ghost is an R package designed to provide a simple interface for extracting data from the World Health Organization’s Global Health Observatory (GHO) database using the Open Data Protocol API. The package allows for exploration of indicators and dimensions available in the GHO and extract of these into R data frames.
gho_indicators()provides a data frame of all available indicators in the GHO.gho_dimensions()provides a data frame of all available dimensions in the GHO.gho_dimension_values()provides a data frame of all available values for a single dimension in the GHO.gho_data()extracts data from a selection of indicators in the GHO and outputs all results in a single data frame.
The interface is designed to be as simple as possible, only requiring input of the code of an indicator to extract it. However, it is also compatible with using more complex queries in line with the OData protocol. Details on its implementation available in the OData documentation.
ghost can be installed using remotes::install_github("gpw13/ghost")
To begin, we can use gho_indicators() to begin to explore all data
available in the GHO.
library(ghost)
gho_indicators()
#> # A tibble: 2,359 x 3
#> IndicatorCode IndicatorName Language
#> <chr> <chr> <chr>
#> 1 AIR_1 Ambient air pollution attributable deaths EN
#> 2 AIR_10 Ambient air pollution attributable DALYs per 100'000 … EN
#> 3 AIR_11 Household air pollution attributable deaths EN
#> 4 AIR_12 Household air pollution attributable deaths in childre… EN
#> 5 AIR_13 Household air pollution attributable deaths per 100'00… EN
#> # … with 2,354 more rowsIf we want the data for AIR_1, we could now just quickly access the
data frame using gho_data().
gho_data("AIR_1")
#> # A tibble: 372 x 23
#> Id IndicatorCode SpatialDimType SpatialDim TimeDimType TimeDim Dim1Type
#> <int> <chr> <chr> <chr> <chr> <int> <lgl>
#> 1 4882 AIR_1 COUNTRY AFG YEAR 2004 NA
#> 2 4883 AIR_1 COUNTRY ALB YEAR 2004 NA
#> 3 4884 AIR_1 COUNTRY DZA YEAR 2004 NA
#> 4 4885 AIR_1 COUNTRY AND YEAR 2004 NA
#> 5 4886 AIR_1 COUNTRY AGO YEAR 2004 NA
#> # … with 367 more rows, and 16 more variables: Dim1 <lgl>, Dim2Type <lgl>,
#> # Dim2 <lgl>, Dim3Type <lgl>, Dim3 <lgl>, DataSourceDimType <lgl>,
#> # DataSourceDim <lgl>, Value <chr>, NumericValue <dbl>, Low <dbl>,
#> # High <dbl>, Comments <lgl>, Date <chr>, TimeDimensionValue <chr>,
#> # TimeDimensionBegin <dbl>, TimeDimensionEnd <dbl>From here, standard methods of data manipulation (e.g. base R, the tidyverse) could be used to select variables, filter rows, and explore the data. However, we could also provide OData queries as desired, filtering on different dimensions of the data. Let’s first have a quick look at available dimensions.
gho_dimensions()
#> # A tibble: 91 x 2
#> Code Title
#> <chr> <chr>
#> 1 ADVERTISINGTYPE SUBSTANCE_ABUSE_ADVERTISING_TYPES
#> 2 AGEGROUP Age Group
#> 3 ALCOHOLTYPE Beverage Types
#> 4 AMRGLASSCATEGORY AMR GLASS Category
#> 5 ARCHIVE Archive date
#> # … with 86 more rowsLet’s say we want to filter by COUNTRY, then we can explore explore
the possible values the SpatialDim COUNTRY dimension can take.
gho_dimension_values("COUNTRY")
#> # A tibble: 245 x 6
#> Code Title Dimension ParentDimension ParentCode ParentTitle
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 AGO Angola COUNTRY REGION AFR Africa
#> 2 BDI Burundi COUNTRY REGION AFR Africa
#> 3 BEN Benin COUNTRY REGION AFR Africa
#> 4 BFA Burkina Faso COUNTRY REGION AFR Africa
#> 5 BWA Botswana COUNTRY REGION AFR Africa
#> # … with 240 more rowsIf we wanted to only extract AIR_1 data on Burundi from the GHO, then
we can now implement an OData query using the code we’ve identified
above. While ghost doesn’t implement complex checks on your OData
queries due to their complexity, it allows you to type them with spaces
and checks that each query begins with the required "$filter=...".
gho_data("AIR_1", "$filter=SpatialDim eq 'BDI'")
#> # A tibble: 2 x 23
#> Id IndicatorCode SpatialDimType SpatialDim TimeDimType TimeDim Dim1Type
#> <int> <chr> <chr> <chr> <chr> <int> <lgl>
#> 1 4909 AIR_1 COUNTRY BDI YEAR 2004 NA
#> 2 21904 AIR_1 COUNTRY BDI YEAR 2008 NA
#> # … with 16 more variables: Dim1 <lgl>, Dim2Type <lgl>, Dim2 <lgl>,
#> # Dim3Type <lgl>, Dim3 <lgl>, DataSourceDimType <lgl>, DataSourceDim <lgl>,
#> # Value <chr>, NumericValue <dbl>, Low <lgl>, High <lgl>, Comments <lgl>,
#> # Date <chr>, TimeDimensionValue <chr>, TimeDimensionBegin <dbl>,
#> # TimeDimensionEnd <dbl>And we can get data from the GHO on multiple indicators in one call, with the output data frames already merged together.
gho_data(c("AIR_1", "AIR_10", "AIR_11"), "$filter=SpatialDim eq 'BDI'")
#> # A tibble: 21 x 23
#> Id IndicatorCode SpatialDimType SpatialDim TimeDimType TimeDim Dim1Type
#> <int> <chr> <chr> <chr> <chr> <int> <chr>
#> 1 4909 AIR_1 COUNTRY BDI YEAR 2004 <NA>
#> 2 21904 AIR_1 COUNTRY BDI YEAR 2008 <NA>
#> 3 6479 AIR_10 COUNTRY BDI YEAR 2004 <NA>
#> 4 19580064 AIR_11 COUNTRY BDI YEAR 2016 SEX
#> 5 19580065 AIR_11 COUNTRY BDI YEAR 2016 SEX
#> # … with 16 more rows, and 16 more variables: Dim1 <chr>, Dim2Type <chr>,
#> # Dim2 <chr>, Dim3Type <lgl>, Dim3 <lgl>, DataSourceDimType <lgl>,
#> # DataSourceDim <lgl>, Value <chr>, NumericValue <dbl>, Low <dbl>,
#> # High <dbl>, Comments <lgl>, Date <chr>, TimeDimensionValue <chr>,
#> # TimeDimensionBegin <dbl>, TimeDimensionEnd <dbl>We can even provide different filters for each indicator separately,
such as Burundi for AIR_1, Uganda for AIR_10, and South Africa for
AIR_11.
gho_data(c("AIR_1", "AIR_10", "AIR_11"),
c("$filter=SpatialDim eq 'BDI'", "$filter=SpatialDim eq 'UGA'", "$filter=SpatialDim eq 'ZAF'"))
#> # A tibble: 21 x 23
#> Id IndicatorCode SpatialDimType SpatialDim TimeDimType TimeDim Dim1Type
#> <int> <chr> <chr> <chr> <chr> <int> <chr>
#> 1 4909 AIR_1 COUNTRY BDI YEAR 2004 <NA>
#> 2 21904 AIR_1 COUNTRY BDI YEAR 2008 <NA>
#> 3 6612 AIR_10 COUNTRY UGA YEAR 2004 <NA>
#> 4 19583070 AIR_11 COUNTRY ZAF YEAR 2016 SEX
#> 5 19583071 AIR_11 COUNTRY ZAF YEAR 2016 SEX
#> # … with 16 more rows, and 16 more variables: Dim1 <chr>, Dim2Type <chr>,
#> # Dim2 <chr>, Dim3Type <lgl>, Dim3 <lgl>, DataSourceDimType <lgl>,
#> # DataSourceDim <lgl>, Value <chr>, NumericValue <dbl>, Low <dbl>,
#> # High <dbl>, Comments <lgl>, Date <chr>, TimeDimensionValue <chr>,
#> # TimeDimensionBegin <dbl>, TimeDimensionEnd <dbl>Of course, the reality is that it’s likely easier for us to work outside the OData filtering framework and directly in R, so here’s a final more complex example using dplyr and stringr alongside ghost to automatically download all indicators with the word “drug” in the indicator name (case insensitive).
library(dplyr)
library(stringr)
gho_indicators() %>%
filter(str_detect(str_to_lower(IndicatorName), "drug")) %>%
pull(IndicatorCode) %>%
gho_data()
#> # A tibble: 25,302 x 23
#> Id IndicatorCode SpatialDimType SpatialDim TimeDimType TimeDim Dim1Type
#> <int> <chr> <chr> <chr> <chr> <int> <chr>
#> 1 273692 MALARIA_30539 COUNTRY MWI YEAR 2004 RESIDENCEA…
#> 2 273693 MALARIA_30539 COUNTRY MWI YEAR 2004 RESIDENCEA…
#> 3 273694 MALARIA_30539 COUNTRY MWI YEAR 2004 <NA>
#> 4 273695 MALARIA_30539 COUNTRY TZA YEAR 2004 RESIDENCEA…
#> 5 273714 MALARIA_30539 COUNTRY BDI YEAR 2005 RESIDENCEA…
#> # … with 25,297 more rows, and 16 more variables: Dim1 <chr>, Dim2Type <lgl>,
#> # Dim2 <lgl>, Dim3Type <lgl>, Dim3 <lgl>, DataSourceDimType <chr>,
#> # DataSourceDim <lgl>, Value <chr>, NumericValue <dbl>, Low <lgl>,
#> # High <lgl>, Comments <lgl>, Date <chr>, TimeDimensionValue <chr>,
#> # TimeDimensionBegin <dbl>, TimeDimensionEnd <dbl>And once we have that data, we can then filter, explore, and analyze the data with our standard R workflow, or even export the downloaded data to Excel or other analytical tools for further use.