rgho is an R package to access WHO GHO data from R via the GHO OData 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-values-gho", "rgho")).
It is possible to access dimensions with get_gho_dimensions():
get_gho_dimensions()## A 'GHO' object of 101 elements.
##
## Code Title
## 1 ADVERTISINGTYPE SUBSTANCE_ABUSE_ADVERTISING_TYPES
## 2 AGEGROUP Age Group
## 3 ALCOHOLTYPE Beverage Types
## 4 AMRGLASSCATEGORY AMR GLASS Category
## 5 ARCHIVE Archive date
## 6 ASSISTIVETECHBARRIER Barriers to accessing assistive products
## ...
##
## (Printing 6 first elements.)And codes for a given dimension with get_gho_values():
get_gho_values(dimension = "COUNTRY")## A 'GHO' object of 245 elements.
##
## Code Title
## 1 ABW Aruba
## 2 AFG Afghanistan
## 3 AGO Angola
## 4 AIA Anguilla
## 5 ALB Albania
## 6 AND Andorra
## ...
##
## (Printing 6 first elements.)get_gho_values(dimension = "GHO")## A 'GHO' object of 2264 elements.
##
## Code
## 1 Adult_curr_cig_smoking
## 2 Adult_curr_e-cig
## 3 Adult_curr_smokeless
## 4 Adult_curr_tob_smoking
## 5 Adult_curr_tob_use
## 6 Adult_daily_cig_smoking
## Title
## 1 Prevalence of current cigarette smoking among adults (%)
## 2 Prevalence of current e-cigarette use among adults (%)
## 3 Prevalence of current smokeless tobacco use among adults (%)
## 4 Prevalence of current tobacco smoking among adults (%)
## 5 Prevalence of current tobacco use among adults (%)
## 6 Prevalence of daily cigarette smoking among adults (%)
## ...
##
## (Printing 6 first elements.)The function search_dimensions() and search_values() research a term in dimension or codes labels, respectively.
search_dimensions("region")## A 'GHO' object of 8 elements.
##
## Code Title
## 1 DHSMICSGEOREGION DHS/MICS subnational regions (Health equity monitor)
## 2 GBDREGION GBD Region
## 3 MGHEREG Region
## 4 REGION WHO region
## 5 UNREGION UN Region
## 6 UNSDGREGION UN SDG Region
## ...
##
## (Printing 6 first elements.)search_values("neonatal", dimension = "GHO")## A 'GHO' object of 5 elements.
##
## Code
## 1 CM_03
## 2 nmr
## 3 WHOSIS_000003
## 4 WHS3_56
## 5 WHS4_128
## Title
## 1 Number of neonatal deaths (0 to 27 days)
## 2 Neonatal mortality rate (deaths per 1000 live births)
## 3 Neonatal mortality rate (0 to 27 days) per 1000 live births) (SDG 3.2.2)
## 4 Neonatal tetanus - number of reported cases
## 5 Neonates protected at birth against neonatal tetanus (PAB) (%)It is also possible to search results from an existing object.
result <- get_gho_values(dimension = "REGION")
search_gho(result, "asia")## A 'GHO' object of 5 elements.
##
## Code Title
## 1 GBD_REG14_SEARB South East Asia region, stratum B (SEAR B)
## 2 GBD_REG14_SEARD South East Asia region, stratum D (SEAR D)
## 3 OECD_NON_SEAR South-East Asia (non-OECD)
## 4 SEAR South-East Asia
## 5 WHO_LMI_SEAR Low-and-middle-income countries of the South-East Asia RegionAn 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):
result <- get_gho_data(
code = "MDG_0000000001"
)
print(result)## A 'GHO' object of 35933 elements.
##
## Id IndicatorCode Value NumericValue Low
## 1 27816263 MDG_0000000001 105.51 [103.62-107.68] 105.51129 103.61955
## 2 27816264 MDG_0000000001 97.2 [95.33-99.33] 97.19640 95.32996
## 3 27816265 MDG_0000000001 113.41 [111.29-115.88] 113.41113 111.28832
## 4 27816266 MDG_0000000001 104.72 [102.87-106.84] 104.71737 102.86652
## 5 27816267 MDG_0000000001 96.4 [94.57-98.52] 96.39962 94.56988
## 6 27816268 MDG_0000000001 112.62 [110.55-115.0] 112.62281 110.55119
## High Date TimeDimensionValue
## 1 107.68372 2022-01-18T13:12:40.993+01:00 1990
## 2 99.33017 2022-01-18T13:12:41.023+01:00 1990
## 3 115.87599 2022-01-18T13:12:41.04+01:00 1990
## 4 106.84179 2022-01-18T13:12:41.057+01:00 1991
## 5 98.51799 2022-01-18T13:12:41.07+01:00 1991
## 6 115.00073 2022-01-18T13:12:41.087+01:00 1991
## TimeDimensionBegin TimeDimensionEnd REGION COUNTRY YEAR SEX
## 1 1990-01-01T00:00:00+01:00 1990-12-31T00:00:00+01:00 AFR <NA> 1990 BTSX
## 2 1990-01-01T00:00:00+01:00 1990-12-31T00:00:00+01:00 AFR <NA> 1990 FMLE
## 3 1990-01-01T00:00:00+01:00 1990-12-31T00:00:00+01:00 AFR <NA> 1990 MLE
## 4 1991-01-01T00:00:00+01:00 1991-12-31T00:00:00+01:00 AFR <NA> 1991 BTSX
## 5 1991-01-01T00:00:00+01:00 1991-12-31T00:00:00+01:00 AFR <NA> 1991 FMLE
## 6 1991-01-01T00:00:00+01:00 1991-12-31T00:00:00+01:00 AFR <NA> 1991 MLE
## ...
##
## (Printing 6 first elements.)The filter argument in get_gho_data() allows request filtering:
result <- get_gho_data(
code = "MDG_0000000001",
filter = list(
REGION = "EUR",
YEAR = 2015
)
)
print(result)## A 'GHO' object of 3 elements.
##
## Id IndicatorCode Value NumericValue Low High
## 1 27816617 MDG_0000000001 8.0 [7.76-8.28] 8.00280 7.75596 8.27904
## 2 27816618 MDG_0000000001 7.11 [6.89-7.37] 7.10611 6.88558 7.36740
## 3 27816619 MDG_0000000001 8.85 [8.57-9.17] 8.85070 8.56613 9.17291
## Date TimeDimensionValue TimeDimensionBegin
## 1 2022-01-18T13:12:46.503+01:00 2015 2015-01-01T00:00:00+01:00
## 2 2022-01-18T13:12:46.52+01:00 2015 2015-01-01T00:00:00+01:00
## 3 2022-01-18T13:12:46.537+01:00 2015 2015-01-01T00:00:00+01:00
## TimeDimensionEnd REGION YEAR SEX
## 1 2015-12-31T00:00:00+01:00 EUR 2015 BTSX
## 2 2015-12-31T00:00:00+01:00 EUR 2015 FMLE
## 3 2015-12-31T00:00:00+01:00 EUR 2015 MLEFor details about how the requests are performed and the options available (especially proxy settings) see vignette("e-details", "rgho").