The ‘riskintrodata’ package is designed to provide a set of functions and datasets
that support the management of data used to estimate the risk of introducing an animal
disease into a specific geographical region.
It includes tools for reading and validating both geographic and tabular datasets
commonly used in the context of animal disease risk estimation.
Motivation
The primary motivation for creating ‘riskintrodata’ is to isolate and centralize the datasets and data import functions required by the ‘riskintro’ application
into a dedicated package. This separation simplifies testing, improves clarity,
and makes it easier to document the datasets used in the application in a structured way.
Additionally, the ‘riskintrodata’ package is designed to simplify package management.
It helps reduce the complexity of handling the numerous packages required by the
‘riskintro’ application. By centralizing essential datasets and their associated
import functions, ‘riskintrodata’ minimizes package dependencies.
Installation
You can install the development version of riskintrodata like so:
pak::pak("ardata-fr/riskintrodata")Read data
library(riskintrodata)The package provides functions to read and validate geographic and tabular datasets.
To read geographic data from a file, you can use the read_geo_file() function:
tun_files <-
system.file(
package = "riskintrodata",
"samples",
"tunisia",
"epi_units",
"tunisia_adm2_clean.gpkg"
)
read_geo_file(tun_files)
#> Simple feature collection with 268 features and 1 field
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: 7.530076 ymin: 30.23681 xmax: 11.59826 ymax: 37.55986
#> Geodetic CRS: WGS 84
#> # A tibble: 268 × 2
#> eu_name geom
#> <chr> <MULTIPOLYGON [°]>
#> 1 Ariana Médina (((10.13861 36.89453, 10.14495 36.89476, 10.15127 36.8947…
#> 2 Ettadhamen (((10.05585 36.84308, 10.06575 36.85019, 10.07327 36.8544…
#> 3 Kalaat El Andalous (((10.13862 36.89416, 10.1329 36.88994, 10.13283 36.88892…
#> 4 Mnihla (((10.1317 36.88428, 10.1317 36.88271, 10.1317 36.8797, 1…
#> 5 Raoued (((10.16651 36.88694, 10.16422 36.88874, 10.1576 36.89235…
#> 6 Sebkhet Ariana (((10.27118 36.88874, 10.26842 36.88874, 10.26149 36.8878…
#> 7 Sidi Thabet (((10.01018 37.00285, 10.0102 37.00285, 10.01045 37.00283…
#> 8 Soukra (((10.19313 36.85656, 10.19313 36.85892, 10.19313 36.8640…
#> 9 Amdoun (((9.141866 36.86897, 9.140129 36.86767, 9.137473 36.8660…
#> 10 Béja Nord (((9.086732 36.70221, 9.082556 36.70772, 9.078131 36.7114…
#> # ℹ 258 more rows
nga_files <- system.file(
package = "riskintrodata",
"samples",
"nigeria",
"epi_units",
"NGA-ADM1.geojson"
)
read_geo_file(nga_files)
#> Simple feature collection with 37 features and 5 fields
#> Geometry type: POLYGON
#> Dimension: XY
#> Bounding box: xmin: 2.692613 ymin: 4.270204 xmax: 14.67797 ymax: 13.88571
#> Geodetic CRS: WGS 84
#> # A tibble: 37 × 6
#> shapeName shapeISO shapeID shapeGroup shapeType geometry
#> <chr> <chr> <chr> <chr> <chr> <POLYGON [°]>
#> 1 Cross River NG-CR 276711… NGA ADM1 ((8.274303 4.854739, 8.3…
#> 2 Abuja Federa… NG-FC 276711… NGA ADM1 ((6.980815 8.443728, 7.0…
#> 3 Ogun NG-OG 276711… NGA ADM1 ((4.483238 6.326054, 4.4…
#> 4 Oyo NG-OY 276711… NGA ADM1 ((4.088356 7.133446, 4.0…
#> 5 Sokoto NG-SO 276711… NGA ADM1 ((4.126405 13.24967, 4.1…
#> 6 Zamfara NG-ZA 276711… NGA ADM1 ((4.941011 11.73083, 4.9…
#> 7 Lagos NG-LA 276711… NGA ADM1 ((2.704644 6.459847, 2.6…
#> 8 Akwa Ibom NG-AK 276711… NGA ADM1 ((7.88037 5.366796, 7.87…
#> 9 Bayelsa NG-BY 276711… NGA ADM1 ((5.448385 5.133691, 5.4…
#> 10 Ondo NG-ON 276711… NGA ADM1 ((4.483238 6.326054, 4.5…
#> # ℹ 27 more rowsTo read raster data, you can use the read_raster_file() function:
road_access_raster_file <- download_road_access_raster()
ra_raster <- read_raster_file(x = road_access_raster_file)
ra_rasterThere is no specific function to read tabular data, but you can use the package ‘readr’ or ‘readxl’ to read the data if the format is CSV or Excel.
Validate data
The package provides a function named validate_table_content() to validate the content of datasets. This function checks the structure of the data and ensures that it meets the expected format, the function will check:
- the presence of required columns
- the data types of the columns, mandatory or optional
- and a set of rules to validate the data.
It can be used with datasets for:
- Epi units, use
validate_table_content(..., name = "epi_units"). - Emission risks, use
validate_table_content(..., name = "emission_risk_factors"). - Animal mobility, use
validate_table_content(..., name = "animal_mobility"). - Entry points, use
validate_table_content(..., name = "entry_points").
The function takes a data frame or an ‘sf’ object as input, along with the type of the dataset and any additional arguments for mapping columns. It returns a list containing the validation status of the dataset, i.e. the required and optional columns, the validation rules, and the dataset itself after renaming and selecting the specified columns.
tun_epi_files <-
system.file(
package = "riskintrodata",
"samples",
"tunisia",
"epi_units", "tunisia_adm2_raw.gpkg"
)
tun_epi_unit <- read_geo_file(tun_epi_files)
DATA_EPI_UNITS <- validate_table_content(
x = tun_epi_unit,
table_name = "epi_units",
eu_name = "shapeName",
user_id = "fid"
)
DATA_EPI_UNITS
#> $table_name
#> [1] "epi_units"
#>
#> $required_columns
#> $chk
#> [1] FALSE
#>
#> $msg
#> The following required columns are missing: `eu_name` and `geometry`
#>
#> $details
#> [1] "eu_name" "geometry"
#>
#> attr(,"class")
#> [1] "validation_status"
#>
#> $optional_columns
#> $chk
#> [1] TRUE
#>
#> $msg
#> [1] "Optional columns selected are available."
#>
#> $details
#> character(0)
#>
#> attr(,"class")
#> [1] "validation_status"
#>
#> $validate_rules
#> $chk
#> [1] FALSE
#>
#> $msg
#> [1] "Found invalidities while checking dataset."
#>
#> $details
#> # A tibble: 4 × 8
#> colname valid required column_found n index value msg
#> <chr> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <glue>
#> 1 eu_id TRUE FALSE TRUE NA NA NA "eu_id" has been valid…
#> 2 eu_id TRUE FALSE TRUE NA NA NA "eu_id" has been valid…
#> 3 eu_name FALSE TRUE FALSE NA NA NA Column: "eu_name" is m…
#> 4 geometry FALSE TRUE FALSE NA NA NA Column: "geometry" is …
#>
#> attr(,"class")
#> [1] "validation_status"
#>
#> $dataset
#> Simple feature collection with 268 features and 0 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: 7.530076 ymin: 30.23681 xmax: 11.59826 ymax: 37.55986
#> Geodetic CRS: WGS 84
#> # A tibble: 268 × 1
#> geom
#> <MULTIPOLYGON [°]>
#> 1 (((10.13861 36.89453, 10.14495 36.89476, 10.15127 36.89476, 10.1576 36.89235…
#> 2 (((10.05585 36.84308, 10.06575 36.85019, 10.07327 36.8544, 10.07366 36.85451…
#> 3 (((10.13862 36.89416, 10.1329 36.88994, 10.13283 36.88892, 10.1326 36.88572,…
#> 4 (((10.1317 36.88428, 10.1317 36.88271, 10.1317 36.8797, 10.12929 36.87579, 1…
#> 5 (((10.16651 36.88694, 10.16422 36.88874, 10.1576 36.89235, 10.15127 36.89476…
#> 6 (((10.27118 36.88874, 10.26842 36.88874, 10.26149 36.88783, 10.25577 36.8863…
#> 7 (((10.01018 37.00285, 10.0102 37.00285, 10.01045 37.00283, 10.01063 37.00281…
#> 8 (((10.19313 36.85656, 10.19313 36.85892, 10.19313 36.86404, 10.19313 36.8667…
#> 9 (((9.141866 36.86897, 9.140129 36.86767, 9.137473 36.86604, 9.133329 36.8623…
#> 10 (((9.086732 36.70221, 9.082556 36.70772, 9.078131 36.71146, 9.075724 36.7131…
#> # ℹ 258 more rows
#>
#> attr(,"class")
#> [1] "table_validation_status"The mapping_* functions are utilities to help users map their own data column names to the standardized column names expected by the ‘riskintrodata’ package. These mappings are used with the apply_mapping() function to rename, select, and validate columns for risk analysis workflows.
Mapping for entry points
Maps columns for entry points datasets (e.g., border crossings, airports, seaports).
Required:
- point_name: Name/description of the entry point
- Geospatial info: either lat/lng or geometry
Optional:
- mode: Contraband status (C, NC, or missing)
- type: Type of entry point (AIR, SEA, BC, CC, TC, or missing)
- sources: List of ISO3 codes for source countries
Mapping for epidemiological units
Maps columns for epidemiological units datasets (e.g., administrative areas).
Required:
- eu_name: Name/description of the epi unit
- geometry: Geospatial polygon/multipolygon
Optional:
- eu_id: Unique identifier for the epi unit
Mapping for animal mobility
Maps columns for animal movement datasets.
Required:
- o_name: Origin name
- d_name: Destination name
- d_lng, d_lat: Destination longitude/latitude
Optional:
- o_iso3, o_lng, o_lat: Origin ISO3 code or coordinates
- d_iso3: Destination ISO3 code
- quantity: Number of animals moved
Mapping for emission risk factors
Maps columns for emission risk factors datasets (used for risk scoring).
Required:
- iso3, country, disease, animal_category, species
- Control/surveillance measures: disease_notification, targeted_surveillance, general_surveillance, screening, precautions_at_the_borders, slaughter, selective_killing_and_disposal, zoning, official_vaccination
- last_outbreak_end_date, commerce_illegal, commerce_legal
Optional:
- data_source: Source of the data
Each mapping function returns a mapping object that can be passed to apply_mapping() to standardize and validate your dataset for use in the ‘riskintro’ analysis pipeline.
References data
The package includes several reference datasets that are used in the context of animal disease risk estimation:
- iso3 country codes, available with the function
country_reference(). Also a utility functioniso3_to_name()is provided to convert ISO3 codes to country names.