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():
## A 'GHO' object of 108 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():
## A 'GHO' object of 248 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.)## A 'GHO' object of 2359 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.
## A 'GHO' object of 9 elements.
##
## Code Title
## 1 DHSMICSGEOREGION DHS/MICS subnational regions (Health equity monitor)
## 2 GBDREGION GBD Region
## 3 MGHEREG Region
## 4 REGION WHO region
## 5 UNICEFREGION UNICEF region
## 6 UNREGION UN Region
## ...
##
## (Printing 6 first elements.)## 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.
## 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):
## A 'GHO' object of 37328 elements.
##
## Id IndicatorCode Value NumericValue Low High
## 1 30548337 MDG_0000000001 105.66 [103.75-107.78] 105.6558 103.7520 107.7806
## 2 30548338 MDG_0000000001 104.74 [102.86-106.84] 104.7360 102.8642 106.8412
## 3 30548339 MDG_0000000001 103.92 [102.09-106] 103.9250 102.0883 106.0019
## 4 30548340 MDG_0000000001 103.09 [101.33-105.14] 103.0931 101.3256 105.1365
## 5 30548341 MDG_0000000001 102.96 [101.24-104.99] 102.9567 101.2391 104.9919
## 6 30548342 MDG_0000000001 100.9 [99.2-102.89] 100.8955 99.2008 102.8876
## Date TimeDimensionValue TimeDimensionBegin
## 1 2023-02-16T07:30:20.313+01:00 1990 1990-01-01T00:00:00+01:00
## 2 2023-02-16T07:30:20.343+01:00 1991 1991-01-01T00:00:00+01:00
## 3 2023-02-16T07:30:20.373+01:00 1992 1992-01-01T00:00:00+01:00
## 4 2023-02-16T07:30:20.39+01:00 1993 1993-01-01T00:00:00+01:00
## 5 2023-02-16T07:30:20.42+01:00 1994 1994-01-01T00:00:00+01:00
## 6 2023-02-16T07:30:20.47+01:00 1995 1995-01-01T00:00:00+01:00
## TimeDimensionEnd REGION COUNTRY YEAR SEX
## 1 1990-12-31T00:00:00+01:00 AFR <NA> 1990 BTSX
## 2 1991-12-31T00:00:00+01:00 AFR <NA> 1991 BTSX
## 3 1992-12-31T00:00:00+01:00 AFR <NA> 1992 BTSX
## 4 1993-12-31T00:00:00+01:00 AFR <NA> 1993 BTSX
## 5 1994-12-31T00:00:00+01:00 AFR <NA> 1994 BTSX
## 6 1995-12-31T00:00:00+01:00 AFR <NA> 1995 BTSX
## ...
##
## (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 30860630 MDG_0000000001 8.09 [7.84-8.37] 8.09124 7.83819 8.36952
## 2 30860982 MDG_0000000001 8.96 [8.67-9.28] 8.95834 8.66572 9.28486
## 3 30861334 MDG_0000000001 7.18 [6.95-7.44] 7.18045 6.95096 7.44140
## Date TimeDimensionValue TimeDimensionBegin
## 1 2023-03-01T16:34:25.443+01:00 2015 2015-01-01T00:00:00+01:00
## 2 2023-03-01T16:34:32.47+01:00 2015 2015-01-01T00:00:00+01:00
## 3 2023-03-01T16:34:39.03+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 MLE
## 3 2015-12-31T00:00:00+01:00 EUR 2015 FMLEFor details about how the requests are performed and the options
available (especially proxy settings) see
vignette("e-details", "rgho").