Function reference • billionaiRe

Using the right populations for aggregation between countries can be tricky. Those functions provide helper to add the appropriate populations in all cases.

add_hep_populations(): Add Population Figures for HEP Billion

add_hpop_populations(): Add Population Figures for HPOP Billion

add_populations(): Add Population Figures for all billions

pop_links: HPOP Billion population links

Aggregations

Aggregating multiple countries values together requires special considerations. Those functions provide helper.

calculate_contribution_sums(): Calculate global/regional billions contributions

Acceleration scenarios

Functions to generate acceleration scenarios values at indicator-level. See vignette("acceleration-scenarios", package = "billionaiRe") for more details.

HEP

accelerate_cholera_campaign(): Accelerate cholera

accelerate_detect() accelerate_respond() accelerate_notify() accelerate_detect_respond(): Accelerate Detect, Notify, Respond, and Detect and Respond

accelerate_espar(): Accelerate espar

accelerate_measles_routine(): Accelerate measles

accelerate_meningitis_campaign() accelerate_meningitis_routine(): Accelerate meningitis

accelerate_polio_routine(): Accelerate polio

accelerate_yellow_fever_campaign() accelerate_yellow_fever_routine(): Accelerate yellow_fever

HPOP

accelerate_adult_obese(): Accelerate adult_obese

accelerate_alcohol(): Accelerate alcohol

accelerate_child_obese(): Accelerate child_obese

accelerate_child_viol(): Accelerate child_viol

accelerate_devontrack(): Accelerate devontrack

accelerate_fuel(): Accelerate fuel

accelerate_hpop_sanitation(): Accelerate hpop_sanitation

accelerate_hpop_sanitation_rural(): Accelerate hpop_sanitation_rural

accelerate_hpop_sanitation_urban(): Accelerate hpop_sanitation_urban

accelerate_hpop_tobacco(): Accelerate hpop_tobacco

accelerate_ipv(): Accelerate ipv

accelerate_overweight(): Accelerate overweight

accelerate_pm25(): Accelerate pm25

accelerate_road(): Accelerate road

accelerate_stunting(): Accelerate stunting

accelerate_suicide(): Accelerate suicide

accelerate_transfats(): Accelerate transfats

accelerate_wasting(): Accelerate wasting

accelerate_water(): Accelerate water

accelerate_water_rural(): Accelerate water_rural

accelerate_water_urban(): Accelerate water_urban

UHC

accelerate_anc4(): Accelerate anc4

accelerate_art(): Accelerate art

accelerate_beds(): Accelerate beds

accelerate_bp(): Accelerate bp

accelerate_doctors(): Accelerate doctors

accelerate_dtp3(): Accelerate dtp3

accelerate_fh(): Accelerate fh

accelerate_fp(): Accelerate fp

accelerate_fpg(): Accelerate fpg

accelerate_hwf(): Accelerate hwf

accelerate_itn(): Accelerate itn

accelerate_nurses(): Accelerate nurses

accelerate_pneumo(): Accelerate pneumo

accelerate_tb(): Accelerate tb

accelerate_uhc_sanitation(): Accelerate uhc_sanitation

accelerate_uhc_tobacco(): Accelerate uhc_tobacco

Sustainable Development Goals (SDG) scenarios

Functions to generate Sustainable Development Goals (SDG) scenarios values at indicator-level. See vignette("sdg-scenarios", package = "billionaiRe") for more details.

HPOP

sdg_adult_obese(): Accelerate adult_obese to SDG target

sdg_alcohol(): Accelerate alcohol to SDG target

sdg_child_obese(): Accelerate child_obese to SDG target

sdg_child_viol(): Accelerate child_viol to SDG target

sdg_devontrack(): Accelerate devontrack to SDG target

sdg_fuel(): Accelerate fuel to SDG target

sdg_hpop_sanitation(): Accelerate hpop_sanitation to SDG target

sdg_hpop_sanitation_rural(): Accelerate hpop_sanitation_rural to SDG target

sdg_hpop_sanitation_urban(): Accelerate hpop_sanitation_urban to SDG target

sdg_hpop_tobacco(): Accelerate hpop_tobacco to SDG target

sdg_ipv(): Accelerate ipv to SDG target

sdg_overweight(): Accelerate overweight to SDG target

sdg_pm25(): Accelerate pm25 to SDG target

sdg_road(): Accelerate road to SDG target

sdg_stunting(): Accelerate stunting to SDG target

sdg_suicide(): Accelerate suicide to SDG targets

sdg_transfats(): Accelerate transfats to SDG targets

sdg_wasting(): Accelerate wasting to SDG targets

sdg_water(): Accelerate water to SDG target

sdg_water_rural(): Accelerate water_rural to SDG target

sdg_water_urban(): Accelerate water_urban to SDG target

UHC

sdg_anc4(): Accelerate anc4 to SDG target

sdg_art(): Accelerate art to SDG target

sdg_beds(): Accelerate beds to SDG target

sdg_bp(): Accelerate bp to SDG target

sdg_doctors(): Accelerate doctors to SDG target

sdg_dtp3(): Accelerate dtp3 to SDG target

sdg_fh(): Accelerate fh to SDG target

sdg_fp(): Accelerate fp to SDG target

sdg_fpg(): Accelerate fpg to SDG target

sdg_hwf(): Accelerate hwf to SDG target

sdg_itn(): Accelerate itn to SDG target

sdg_nurses(): Accelerate nurses to SDG target

sdg_pneumo(): Accelerate pneumo to SDG target

sdg_tb(): Accelerate tb to SDG target

sdg_uhc_sanitation(): Accelerate uhc_sanitation to SDG target

sdg_uhc_tobacco(): Accelerate uhc_tobacco to SDG target

Benchmarking scenario

benchmarking_anc4() benchmarking_art() benchmarking_beds() benchmarking_bp() benchmarking_doctors() benchmarking_nurses() benchmarking_hwf() benchmarking_dtp3() benchmarking_fh() benchmarking_fp() benchmarking_fpg() benchmarking_itn() benchmarking_pneumo() benchmarking_tb() benchmarking_uhc_sanitation() benchmarking_uhc_tobacco() benchmarking_espar(): Benchmarking scenarios

Basic scenarios

The basic scenarios are the building blocks of all other scenarios. See vignette("basic-scenarios", package = "billionaiRe") for more details.

AROC scenario

scenario_aroc(): Scenario to use the average annual rate of change

Linear change scenarios

scenario_linear_change() scenario_linear_change_col(): Scenario to add a linear point change

Reach a fixed target

scenario_fixed_target() scenario_fixed_target_col() scenario_halt_rise(): Scenario to reach a fixed target

Percent from baseline scenarios

scenario_percent_baseline(): Scenario to change by a fixed percentage from a baseline value by target year

Basic benchmarking scenarios

scenario_top_n_iso3(): Scenario to reach the top performing rate of change countries

Compairing scenarios

scenario_bau(): Scenario establish a business as usual scenario

scenario_best_in_region(): Scenario to add a linear percentage point aimed at regional values

scenario_best_of(): Scenario to pick the best scenario out of a list of scenarios

scenario_quantile(): Scenario to add a linear percentage point aimed at quantiles

Target scenarios

accelerate_target_anc4(): Accelerate anc4 to 95% by 2030

accelerate_target_art(): Accelerate art to 90.25 by 2025

accelerate_target_beds(): Accelerate beds to 18 by 2025

accelerate_target_bp(): Accelerate bp

accelerate_target_doctors(): Accelerate doctors

accelerate_target_dtp3(): Accelerate dtp3

accelerate_target_fh(): Accelerate fh

accelerate_target_fp(): Accelerate fp

accelerate_target_fpg(): Accelerate fpg

accelerate_target_hwf(): Accelerate hwf

accelerate_target_itn(): Accelerate itn

accelerate_target_nurses(): Accelerate nurses

accelerate_target_pneumo(): Accelerate pneumo

accelerate_target_tb(): Accelerate tb

accelerate_target_uhc_sanitation(): Accelerate uhc_sanitation

accelerate_target_uhc_tobacco(): Accelerate uhc_tobacco

Scenarios helper

Helper functions used to generate scenarios. See vignette("scenarios", package = "billionaiRe") for more details

add_scenario() add_scenario_indicator(): Add scenario to data frame

Populations

Using the right populations for aggregation between countries can be tricky. Those functions provide helper to add the appropriate populations in all cases.

add_hep_populations(): Add Population Figures for HEP Billion

add_hpop_populations(): Add Population Figures for HPOP Billion

add_populations(): Add Population Figures for all billions

pop_links: HPOP Billion population links

WHDH interface

Load data

load_billion_data(): Load Billions indicator data

load_billion_data_legacy(): Load Raw Billions Indicator Data

load_billion_data_whdh(): Load Billions indicator data from WHDH

load_billion_data_xmart(): Load Billions indicator data from xMart

load_misc_data(): Load miscellaneous data

upload_billion_data(): Upload Billions indicator data

Data Wrangling

add_missing_xmart_rows(): Add missing rows for xMart upload

get_data_lake_name(): Return the name of the 3B data lake

get_whdh_path(): Generate file paths for the Triple Billions WHDH data lake

has_xmart_cols(): Check data frame for xMart4 columns

save_gho_backup_to_whdh(): Backup GHO data to the WHDH Data Lake

save_wrangled_output(): Save the output to disk after ensuring column specs

wrangle_gho_data(): Put GHO data into billionaiRe format

wrangle_gho_rural_urban_data(): Wrangle GHO data with TOTL/RUR/URB dimensions

wrangle_unsd_data(): Put UNSD data into billionaiRe format

xmart_col_types(): Get the column types for xMart columns

xmart_cols(): Get names of xMart4 columns

Data recycling

Functions to recycle the data and recombine it in different datasets into single timeseries that then can be used in the calculations. See vignette("data-recycling", package = "billionaiRe") for more details

recycle_data() recycle_data_scenario_single() make_default_scenario(): Recycle data between scenarios

remove_recycled_data(): Remove recycled values from df

Metadata

Functions to access the metadata.

billion_ind_codes(): Indicator codes for the Billions

convert_ind_codes(): Convert indicator codes between types

get_country_shares(): Get country shares data

get_ind_billion(): Denotes which billion a given indicator belongs to

get_ind_metadata(): Get metadata for a Triple Billions indicator(s)

get_sdi_ratio(): Get SDI ratio data

indicator_df: Dataset of indicators used within the Billions calculations.