rgho is an R package to access WHO GHO data from R via the Athena web service, an API providing a simple query interface to the World Health Organization’s data and statistics content.
As stated by the WHO website: The GHO data repository contains an extensive list of indicators, which can be selected by theme or through a multi-dimension query functionality. It is the World Health Organization’s main health statistics repository.
GHO data is composed of indicators structured in dimensions. The list of dimensions is available in vignette("b-dimensions", "rgho"), the list of indicators for the GHO dimension (the main dimension) in vignette("c-codes-gho", "rgho")).
It is possible to access dimensions with get_gho_dimensions():
## New names:
## * `` -> ...1
## * `` -> ...2
## * `` -> ...3## A 'GHO' object of 145 elements.
##
## Label ID
## 1 SUBSTANCE_ABUSE_ADVERTISING_TYPES ADVERTISINGTYPE
## 2 Age Group AGEGROUP
## 3 Action area ALCACTIONAREA
## 4 SUBSTANCE_ABUSE_ALCOHOL_POLICY_YEARS ALCOHOLPOLICYYEAR
## 5 Beverage Types ALCOHOLTYPE
## 6 AMR GLASS Category AMRGLASSCATEGORY
## ...
##
## (Printing 6 first elements.)And codes for a given dimension with get_gho_codes():
## New names:
## * `` -> ...1
## * `` -> ...2
## * `` -> ...3## New names:
## * `` -> ...3## A 'GHO' object of 247 elements.
##
## Label ID
## 1 Aruba ABW
## 2 Afghanistan AFG
## 3 Angola AGO
## 4 Anguilla AIA
## 5 Albania ALB
## 6 Andorra AND
## ...
##
## (Printing 6 first elements.)
##
## Attributes:
##
## DS
## FIPS
## GEOMETRY
## IOC
## ISO
## ISO2
## ITU
## LAND_AREA_KMSQ_2012
## LANGUAGES_EN_2012
## MARC
## MORT
## SHORTNAMEES
## SHORTNAMEFR
## WHO
## WHOLEGALSTATUS
## WHO_REGION
## WHO_REGION_CODE
## WMO
## WORLD_BANK_INCOME_GROUP
## WORLD_BANK_INCOME_GROUP_CODE
## WORLD_BANK_INCOME_GROUP_GNI_REFERENCE_YEAR
## WORLD_BANK_INCOME_GROUP_RELEASE_DATE## New names:
## * `` -> ...1
## * `` -> ...2
## * `` -> ...3
## New names:
## * `` -> ...3## A 'GHO' object of 3432 elements.
##
## Label
## 1 Ambient air pollution attributable deaths
## 2 Ambient air pollution attributable DALYs per 100'000 children under 5 years
## 3 Household air pollution attributable deaths
## 4 Household air pollution attributable deaths in children under 5 years
## 5 Household air pollution attributable deaths per 100'000 capita
## 6 Household air pollution attributable deaths per 100'000 children under 5 years
## ID
## 1 AIR_1
## 2 AIR_10
## 3 AIR_11
## 4 AIR_12
## 5 AIR_13
## 6 AIR_14
## ...
##
## (Printing 6 first elements.)
##
## Attributes:
##
## CATEGORY
## DEFINITION_XML
## DISPLAY_ES
## DISPLAY_FR
## IMR_ID
## RENDERER_IDThe number of printed items can be changed by the option rgho.n.
The function search_dimensions() and search_codes() research a term in dimension or codes labels, respectively.
## New names:
## * `` -> ...1
## * `` -> ...2
## * `` -> ...3## A 'GHO' object of 12 elements.
##
## Label ID
## 1 Subnational region DHSMICSGEOREGION
## 2 GBD Region GBDREGION
## 3 Region MGHEREG
## 4 OECD Region OECDREGION
## 5 WHO region REGION
## 6 SDG indicators regional groupings SDGREGION
## ...
##
## (Printing 6 first elements.)## New names:
## * `` -> ...1
## * `` -> ...2
## * `` -> ...3## New names:
## * `` -> ...3## A 'GHO' object of 7 elements.
##
## Label
## 1 Number of neonatal deaths (thousands)
## 2 Neonatal mortality rate (per 1000 live births)
## 3 Distribution of causes of death among children aged <5 years (%) - Neonatal sepsis
## 4 Neonatal tetanus - number of reported cases
## 5 Neonates protected at birth against neonatal tetanus (PAB) (%)
## 6 Neonatal mortality rate (deaths per 1000 live births)
## ID
## 1 CM_03
## 2 WHOSIS_000003
## 3 WHS2_515
## 4 WHS3_56
## 5 WHS4_128
## 6 nmr
## ...
##
## (Printing 6 first elements.)
##
## Attributes:
##
## CATEGORY
## DEFINITION_XML
## DISPLAY_ES
## DISPLAY_FR
## IMR_ID
## RENDERER_IDIt is also possible to search results from an existing object.
## New names:
## * `` -> ...1
## * `` -> ...2
## * `` -> ...3
## New names:
## * `` -> ...1
## * `` -> ...2
## * `` -> ...3## A 'GHO' object of 7 elements.
##
## Label ID
## 1 South East Asia region, stratum B (SEAR B) GBD_REG14_SEARB
## 2 South East Asia region, stratum D (SEAR D) GBD_REG14_SEARD
## 3 South-East Asia OECD_NON_SEAR
## 4 South-East Asia SEAR
## 5 High income countries of the South-East Asia Region WHO_HI_SEAR
## 6 Low-and-middle-income countries of the South-East Asia Region WHO_LMI_SEAR
## ...
##
## (Printing 6 first elements.)Dimension codes can be filtered according to their attributes.
## New names:
## * `` -> ...1
## * `` -> ...2
## * `` -> ...3## New names:
## * `` -> ...3## A 'GHO' object of 53 elements.
##
## Label ID
## 1 Albania ALB
## 2 Andorra AND
## 3 Armenia ARM
## 4 Austria AUT
## 5 Azerbaijan AZE
## 6 Belgium BEL
## ...
##
## (Printing 6 first elements.)
##
## Attributes:
##
## DS
## FIPS
## GEOMETRY
## IOC
## ISO
## ISO2
## ITU
## LAND_AREA_KMSQ_2012
## LANGUAGES_EN_2012
## MARC
## MORT
## SHORTNAMEES
## SHORTNAMEFR
## WHO
## WHOLEGALSTATUS
## WHO_REGION
## WHO_REGION_CODE
## WMO
## WORLD_BANK_INCOME_GROUP
## WORLD_BANK_INCOME_GROUP_CODE
## WORLD_BANK_INCOME_GROUP_GNI_REFERENCE_YEAR
## WORLD_BANK_INCOME_GROUP_RELEASE_DATEAttribute names and values can be displayed.
## [1] "code"
## [2] "DS"
## [3] "FIPS"
## [4] "GEOMETRY"
## [5] "IOC"
## [6] "ISO"
## [7] "ISO2"
## [8] "ITU"
## [9] "LAND_AREA_KMSQ_2012"
## [10] "LANGUAGES_EN_2012"
## [11] "MARC"
## [12] "MORT"
## [13] "SHORTNAMEES"
## [14] "SHORTNAMEFR"
## [15] "WHO"
## [16] "WHOLEGALSTATUS"
## [17] "WHO_REGION"
## [18] "WHO_REGION_CODE"
## [19] "WMO"
## [20] "WORLD_BANK_INCOME_GROUP"
## [21] "WORLD_BANK_INCOME_GROUP_CODE"
## [22] "WORLD_BANK_INCOME_GROUP_GNI_REFERENCE_YEAR"
## [23] "WORLD_BANK_INCOME_GROUP_RELEASE_DATE"## [1] "AFR" "AMR" "EMR" "EUR" "SEAR" "WPR"An indicator can be downloaded as a data_frame with get_gho_data(). Here we use MDG_0000000001, Infant mortality rate (probability of dying between birth and age 1 per 1000 live births):
## New names:
## * `` -> ...1
## * `` -> ...2
## * `` -> ...3
## New names:
## * `` -> ...1
## * `` -> ...2
## * `` -> ...3## New names:
## * `` -> ...3## Warning: 942 parsing failures.
## row col expected actual file
## 13205 Comments 1/0/T/F/TRUE/FALSE The most recent national official estimates of neonatal, infant and under-five mortality rates in India are from the India Sample Registration System with a rate of 23, 32 and 36 deaths per 1,000 live births, respectively, in the year 2018. literal data
## 13206 Comments 1/0/T/F/TRUE/FALSE The most recent national official estimates of neonatal, infant and under-five mortality rates in India are from the India Sample Registration System with a rate of 23, 32 and 36 deaths per 1,000 live births, respectively, in the year 2018. literal data
## 13207 Comments 1/0/T/F/TRUE/FALSE The most recent national official estimates of neonatal, infant and under-five mortality rates in India are from the India Sample Registration System with a rate of 23, 32 and 36 deaths per 1,000 live births, respectively, in the year 2018. literal data
## 13208 Comments 1/0/T/F/TRUE/FALSE The most recent national official estimates of neonatal, infant and under-five mortality rates in India are from the India Sample Registration System with a rate of 23, 32 and 36 deaths per 1,000 live births, respectively, in the year 2018. literal data
## 13209 Comments 1/0/T/F/TRUE/FALSE The most recent national official estimates of neonatal, infant and under-five mortality rates in India are from the India Sample Registration System with a rate of 23, 32 and 36 deaths per 1,000 live births, respectively, in the year 2018. literal data
## ..... ........ .................. ................................................................................................................................................................................................................................................ ............
## See problems(...) for more details.## # A tibble: 34,331 x 13
## GHO PUBLISHSTATE YEAR REGION UNREGION WORLDBANKINCOMEGROUP
## <chr> <chr> <dbl> <chr> <lgl> <lgl>
## 1 MDG_0000000001 PUBLISHED 1965 EMR NA NA
## 2 MDG_0000000001 PUBLISHED 1967 EMR NA NA
## 3 MDG_0000000001 PUBLISHED 1969 EMR NA NA
## 4 MDG_0000000001 PUBLISHED 1974 EMR NA NA
## 5 MDG_0000000001 PUBLISHED 1976 EMR NA NA
## 6 MDG_0000000001 PUBLISHED 1983 EMR NA NA
## 7 MDG_0000000001 PUBLISHED 1985 EMR NA NA
## 8 MDG_0000000001 PUBLISHED 1992 EMR NA NA
## 9 MDG_0000000001 PUBLISHED 1994 EMR NA NA
## 10 MDG_0000000001 PUBLISHED 1999 EMR NA NA
## COUNTRY SEX `Display Value` Numeric Low High Comments
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <lgl>
## 1 AFG BTSX 220. 220. 193. 252. NA
## 2 AFG BTSX 212. 212. 187. 243. NA
## 3 AFG BTSX 205. 205. 181. 234. NA
## 4 AFG BTSX 186. 185. 164. 213. NA
## 5 AFG BTSX 178. 178. 158. 203. NA
## 6 AFG BTSX 148. 148. 134. 167. NA
## 7 AFG BTSX 140. 140. 128. 156. NA
## 8 AFG BTSX 113. 113. 105. 122. NA
## 9 AFG BTSX 107. 107. 99.3 115. NA
## 10 AFG BTSX 92.7 92.7 86.7 99.5 NA
## # … with 34,321 more rowsThe filter argument in get_gho_data() allows request filtering:
result <- get_gho_data(
dimension = "GHO",
code = "MDG_0000000001",
filter = list(
REGION = "EUR",
YEAR = "2015"
)
)## New names:
## * `` -> ...1
## * `` -> ...2
## * `` -> ...3
## New names:
## * `` -> ...1
## * `` -> ...2
## * `` -> ...3## New names:
## * `` -> ...3## # A tibble: 159 x 11
## GHO PUBLISHSTATE YEAR REGION COUNTRY SEX `Display Value`
## <chr> <chr> <dbl> <chr> <chr> <chr> <dbl>
## 1 MDG_0000000001 PUBLISHED 2015 EUR ALB BTSX 8.5
## 2 MDG_0000000001 PUBLISHED 2015 EUR ALB FMLE 7.6
## 3 MDG_0000000001 PUBLISHED 2015 EUR ALB MLE 9.4
## 4 MDG_0000000001 PUBLISHED 2015 EUR AND BTSX 3.3
## 5 MDG_0000000001 PUBLISHED 2015 EUR AND FMLE 3
## 6 MDG_0000000001 PUBLISHED 2015 EUR AND MLE 3.6
## 7 MDG_0000000001 PUBLISHED 2015 EUR ARM BTSX 12.8
## 8 MDG_0000000001 PUBLISHED 2015 EUR ARM FMLE 11.4
## 9 MDG_0000000001 PUBLISHED 2015 EUR ARM MLE 14.2
## 10 MDG_0000000001 PUBLISHED 2015 EUR AUT BTSX 3
## Numeric Low High Comments
## <dbl> <dbl> <dbl> <lgl>
## 1 8.51 8.21 8.84 NA
## 2 7.56 7.23 7.93 NA
## 3 9.41 9.01 9.84 NA
## 4 3.34 1.17 10.0 NA
## 5 3.01 1.04 9.03 NA
## 6 3.64 1.28 11.0 NA
## 7 12.8 10.2 15.6 NA
## 8 11.4 9.06 13.9 NA
## 9 14.2 11.3 17.3 NA
## 10 3.05 2.92 3.18 NA
## # … with 149 more rowsOther parameters than format can be specified to get_gho_data() (such as apikey, asof…). Parameters are listed on this page. Note that most parameters are not available to general users.
For details about how the requests are performed and the option availables (especially proxy settings) see vignette("e-details", "rgho").