Package index • sntutils

Data import and export

Read and write a wide range of tabular and spatial formats, with an SNT-aware wrapper that hashes, fingerprints and writes sidecar metadata.

read(): Read in Data and Shapefiles from Various File Formats
write(): Save Data and Shapefiles to Various File Formats
read_snt_data(): Read the most-recently modified saved dataset
write_snt_data(): Write an object to standardized filenames in one or more formats

Data cleaning and standardisation

Type inference, date parsing, name and column harmonisation, and dictionary building.

auto_parse_types(): Infer column types using readr, then layer factor detection
autoparse_dates(): Parse Dates in a Data Frame
available_date_formats: Available date formats for autoparse_dates function
standardize_names(): Standardize name-like strings with optional steps
clean_filenames(): Clean Filenames
prep_geonames(): Interactive Admin Name Cleaning and Matching
build_dictionary(): build a compact data dictionary
snt_data_dict(): Load and flatten the SNT variable tree
check_snt_var(): Detect and display the structural components of an SNT variable name

Spatial validation and mapping

Validate and harmonise admin geometries and facility coordinates, crosswalk between shapefile vintages, fuzzy-match names, and render maps.

validate_process_spatial(): Spatial Vector Validation and Cleaning
validate_process_coordinates(): Coordinate Validation and Cleaning
fuzzy_match_facilities(): Facility name matching across datasets (DHIS2 vs MFL)
calculate_match_stats(): Calculate and report geo-naming match statistics
crosswalk_shapefiles_sf(): Create an area-weighted crosswalk between old and new admin polygons
dhis2_map(): Crosswalk DHIS2 dataset using dictionary
plot_admin_map_distinct(): Color admin groups so touching neighbors never share a color
facetted_map_bins(): Plot Faceted Choropleth Maps from sf Data with Discrete Bins
facetted_map_gradient(): Plot Faceted Choropleth Maps from sf Data with Continuous Gradient
get_palette(): Get a color palette
list_palettes(): List available palette names
download_shapefile(): Download WHO Administrative Boundaries with Partial Update

Routine surveillance - reporting rates

Quantify and visualise how completely health facilities are reporting, by time and admin unit.

calculate_reporting_metrics(): Calculate reporting/missing rate and proportion of reporting facilities
calculate_reporting_metrics_dates(): Calculate reporting metrics based on facility date ranges
reporting_rate_plot(): Plot Missing data or Reporting Rate over time
reporting_rate_map(): Plot reporting rate maps over time
facility_reporting_plot(): Plot monthly reporting activity by health facility
classify_facility_activity(): Classify health facility activity status by reporting behaviour
get_active_facilities(): Get active facilities from a dataset
validate_routine_hf_data(): Orchestrates a suite of validation checks on routine HF data. It standardizes column resolution, selects indicators, runs missing/duplicate/future/logic/ outlier checks, compiles a summary, and optionally translates and saves.

Routine surveillance - data quality

Cascade consistency checks, outlier detection and correction, imputation helpers.

consistency_check(): Consistency Check Function
consistency_map(): Consistency violation map
detect_outliers(): Detect outliers with guardrails and consensus
outlier_plot(): Create Outlier Detection Plots
correct_outliers(): Correct outliers using temporal neighbors
impute_outlier_ma(): Impute outliers using moving average from adjacent time points
impute_higher_admin(): Impute higher administrative level using a lookup table
compare_methods_plot(): Compare facility activity classification methods (multilingual)
fallback_diff(): Fallback Absolute Difference Between Two Vectors (type-preserving)
fallback_row_sum(): Smart row-wise sum with missing data handling and type preservation
safe_sum(): Row-safe sum for grouped aggregation

Climate and environmental downloads

Pull rainfall, temperature, land cover and other environmental rasters from public archives.

download_chirps(): Download CHIRPS Raster Data from UCSB Archive
check_chirps_available(): List Available CHIRPS Raster Files for a Dataset
chirps_options(): List Available Monthly CHIRPS Dataset Options
download_era5(): Download ERA5 Data from Copernicus CDS
check_era5_available(): List Available ERA5 Files for a Dataset
era5_options(): List Available ERA5 Dataset Options
get_era5_metadata(): Get Metadata from ERA5 NetCDF File
print_era5_metadata(): Print ERA5 Metadata Summary
read_era5(): Read and Process ERA5 NetCDF Files to Tidy Data
migrate_era5_filenames(): Migrate ERA5 Filenames to New Format
download_modis(): Download MODIS Data from NASA
modis_options(): List Available MODIS Products
download_process_nasapower(): Download and process NASA POWER daily climate data
process_ihme_u5m_raster(): Extract Under-5 Mortality Values from IHME Raster Stack

Population downloads and processing

WorldPop downloads (totals, age-bands, urbanicity), extrapolation, and SNT-shaped population outputs.

download_worldpop(): Download Population Rasters from WorldPop
download_worldpop_age_band(): Download WorldPop Population Raster Data for Specific Age Bands
download_worldpop_urbanicity(): Download WorldPop DUG Urbanicity Rasters
get_worldpop_paths(): Download WorldPop Rasters and Get Paths
extrapolate_pop(): Extrapolate Population Estimates for Target Years
snt_process_population(): Summarise population by available admin levels and build a dictionary

Raster batch processing

Aggregate raster stacks to admin units (zonal stats), with optional population weighting and time-varying boundaries.

process_raster_collection(): Process multiple raster files from a directory
process_raster_with_boundaries(): Process raster data with administrative boundaries
process_rasters_by_year(): Process Year-Indexed Rasters Against Time-Varying Admin Boundaries
process_weighted_raster_collection(): Process Weighted Raster Data in Batch
process_weighted_raster_stacks(): Process Weighted Raster Stacks
normalize_raster_by_polygon(): Normalize Raster Values by Polygon Regions
tidy_malaria_raster_names(): Normalize malariaAtlas Raster Filenames

DHS indicators

Bulk download and lookup of DHS/MIS indicators.

download_dhs_indicators(): Query DHS API Directly via URL Parameters
check_dhs_indicators(): Check DHS Indicator List from API
get_dhs_data(): Load DHS Parquet datasets using DuckDB

EMOD demography and weather inputs

Build demographic and climate inputs for EMOD-style simulations from SNT data.

build_emod_demog(): Build EMOD Demographics JSON for a Single Node
build_emod_demog_from_wpp(): Build EMOD Demographic Inputs from UN WPP 2024 Data
write_emod_demog_by_adm2(): Write EMOD Demographics JSON Files by ADM2
read_emod_weather(): Read EMOD weather binary files into a data.frame
write_emod_weather(): Write EMOD binary weather files (.bin + .json) from a data.frame
write_emod_weather_by_adm2(): Write EMOD weather files per adm2 (one folder per district)

Project structure and paths

Create the AHADI hierarchical data folders, set up a full project skeleton, resolve standardised paths.

setup_project_paths(): Setup project paths and environment for SNT pipeline
create_data_structure(): Create Hierarchical Data Folder Structure (AHADI Style)
initialize_project_structure(): Initialize Full Project Folder Structure
ahadi_path(): Resolve path inside AHADI OneDrive shared library
clear_snt_cache(): clear snt variable tree cache

Translation and localisation

Cached Google Translate wrappers plus locale-aware year-month formatting.

translate_text(): Translate text to target language with persistent file cache
translate_text_vec(): Vectorized version of translate_text function
translate_yearmon(): Convert date to yearmon format with localized month names
french_malaria_acronyms(): French malaria acronyms mapping

Plotting helpers and trend models

Reusable building blocks for SNT plots - palettes, model wrappers, IR plots.

get_model(): Fit regression model for insecticide resistance trends
generate_ir_plot(): Generate insecticide resistance trend plot
run_resistance_trend(): Generate insecticide resistance trend scenarios
prepare_plot_data(): Prepare data for reporting rate or missing data visualization
get_pathway_vars(): Get Upstream and Downstream Variables for Malaria Pathway Indicators
auto_bin(): Automatically bin numeric data for choropleth maps
detect_factors(): Detect factor-like character columns (low-cardinality only)
detect_time_pattern(): Detect time pattern in filenames
extract_time_components(): Extract time components from a filename

Numeric and hashing utilities

Small but very-used helpers that show up across SNT pipelines.

big_mark(): Format Numbers with Thousand Separator
sum2(): Sum values with automatic NA handling
mean2(): Calculate mean with automatic NA handling
median2(): Calculate median with automatic NA handling
vdigest(): Vectorized version of digest::digest
compress_png(): Compress PNG Files in a Directory or a Single PNG File with pngquant