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 143 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 233 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 2833 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 10 elements.
##
## Code Title
## 1 DHSMICSGEOREGION DHS/MICS subnational regions (Health equity monitor)
## 2 GBDREGION GBD Region
## 3 MGHEREG MGHE Region
## 4 RCREGION RC Region
## 5 REGION WHO regions
## 6 SUBREGION WHO subregions by child and adult mortality (GBD)
## ...
##
## (Printing 6 first elements.)## A 'GHO' object of 6 elements.
##
## Code
## 1 CM_03
## 2 nmr
## 3 WHOSIS_000003
## 4 WHS2_515
## 5 WHS3_56
## 6 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 Distribution of causes of death among children aged <5 years (%) - Neonatal sepsis
## 5 Neonatal tetanus - number of reported cases
## 6 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 ParentLocationCode ParentLocation
## 1 1403778 MDG_0000000001 <NA> <NA>
## 2 1448715 MDG_0000000001 EUR Europe
## 3 378739 MDG_0000000001 AMR Americas
## 4 1035891 MDG_0000000001 EMR Eastern Mediterranean
## 5 874412 MDG_0000000001 AFR Africa
## 6 355877 MDG_0000000001 SEAR South-East Asia
## Value NumericValue Low High
## 1 11.76 [11.33-12.42] 11.75755 11.32657 12.42492
## 2 2.48 [2.29-2.68] 2.47646 2.28634 2.67760
## 3 160.46 [137.26-187.84] 160.45813 137.26335 187.83739
## 4 104.51 [84.65-136.78] 104.50555 84.64581 136.77897
## 5 119.68 [110.63-128.92] 119.67624 110.62894 128.91993
## 6 215.57 [181.37-258.94] 215.57298 181.36834 258.94449
## Date TimeDimensionValue TimeDimensionBegin
## 1 2023-02-16T07:31:03+01:00 2019 2019-01-01T00:00:00+01:00
## 2 2023-02-16T07:56:51+01:00 2021 2021-01-01T00:00:00+01:00
## 3 2023-02-16T07:54:36+01:00 1954 1954-01-01T00:00:00+01:00
## 4 2023-02-16T08:07:16+01:00 1998 1998-01-01T00:00:00+01:00
## 5 2023-02-16T07:55:59+01:00 1990 1990-01-01T00:00:00+01:00
## 6 2023-02-16T07:53:44+01:00 1950 1950-01-01T00:00:00+01:00
## TimeDimensionEnd REGION COUNTRY GLOBAL YEAR SEX
## 1 2019-12-31T00:00:00+01:00 AMR <NA> <NA> 2019 SEX_BTSX
## 2 2021-12-31T00:00:00+01:00 <NA> CZE <NA> 2021 SEX_MLE
## 3 1954-12-31T00:00:00+01:00 <NA> BRA <NA> 1954 SEX_MLE
## 4 1998-12-31T00:00:00+01:00 <NA> SOM <NA> 1998 SEX_BTSX
## 5 1990-12-31T00:00:00+01:00 <NA> COD <NA> 1990 SEX_BTSX
## 6 1950-12-31T00:00:00+01:00 <NA> BGD <NA> 1950 SEX_FMLE
## ...
##
## (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 1334428 MDG_0000000001 8.09 [7.84-8.37] 8.09124 7.83819 8.36952
## 2 1334430 MDG_0000000001 8.96 [8.67-9.28] 8.95834 8.66572 9.28486
## 3 1334429 MDG_0000000001 7.18 [6.95-7.44] 7.18045 6.95096 7.44140
## Date TimeDimensionValue TimeDimensionBegin
## 1 2023-03-01T16:34:25+01:00 2015 2015-01-01T00:00:00+01:00
## 2 2023-03-01T16:34:32+01:00 2015 2015-01-01T00:00:00+01:00
## 3 2023-03-01T16:34:39+01:00 2015 2015-01-01T00:00:00+01:00
## TimeDimensionEnd REGION YEAR SEX
## 1 2015-12-31T00:00:00+01:00 EUR 2015 SEX_BTSX
## 2 2015-12-31T00:00:00+01:00 EUR 2015 SEX_MLE
## 3 2015-12-31T00:00:00+01:00 EUR 2015 SEX_FMLEFor details about how the requests are performed and the options
available (especially proxy settings) see
vignette("e-details", "rgho").