduction | Installation | Available Datasets | Example Workflow | More Examples | Example: Return the first 100 NFIP claims for Florida that happened between 2010 and 2020. | Example: Get data on all Hazard Mitigation Assistance Projects associated with flood mitigation in Florida. | Example: Determine how much money was awarded by FEMA for rental assistance following Hurricane Irma. | Clean one of the data sets with a nested structure

1. Base map | 2. Set features | 3. Set pop-up boxes | 4. Set labels | 5. Set coordinates | 6. Set colors | 7. Set shapes | 8. Set control box | 9. Set an additional set of features using strokes | 10. Set width and an opacity feature | 11. Customizing legends | 12. Set scale bar | 13. Set layouts | 14. Add a minimap to a map | 15. Add minicharts instead of points | 16. Add a rectangle to a map | 17. Add a density contourplot to a map | 18. Add isoglosses | 19. Add lines

Data format | Specifying the data columns | Specifying the timepoint unit | Generating a report | Interactive plots | Static plots | Heatmap | Multipanel

Data availability | Package information | Install and load comtradr | Authentication 🔐 | Storing the API key | Making API calls | API search metadata | More on the lookup functions | API rate limits | Package Data | Visualize | Handling large amounts of Parameters | commodity_code | flow_direction | reporter and partner | mode_of_transport, partner_2, and customs_code | Example Usage

Introduction | Key Features | Core Functions | When to Use datefixR | Core Workflow | Date standardization | Advanced Topics | Localization | Date and Month Imputation | Converting Numeric Dates | Roman Numeral Months Experimental | Error & Edge-Case Handling | Common Error Examples | FAQ | Understanding Year Parsing Logic | Two-Digit vs Four-Digit Years | Automatic YMD Detection Logic | 1. Year-First Detection | 2. Month Name Detection | 3. Numeric Component Defaults | 4. Handling Ambiguous Cases | 5. Format Detection Examples | Overriding Automatic Detection | Explicit Format Specification | Data Frame Processing | Citation

Overview of the EML hierarchy | Our example: (re)-creating hf205.xml | Our strategy | Attribute Metadata | Data file format | Assembling the dataTable | Coverage metadata | Creating methods | Creating parties | Creating a keywordSet

Introduction | Skimming objects that are not coercible to data frames | Defining sfl's for a package | Adding new methods | Conclusion

Scope | Architecture | Naming conventions

Step 1: import un-normalised data | Step 2: find the matching white reference | Step 3: normalise the reflectance data

What are C3D files? | Why use the c3dr package for R? | Install the c3dr package | Read C3D files with c3dr | Data structure of C3D objects in R | Header | Parameters | Point data | Analog data | Force platform data | Work with C3D point data | Work with C3D analog data | Further analysis of imported data | Write C3D files with c3dr

How vcr works | Where vcr comes from | Principles | An easy to use interface hides complexity | Hooks into HTTP clients | Internal classes | Configuration | Cassette class | How HTTP requests are handled | Serializers

Introduction | Obtaining a Taxon Key | Downloading Occurrence Records Using occ_download() | Downloading Occurrence Records Using occ_search() | Restricting Occurrence Records Using Geometry | Curating GBIF Occurrence Records

Use Case #1: Functional Annotation of Genes Sharing a Common Evolutionary History | Step 1 | Enrichment Analyses

Discover curl options | Other ways to use curl besides R | general info | using curl options in other packages | timeout | verbose | headers | authenticate | cookies | progress | proxies | user agent

Testing with vcr | Cassette files | Request matching | What happens if the API changes? | Other uses: examples and vignettes | Conclusion

Data with subject-level differences | A single component model with random effects | REML

What is concstats? | How can you install it? | Why did I build it? | How can it be used?

Installation | Data | Group wrapper overview | Examples | concstats_mstruct | concstats_inequ | concstats_comp | concstats_concstats | Visualization

Import spectral data: lr_get_spec() | Import spectral metadata: lr_get_metadata() | Convert spectral data to csv: lr_convert_tocsv()

Comparison of outputs | Example of analysis

Install | HttpClient - the main interface | Do some HTTP requests | write to disk | stream data

Background | Accessing and Importing Project FeederWatch Data | Downloading and Importing Raw Data | Filtering and Zerofilling Project FeederWatch Data | Filtering your data | Zerofilling your data | Helper functions | Acknowledgements | References

Installation | Package functionality | loadData() | cleanData() | data(locations) | plotting host-helminth networks

Logging basics | Second and subsequent requests | A match failure | Multiple requests | Common problems and their solutions | Re-used cassette name | Non-deterministic requests

Introduction | Examples | Prepare data.frame of pixel values | Show Linear trapezoid plot | Show Exponential fitted trapezoid plot | Show Polynomial fitted trapezoid plot

Installation | Usage | Citations

The XOR operator | Stream ciphers | Symmetric encryption | Public-key encryption and Diffie-Hellman

Introduction: The NCBI, entrez and rentrez. | Getting started with the rentrez | Searching databases: entrez_search() | Building search terms | Using the Filter field | Advanced counting | Finding cross-references : entrez_link(): | My god, it's full of links | Narrowing our focus | External links | Using more than one ID | Getting summary data: entrez_summary() | The summary record | Dealing with many records | Fetching full records: entrez_fetch() | Fetch DNA sequences in fasta format | Fetch a parsed XML document | Using NCBI's Web History features | Get a web_history object from entrez_search or entrez_link() | Use a web_history object | Rate-limiting and API Keys | Slowing rentrez down when you hit the rate-limit | What next ?

1. OpenStreetMap Data Structure | 2. Simple Features Data Structure | 3. How osmdata translates OSM into Simple Features | 3.1. OSM Nodes | 3.2. OSM Ways | 3.3 OSM Relations | 3.3(a) Multipolygon Relations | 3.3(b) Multilinestring Relations | 4. GDAL Translation of OSM into Simple Features | 4.1. OSM Nodes | 4.2. OSM Ways | 4.3 OSM Relations | 4.3(a) Multipolygon Relations | 4.3(b) Multilinestring Relations | 5 Examples | 5.1 Routing | 5.1(a) Routing with sf/GDAL | 5.1(b) Routing with osmdata

fellingdater: Estimate, report and combine felling dates of historical tree-ring series | Installation | Basic example | Main functions | sw_interval | fd_report | sw_combine | sw_example0 | sw_example2 | sw_example3 | sw_example4 | sw_example5 | sw_sum | Helper functions | sw_data_overview | sw_data_info | sw_model and sw_model_plot() | read_fh and get_header | cor_table

Introduction | Calculating slopes | Measures of route hilliness | Segments in a route: Cumulative slope | References

Extract the OSM network from geofabrik | Clean the road network | Filter the unconnected segments | Break the segments on vertices | Get slope values for each segment | Result: | Other examples

About rangr | Basic workflow | Installing the package | Input maps | Initialise | Simulation | Visualisation | Virtual Ecologist | More advanced workflow | Abundances at the first time step | Carrying capacity | Initialise (using update function)

Install | Parse | Latitudes: | Longitudes: | Both longitudes and latitudes at the same time: | Both longitudes and latitudes in the same string: | Parse into degree, min, sec parts: | Get degree, minutes, or seconds separately: | Add or subtract degrees, minutes, or seconds: | Get hemisphere from lat/lon coords:

Use case: working with spatial R packages

Count formats | Conversion functions | compute_abundances() | compute_concentrations() | compute_frequencies()

Setup | Spatial visualization | Creating a world map | Mapping FORCIS data | Temporal visualization | Plot data by year of sampling | Plot data by month of sampling | Plot data by season | Vertical visualization | Plot data by depth of sampling

Installation | Download FORCIS database | Import FORCIS data | Select a FORCIS taxonomy | Use case 1: Exploration | Use case 2: Specific question | Find the species name | Species filter | Temporal filter | Spatial filter | Distribution map | To go further | References

Setup | Selecting columns | Select a taxonomy | Select required columns | Filtering rows | Filter by month of data collection | Filter by year of data collection | Filter by bounding box | Filter by ocean | Filter by spatial polygon | Filter by species | Reshaping | Convert to long format

Query parameters | Request and response headers | URL, headers, and body

Default Grid Format | Code | Issue Authors | Issue Contributors | Section Organisation | List Format | Text Format

Introduction | Fetching historical data | Querying the data | Historical nominal interest rates | Historical real interest rates | Implied inflation | Forward rates | Conclusion

Shiny app | Paginated responses | Examples | Get build 37 coordinates for a variant | Convert gene symbol to versioned GENCODE ID | Convert rsID to GTEx variant ID | For a gene of interest, which tissues have significant cis-eQTLs? | Get data for non-eQTL variants

Site identifier | Profile identifier | Cast identifier | Sample identifier | Subsample identifier

Using the latest version | Using a specific version

How to run targets examples from vignettes | tar_terra_rast(): targets with terra rasters | Raster metadata | tar_terra_vect(): targets with terra vectors | tar_terra_sprc(): targets with terra raster collections | tar_terra_sds(): targets with terra raster datasets

Session management | Executing SAS code | Within quarto | RStudio | Positron | Data conversion | File management

Available data | Physical vector data | Cultural vector data | Searching for countries and continents | Finding countries | Continents

Introduction | Retrieving available indices | Fetching historical index data | Visualizing index performance | Analyzing index composition | Finding stocks in multiple indices | Finding which indices contain a specific stock | Analyzing index weights with the theoretical portfolio | Top constituents by weight | Comparing index concentration | Sector analysis with the current portfolio | Sector composition analysis | Creating helper functions for index analysis | Analyzing index performance metrics | Calculating summary statistics | Conclusion

Introduction | Fetching historical data | Downloading yearly data | Downloading daily data | Accessing the datasets | Filtering by asset type | Equities | ETFs (Exchange Traded Funds) | REITs (FIIs - Fundos Imobiliários) | BDRs (Brazilian Depositary Receipts) | Other asset classes | Analyzing equity data | Top traded equities by volume | ETF market share analysis | Analyzing distribution IDs for price continuity | Working with BDRs | Conclusion

Using dynamite to generate panel data | Model with categorical responses | Model with time-varying effects

Functions

Data | Examining datasets: loupe() | Additional arguments from waldo::compare() | Extracting unexpected changes: catch() | Dropping unexpected changes: release() | Checking for continuity: timeline() | Grouping distinct continuous sequences: timeline_group() | Using butterfly in a data processing pipeline | A note on controlling verbosity | Rationale | Unexpected changes in models | Unexpected changes in data acquisition | Variable measurement frequencies | In summary

load required packages | Maps in the package. | Downloading other Natural Earth vectors with ne_download(). | Tables of vector layers available via ne_download(type=[layer_name], scale=)

Load required packages | Country maps with labels in other languages

Load required packages | Country types : countries, map_units and sovereignty | Country scales : small, medium and large | States, admin level 1, select by country or geounit

Introduction | Understanding the Magnitude of Revisions | The Dynamics of Revisions and What They Reveal | The Uses of Studying Revisions in Time Series Data | Conclusion

What is an extent? | Helper functions to create multiple extents of a raster | tile_n() | tile_grid() | tile_blocksize() | How to run targets examples from vignettes | Example targets pipeline

Data analysis | 0. Preprocessing | 1. Add surprisal values to the dataset | 2. Analyze the dataset with a Bayesian hierarchical model | Conclusion | References

References

Module not found error in Rstudio | HTTPSConnectionPool error

References

Installation | What is a quarter section? | How do legal land descriptions for quarter section work in Manitoba? | Other land division types | Usage | Download the data set from DataMB | Search using lat/long coordinates | Search using legal land descriptions | Map quarter sections

Introduction | Frames and Channels in TIFF files | The Peculiarity of ImageJ TIFF files | Reading ImageJ TIFF files | The original tiff package | The ijtiff package | Note | Advice for all ImageJ users

Introduction to TIFF Tags | Supported TIFF Tags | Reading Tags from TIFF Files | Understanding Tag Values | Tags in Multi-Frame TIFF Files | Conclusion

Reading TIFF files | Reading only certain frames | More examples | Writing TIFF files | Reading text images | Writing text images

Preparing for mapping | Understanding mapping outputs | Compare mapping | Using a custom metadata input (recommended) | Using a custom domain list input (recommended) | Using a custom lookup table input (advanced) | Copying across tables

Installation | Load the census data | Load and modify the equation table | Subset the equation table | Add new equations | Estimate the aboveground biomass | How AGB is estimated | Attribute a weight to each equation in the equation table for each taxon/location combination | Resample equations | Calibrate a new equation for each taxon/location combination | Visualize the recalibration of equations

Example | 1. create review project | review files | index.Rmd | pkgreview.md | README.md | clone of package source code | 2. Perform your review: | 3. Submit your review: | 4. Publish your report by pushing to GitHub *

index.Rmd | editor.md | request.Rmd

Enable GitHub Pages

Import and processing with spiro() | Options for data processing | Use breath-by-breath data! | Supported metabolic carts | Exercise protocols | Protocol guessing | Protocol setting | Modify body mass | Work with external heart rate data

Load the data | Stepwise summary with spiro_summary() | Maximal parameter values with spiro_max() | Plotting the data

Introduction | Prepare target datasets | Hardware specification | Download and preprocess | Download | Read | TerraClimate | Preprocessing | PRISM dataset | Grid parallelization | Scaled up examples | Larger buffer sizes | Larger number of features | Finely resolved vector | Extract | Finely resolved raster | See also

Assumptions | Basic workflow | Minimize errors | Raster workflow: stacked vs file-based parallelization | Raster-Vector overlay | Customization | More tips to save time and memory | Save computing costs

Temporal plotting of radiocarbon ages | Ridgeplots of density distributions | Calcurve plot | Spatial mapping of radiocarbon dates | Interactive map view | Static map plot

Combining schemas

What does this package do for me? | How do I use it? | Practical tips | Mapbox API tokens | Map generation | Map rectification | Types of map markings and types of spatial objects | Bonus Feature: Polygon Aggregation | How it works

The Wireless Indoor Localization Data | Note | References

Note | References

Wasn't there a package to read Marine Regions already? mregions? | Marine Regions Gazetteer | Search by free text | Search by MRGID | Search by longitude and latitude | Search by place type | Search by source document | Search geometries: Geocoding | Search by relations | RESTful services | Linked Open Data | Marine Regions Data Products | WMS viewer | Caching | OGC and CQL filtering

What is metadata? | Sources of health metadata | Health Data Research Innovation Gateway and the connected Metadata Catalogue | British Heart Foundation Data Science Centre (BHF DSC) Dashboard | Office for National Statistics (ONS) Secure Research Service (SRS) Metadata Catalogue | Do you know of others?

Topics | Perform Meta-Genome Retrieval | Getting Started | Retrieve Genomic Sequences | Example NCBI RefSeq: | Example NCBI Genbank: | Example ENSEMBL | Retrieval from NCBI RefSeq | Restarting a corrupted download | Un-zipping downloaded files | Retrieval from NCBI Genbank | Retrieval from ENSEMBL | Retrieve groups or subgroups of species | Example retrieval of all Gammaproteobacteria genomes from NCBI RefSeq: | Example retrieval of all Adenoviridae genomes from NCBI RefSeq: | Meta retrieval of genome assembly quality information | Metagenome project retrieval from NCBI Genbank | Retrieve Protein Sequences | Retrieval from NCBI RefSeq: | Retrieval from NCBI Genbank: | Retrieval from ENSEMBL: | Retrieve CDS Sequences | Retrieve GFF files | Retrieve GTF files | Retrieve RNA sequences | Retrieve Repeat Masker Sequences | Retrieve Individual Genomes for all Species in the Tree of Life | Genome Retrieval | Proteome Retrieval

Biological Sequence Retrieval | Topics | Getting Started with Sequence Retrieval | Example NCBI RefSeq (?is.genome.available): | Using the NCBI Taxonomy ID instead of the scientific name to screen for organism availability | Using the accession ID instead of the scientific name or taxid to screen for organism availability | A small negative example | Example NCBI Genbank (?is.genome.available): | Using is.genome.available() with ENSEMBL | Example ENSEMBL (?is.genome.available): | Example UniProt (?is.genome.available): | Listing the total number of available genomes | Retrieving kingdom, group and subgroup information | Analogous computations can be performed for groups and subgroups | Downloading Biological Sequences and Annotations | Genome Retrieval | Example NCBI RefSeq: | Use taxid id for genome retrieval | Use assembly_accession id for genome retrieval | Example NCBI Genbank: | Use taxonomy id for genome retrieval | Example ENSEMBL: | GenomeSet Retrieval | Proteome Retrieval | Example Retrieval Uniprot: | ProteomeSet Retrieval | CDS Retrieval | CDSSet Retrieval | RNA Retrieval | RNASet Retrieval | Retrieve the annotation file of a particular genome | Removing corrupt lines from downloaded GFF files | GFFSet Retrieval | Repeat Masker Retrieval | Genome Assembly Stats Retrieval | Collection Retrieval

Introduction | Getting started | Example data | Loading the data | Using gigs to convert growth measurements to z-scores | Single-step approach | Multi-step approach | Using gigs to obtaining growth outcomes from measurements | Size-for-GA | Small vulnerable newborns | References

Default prior distributions in dynamite | Regression coefficients | Intercept | Standard deviation parameters | Correlation matrices | Parameters related to latent factors | Family-specific parameters | Defining priors of dynamite models

Introduction | The dynamic multivariate panel model | The dynamite package | Bayesian inference of seat belt usage and traffic fatalities | Causal effects in a multivariate model | Model construction | Defining response variables | Lagged responses and covariates | Time-varying and time-invariant effects | Group-level random effects | Latent factors | Multivariate responses | Number of trials and offset variables | Auxiliary response variables | Visualizing the model structure | Model fitting and posterior inference | User-defined priors | Extracting model fit information | Visualizing the posterior distributions | Missing data and multiple imputation | Prediction | Aggregated predictions and memory conservation | Summary | References | Details on latent factors

Using modified Stan code with dynamite | Using non-MCMC algorithms in dynamite

Generating a two-component model | Generating a three-component model | Generating models with n-components

Vehicle level variables in the STATS19 datasets | Relationships between vehicle type and crash severity

Introduction | Other options for faking data | What this package does, and does not do | Examples | Fake business transactional data | Steps for creating realistic looking business transactional data | Protected health information | Plumber api | Conclusion

Contributing | Package API | Install | high level function | locale support | generate a dataset | Data types, localized | person name | phone number | job | Data types, universal | currency | credit cards | Missing data | character strings | numeric data | logicals | Messy data

Purpose | Set up a new data package. | What's in the package skeleton structure? | A note about the YAML config file. | Where do I put my raw datasets? | An API to read raw data sets from within an R or Rmd processing script. | YAML header metadata for R files and Rmd files. | Build the data package. | Documenting your data set changes in NEWS. | The build. | A note about the package source directory after building. | Update the auto-generated documentation. | Why not just use R CMD build? | Installing and using the new data package. | Accessing vignettes, data sets, and data set documentation. | Using the DataVersion. | Migrating old data packages. | Create a datapackager.yml file. | Reading data sets from Rmd files. | The old way. | The new way. | Partial builds. | Multi-script pipelines. | File system artifacts. | Passing data objects to subsequent scripts. | Next steps. | Place your package under source control. | Additional Details. | Fingerprints of stored data objects. | DATADIGEST | DESCRIPTION

Installation and docs | Enable a repository | Get data from the APIs | All packages in an universe | Info on all packages in an universe | Info on a single package in an universe | Search within a single universe | Search among all universes

About Unpaywall | Basic usage | What's returned? | Any API restrictions? | Keeping track of crawling | References

Introduction | Basic usage | Specifying the extraction method | Extracting areas | Interactive table extraction | Use case: COVID-19 treatments in Italy | Miscellaneous functionality

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

Description | Robots.txt files | Fast food usage for the uninterested | Example Usage | object oriented style | functional style

Introduction | Package data | Available tables | Country codes | Commodity codes | Inflation data | Package functions | Country code | Commodity code | Trade data | Bilateral trade at commodity level (Year - Reporter - Partner - Commodity Code) | Bilateral trade at aggregated level (Year - Reporter - Partner) | Reporter trade at commodity level (Year - Reporter - Commodity Code) | Reporter trade at aggregated level (Year - Reporter) | Commodity trade at aggregated level (Year - Commodity Code) | Inflation adjustment

Spell Checking | Check Individual Words | Check Documents | Check PDF files | Check Manual Pages | Morphological Analysis | Stemming Words | Analyzing Words | Tokenizing | Summarizing Text | Hunspell Dictionaries | Setting a Language | Installing Dictionaries in RStudio | Dictionaries on Linux | Custom Dictionaries

Introduction to iheatmapr package | Usage documentation

Objective | Installation | Example | Random points in NC | Grid partition of NC | Targets workflow

It's all about the triples | Subject URIs | Predicate URIs | Datatype URIs | Triples in rdflib | Dialing back the ugly | Prefixes for URIs | URI vs URL | Blank nodes | Triple notation: nquads rdfxml, turtle, and nquads | JSON-LD | From tables to Graphs | SPARQL: A Graph Query Language | SPARQL: Getting back to Tidy Tables!

Table of contents | Overview | Installing | Usage overview | Configuration | Users: level of usage and expected knowledge | Defining data sources | Prepackaged data source definitions | Defining your own data sources | Example 1: a single data file | Example 2: multiple files via recursive download | Example 3: an Earthdata source | Example 4: an Oceandata source | Nuances | Recursive download | Other tips and tricks, including resolving recursive download issues | Choosing a data directory | Synchronizing to object storage | Post-processing | Decompressing files | Deleting unwanted files | Modifying data sources | Authentication | Reducing download sizes | Parallelized sync | Data provenance and reproducible research | Developer notes | Writing new data source handlers | Data source summary

1. dplyr and pipe |> | 2. leaflet, leaflet.extras, mapview, mapedit | 3. Combining maps in a grid and facetisation with mapview | 4. Get data from OpenStreetMap with overpass | 5. Create your own atlas with rmarkdown | 6. Create .kml file using sp and rgdal

Lectura de datos y metadatos | Análisis de la precipitación | Anomalía porcentual | Deciles | Índice de intensidad de sequía de Palmer | Índice Estandarizado de Precipitación | Análisis de extremos | Dia promedio de inicio y fin | Persistencia | Mapas

Algunas ideas sobre Programación | Problemas, algoritmos y lenguajes de programación | El diseño algorítmico | Codificación | Errores de programación | Procesador, ambiente y acciones | R y RStudio | Organización de RStudio | Uso de la consola | Diseño del sistema R

Some Ideas about Programming | Problems, algorithms, and programming languages | Algorithmic Design | Coding | Programming Errors | Processor, Environment, and Actions | R and RStudio | Organization of RStudio | Using the Console | R System Design

A simple HTTP request function | More customized function | Asynchronous requests

simple async | varied request async

Importing crul | HTTP request function | Failing with fauxpas | Retrying requests | Mocking with webmockr | Testing with vcr | What else?

HttpRequest | More Async

Update and Append | Writing multiple sheets simultaneously | Flat ODS files (.xml or .fods) | Misc. | readODS 2.0.0 | ods_sheets | get_num_sheets_in_ods and getNrOfSheetsInODS | read.ods

Introduction | Acquiring Sentinel-2 Imagery using CDSE | Step 1: Install Required Packages | Step 2: Create CDSE Account and OAuth Client | Step 3: Creating OAuth Client: | Step 4: Saving Credentials: | Step 5: Acquire Sentinel-2 Imagery | Acquiring Sentinel-2 Imagery using openEO | Step 2: Connect to openEO Platform | Registration Process: | Login Process: | Step 3: Acquire Sentinel-2 Imagery | Conclusion

First things first | Converting your data | Workdays and work-free days variables | fd(): Number of work-free days per week | so(): Local time of sleep onset | gu(): Local time of getting out of bed | sdu(): Sleep duration | tbt(): Total time in bed | msl(): Local time of mid-sleep | Combining workdays and work-free days variables | sd_week(): Average weekly sleep duration | sloss_week(): Weekly sleep loss | le_week(): Average weekly light exposure | msf_sc(): Chronotype or sleep-corrected local time of mid-sleep on work-free days | sjl_rel(): Relative social jetlag | sjl(): Absolute social jetlag | Success! | Utilities

Working around missing sections

The two intervals problem | Methods for computing $SJL$ | method = "difference" | method = "shorter" (default method) | method = "longer" | So, what method should I use?

Comparación con los resultados oficiales. | Comparación de canastas | Canasta Básica Alimentaria | Canasta Básica Total | Inversa del Coeficiente de Engel

The basics of caching 💽 | Careful with outdated data! ⚠️

Introduction | Background | Overview of melt | Usage | Case study | Conclusion

Why use bulk files? 📦📦📦 | Accounting for HS Code changes 🏗️👷 | Getting started with bulk files 🏃 | Formatting bulk files 📝

The limits | The example - Imports of the EU | 1. What are the commodity codes? | 2. Which are the countries? | Getting the data | EU imports from all countries | Data cleaning | Caveat on trade "dependencies"

Introduction | Prerequisites | Installation | Downloading OTP and the demonstration data | Creating a folder to store OTP and its data | Downloading OTP | Downloading Example Data | Building an OTP Graph | Launch OTP and load the graph | Connecting to the OTP from R | Getting a route from the OTP | Stopping the OTP | Building your own OTP Graph | Multiple Routers | Getting Data for OTP | Road Map Data | Public Transport Timetables | Elevation Data

Introduction | Why would I want to use OpenTripPlanner for R? | What is this package for, and what is a package anyway? | What are R and RStudio? | What is an R package? | Help with R | What do I need to get started? | Hardware | Software | The correct version of Java for OTP | Next Steps

Introduction | Major Changes from v1.5 | Correct version of Java | Checking your Java version | Building a graph and starting up OTP 2.2

Required Software | Creation of networks | Networks in Cytoscape

Configuring and controlling DataPackageR builds. | The datapackager.yml file. | YAML config file properties. | Editing the YAML config file. | YAML config API calls. | construct_yml_config | Example | yml_find | yml_list_objects | yml_list_files | yml_disable_compile | yml_enable_compile | yml_add_files | yml_add_objects | yml_remove_files | yml_remove_objects | yml_write

Note | Introduction | Package structure and functionality | Core functions and classes | Accessing and processing transport data | Creating geographic desire lines | Allocating flows to the transport network | Modelling travel catchment areas | Modelling and visualisation | Modelling mode choice | Models of travel behaviour | Visualisation | Future directions of travel | References

Turning right | Karel's superpowers | Filling the hole | Documentation of subalgorithms

Who is Karel? | Installation | Examples

Search | Getting access | Profiles | Credentials management | Doing a search | Simple search | Finding data records | Filtering search results | Previewing search results | Advanced search | The rtoi | Search with an rtoi | Displaying results

Community detection in very large datasets

Exploring | {nycflights13} test database | Adding {nycflights13} data to a database

Introduction | .TextGrid, .eaf, .exb formats | .flextext format

Introduction | Install the package | Philosophy of the phonfieldwork package | The phonfieldwork package in use | Make a list of your stimuli | Create a presentation based on a list of stimuli | Rename collected data | Merge all data together | Annotate your data | Extracting your data | Visualizing your data | Read linguistic files into R | Create a viewer | References

Downloading data | Finding spectral indices | Calculating indices | Stacking rasters

Background | Example project | Interval-based model validation pipeline | Multiple models | Simulation-based calibration | References

Dendrochronological data (or tree-ring data) | hol_rom: Roman tree-ring site chronologies from Hollstein | RING_Visser_2021: Roman tree-ring site chronologies | References

Usage | Visualization in Cytoscape | Citation | References

Data format | Specification of data field types | Generating a data quality report | Contents of the report | Source data | Aggregated data | Individual data fields | Creating a stratified report | Advanced usage details

Girar a la derecha | Los superpoderes de Karel | Llenar el agujero | Documentación de los subalgoritmos

Sequential Control Structures | Conditional Control Structures | Simple Conditional Structures | Double Conditional Structures | Multiple or Nested Conditional Structures | Iterative Control Structures | Iterative Control Structures with a Fixed Number of Iterations | Iterative Control Structures with an Indeterminate Number of Iterations

Estructuras de control secuenciales | Estructuras de control condicionales | Estructuras condicionales simples | Estructuras condicionales dobles | Estructuras condicionales múltiples o anidadas | Estructuras de control iterativas | Estructuras de control iterativas con un número fijo de iteraciones | Estructuras de control iterativas con un número indeterminado de iteraciones

Fixing the street again | Create other functions that make Karel even more powerful | Collect columns of beepers | The Maze | More Problems | Problem 1: Collecting the newspaper | Problem 2: Going around the house | Problem 3: Creating a line of beepers | Problem 4: Jumping over sticks | Problem 5: Cleaning the World | Problem 6: Placing beepers in the vertices of the diamond | Problem 7: Doubling the number of beepers | Problem 8: Repairing columns

Otra vez a reparar la calle | Crear otras funciones que hagan de Karel aún más poderosa | Recoger columnas de cosos | El laberinto | Más problemas | Problema 1: juntar el periódico | Problema 2: dar vueltas a la casa | Problema 3: crear línea de cosos | Problema 4: saltar los palotes | Problema 5: limpiar el mundo | Problema 6: poner cosos en los vértices del rombo | Problema 7: duplicar el número de cosos | Problema 8: reparar columnas

¿Quién es Karel? | Instalación | Ejemplos

Installation | Usage | eBird Taxonomy | Sightings at location determined by latitude/longitude | Recent observations at a region | Recent observations at hotspots | Nearest observations of a species | Recent notable sightings | Historic Observations | Information on a given region or hotspot | Checklist Feed | View Checklist | Hotspots in a region or nearby coordinates

Using OSMChange format and osm_diff_upload_changeset() | Using osmapi patterns

Target network preprocessing

Fitting an EL object

Empirical likelihood computation | Increasing the number of observations | Increasing the number of parameters\

Basic tree and metadata queries | Replicating results | Meta-Analysis

Installation | Loading the package | Search by term | dplyr downstream | Spatial search | Global full text search | Lots of data | Email dump of data | Messages

Out-of-bag data | Out-of-bag predictions and error | Monitoring out-of-bag error | User-supplied out-of-bag evaluation functions | Specific instructions on user-supplied functions | Notes

Partial dependence (PD) | Classification | Regression | Survival | One variable, moving horizon | Multiple variables, jointly | Find interactions using PD | Individual conditional expectations (ICE) | Visualizing ICE curves | Limitations of PD | References

Simulating rhythmic data | Understanding the inputs for a simple model | Simulating grouped cosinor data | Simulating multi-component cosinor data

Go faster | Don't specify a control | Use n_thread | Do less | Show progress | Don't wait. Estimate!

Why piggyback? | Authentication | Download Files | Download URLs | Reading data for R usage | Reading files | Reading from URLs | Uploading data | Write R object directly to release | Deleting Files | Listing Files | Caching | Valid file names | A Note on GitHub Releases vs Data Archiving | What will GitHub think of this?

piggyback vs the alternatives | Git LFS | Amazon S3 | Scientific repositories with private storage | datastorr | Sharding on GitHub

Data Too Big To Fit In Memory | nflverse play by play | DuckDB | duckdbfs

Introduction | fasta_filename | inference_model and mcmc | site_model | clock_model | tree_prior | mrca_prior

Introduction | Create a 'BEAST2' input file | Display (part of) the 'BEAST2' input file | Run MCMC | Display (part of) the 'BEAST2' output files | Parse output

Introduction | Setup | Experiment | Do the run | References

Background | Oblique RFs for survival, classification, and regression | Interpretation | Variable importance | Partial dependence (PD) | Individual conditional expectations (ICE) | What about the original ORSF? | aorsf and other machine learning software | Learning more

Introduction | Circle CI | GitHub Actions | Updating the deployment status | {pkgdown} deployment | Deploying to docs/ (default branch) or gh-pages branch | Default branch deployment | Orphaning the gh-pages branch | Committing single files

Basic idea | Examples

Extract Text from Images | Language Data | Preprocessing with Magick | Read from PDF files | Tesseract Control Parameters | Whitelist / Blacklist characters

birdsize for the Breeding Bird Survey | Obtaining data | Cleaning data | Simulating individual-level measurements | References

Overview | Species-level parameters | Population (one species) simulations | Using species identity | Using a known mean and standard deviation | Using a known mean, but no standard deviation | Community (multiple populations) simulations | Simulations using AOU | Simulation given species' names | Simulation given mean size measurements | Community-wide summary statistics | Biomass total by species | Community total biomass

References

Species known to birdsize | AOU lookup | Scientific name lookup | Unknown species or AOUs | Species not known to birdsize | Manually supplying species parameters | Order of operations | References

Download | Review | Image acquisition | Collaborative rtois

Customize | Review | Mosaic | Index calculation | Definition | Calculation | Cloud removal

Processing | Filling missing data | Theoretical background | Hands-on demonstration | Data | Interpolation of Mean Anomalies

Introduction | Loading Packages | Benchmarking with Fixed Sparsity | Benchmarking with Varying Sparsity | Conclusions | Session Info

Introduction | Loading Packages | Benchmarking with Fixed Sparsity | Benchmarking with Varying Sparsity | Conclusions | Session Info

Introduction | Unique Features | Installation | Basic Usage | Writing Matrix Market files | With Dense Vectors | With Dense Matrices | With Sparse Matrices | Reading Matrix Market files | Addenum | Alternatives | Reading Back In Python

API key | Register a key with EIA | Key storage and retrieval | API requests | Memoization | Anti-DOS measures

API key | Register a key with EIA | Key storage and retrieval | EIA directory | Note on output format | EIA metadata | EIA data | Data structure | Data values | EIA facets | Getting facet values | Using facet values | Frequency-, time-subsetting, and sorting

Analyst Features | Loading OTP with the Analyst | Creating a Pointset | Creating a Surface | Producing a travel time matrix | Producing an isochrone from a surface

Background | Identifying erroneous coordinates with CoordinateCleaner | Install CoordinateCleaner | Set up libraries and data | Visualize the data on a map | Use CoordinateCleaner to automatically flag problematic records | Option A) Using the clean_coordinates wrapper function | Option B) Using the magrittr pipe (%>%) | Temporal outliers | Improving data quality using GBIF meta-data | Improving data quality using external information | Flag records based on fixed longitude and latitude | Flag records based on species natural ranges | Identifying problematic data sets | Identify dataset with ddmm to dd.dd conversion error | Test for rasterized sampling | References

Communication | Definitions | Example | R6 | Inheritance | Locale specific inheritance | Adding new providers or locales | Adding a new locale step by step | Code work: | Documentation work: | Github work: | Adding a new provider, step by step | Guidelines for providers and locales | Where should I add logic or data? | Prior work and related

Installation | Connecting to an SSH server | Authentication | Execute Script or Command | Capturing output | Using sudo | Transfer Files via SCP | Hosting a Tunnel | Disconnecting

Yardstick Extensions | Multi-scale model assessment | Area of Applicability

Introduction | Installation | On Windows | On Linux systems | On Mac OS | Running the tool in Interactive Mode: the MODIStsp GUI | Non-Interactive Execution from within R | Specifying the processing parameters in the function call | Launching MODIStsp using a saved "Options file" | Looping over different Options files | Specifying the processing parameters using a previously saved options file and overwriting some parameters | Looping over different spatial extents | Scheduled Processing | On Linux | Outputs Format and Naming Conventions | Single-band outputs | Virtual multi-band outputs | Accessing the processed time series from R | Extracting Time Series Data on Areas of Interest | Problems and Issues | Citation | References

Setup | Using calculated indicators from STATcompiler | Analyse DHS microdata | Identify surveys that include anemia testing | Download datasets | Identify survey variables | Extract survey data | Data tabulations | Calculate anemia prevalence | Regression analysis: relationship between education and anemia

Overview

Overview | 0. Installation | 1. Access standard indicator data via the API | 2. Identify surveys relevant for further analysis | 3. Download survey datasets | 4. Load datasets and associated metadata into R. | 5. Extract variables and combining datasets for pooled multi-survey analyses. | Summary and further thoughts

Overview | 0. Installation | 1. Access standard indicator data via the API | 2. Identify surveys relevant for further analysis | 3. Download survey datasets | 4. Load datasets and associated metadata into R. | 5. Extract variables and combining datasets for pooled multi-survey analyses. | Summary and further thoughts

Search | Access