The ghoR package can be used to conventiently load data from the GHO portal of the WHO. The GHO database contains over 20,000 indicators which represent a statistic on a country level.
You can install the released version of ghoR from CRAN with:
install.packages("ghoR")And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("markkvdb/ghoR")If you are not sure yet which indicator you would like to explore you
can discover all indicators by loading it into a dataframe. To prevent
downloading the dataset every time you request a dataset, the dataset is
saved in ~/.ghoR/. This file is only updated if the WHO website shows
that the dataset is last updated after downloading our local version of
the dataset.
# First load the library and kable to present table
library(ghoR)
set.seed(420)
# Get a table of all indicators with the code and description.
indicators <- show_GHO_indicators()
indicators_sample <- dplyr::sample_n(indicators, 10)
knitr::kable(indicators_sample)| IndicatorCode | IndicatorName | Language |
|---|---|---|
| WHS7_108 | Per capita government expenditure on health (PPP int. $) | EN |
| CC_5 | Climate change attributable deaths (’000) in children under 5 years | EN |
| EMFLIMITELECTRIC | Electric field (kV/m) | EN |
| OCC_13 | Occupational noise attributable DALYs (’000) | EN |
| TOTENV_11 | Deaths attributable to the environment in children under 5 years (%) | EN |
| GDO_q8x3_5 | Accessibility of palliative and end-of-life care services in community for dementia [Capital/capital and main cities/capital, main cities, rural areas] | EN |
| TB_new_clindx | New or unknown treatment history cases: Pulmonary, clinically diagnosed | EN |
| HIV_0000000020 | Estimated percentage of pregnant women living with HIV who received antiretrovirals for preventing mother-to-child transmission | EN |
| IR_CARBAMATE_EXTENT | Percentage of sites for which carbamate resistance was reported | EN |
| SHS_1 | Percentage of children under 15 years exposed to second-hand smoke | EN |
As an example, we will look at the remaining life expectancy from age (x) for all available countries, years, sexes and ages. We can load this data using
ex_data <- read_GHO_data("LIFE_0000000035")
knitr::kable(head(ex_data, 10))| Id | IndicatorCode | SpatialDimType | SpatialDim | TimeDimType | TimeDim | Dim1Type | Dim1 | Dim2Type | Dim2 | Dim3Type | Dim3 | DataSourceDimType | DataSourceDim | Value | NumericValue | Low | High | Comments | Date |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 15578369 | LIFE_0000000035 | COUNTRY | RWA | YEAR | 2000 | SEX | BTSX | AGEGROUP | AGELT1 | NA | NA | NA | NA | 45.7 | 45.70058 | NA | NA | NA | 2017-03-31T08:39:36.323+02:00 |
| 15578390 | LIFE_0000000035 | COUNTRY | RWA | YEAR | 2000 | SEX | BTSX | AGEGROUP | AGE1-4 | NA | NA | NA | NA | 50.3 | 50.25219 | NA | NA | NA | 2017-03-31T08:39:37.38+02:00 |
| 15578411 | LIFE_0000000035 | COUNTRY | RWA | YEAR | 2000 | SEX | BTSX | AGEGROUP | AGE5-9 | NA | NA | NA | NA | 50.6 | 50.56797 | NA | NA | NA | 2017-03-31T08:39:38.317+02:00 |
| 15578432 | LIFE_0000000035 | COUNTRY | RWA | YEAR | 2000 | SEX | BTSX | AGEGROUP | AGE10-14 | NA | NA | NA | NA | 47.4 | 47.35501 | NA | NA | NA | 2017-03-31T08:39:39.123+02:00 |
| 15578453 | LIFE_0000000035 | COUNTRY | RWA | YEAR | 2000 | SEX | BTSX | AGEGROUP | AGE15-19 | NA | NA | NA | NA | 43.3 | 43.25390 | NA | NA | NA | 2017-03-31T08:39:40.14+02:00 |
| 15578474 | LIFE_0000000035 | COUNTRY | RWA | YEAR | 2000 | SEX | BTSX | AGEGROUP | AGE20-24 | NA | NA | NA | NA | 39.2 | 39.19952 | NA | NA | NA | 2017-03-31T08:39:41.17+02:00 |
| 15578495 | LIFE_0000000035 | COUNTRY | RWA | YEAR | 2000 | SEX | BTSX | AGEGROUP | AGE25-29 | NA | NA | NA | NA | 35.4 | 35.41076 | NA | NA | NA | 2017-03-31T08:39:42.293+02:00 |
| 15578516 | LIFE_0000000035 | COUNTRY | RWA | YEAR | 2000 | SEX | BTSX | AGEGROUP | AGE30-34 | NA | NA | NA | NA | 32 | 31.97939 | NA | NA | NA | 2017-03-31T08:39:43.363+02:00 |
| 15578537 | LIFE_0000000035 | COUNTRY | RWA | YEAR | 2000 | SEX | BTSX | AGEGROUP | AGE35-39 | NA | NA | NA | NA | 28.9 | 28.89335 | NA | NA | NA | 2017-03-31T08:39:44.717+02:00 |
| 15578558 | LIFE_0000000035 | COUNTRY | RWA | YEAR | 2000 | SEX | BTSX | AGEGROUP | AGE40-44 | NA | NA | NA | NA | 26 | 25.98571 | NA | NA | NA | 2017-03-31T08:39:45.543+02:00 |
Datasets downloaded from the WHO website do not follow the tidy
philosophy. For the spatial, time and other dimensions, each dimension
has a column for the unit of the dimension and one for the value. Most
users prefer having their datasets ready for analysis. The ghoR
package provides a function to transform the dataset as
tidy_data <- tidy_data(ex_data)
knitr::kable(head(tidy_data, 10))| Id | IndicatorCode | Value | NumericValue | Low | High | Comments | Date | COUNTRY | REGION | YEAR | SEX | AGEGROUP |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 15578369 | LIFE_0000000035 | 45.7 | 45.70058 | NA | NA | NA | 2017-03-31T08:39:36.323+02:00 | RWA | NA | 2000 | BTSX | AGELT1 |
| 15578390 | LIFE_0000000035 | 50.3 | 50.25219 | NA | NA | NA | 2017-03-31T08:39:37.38+02:00 | RWA | NA | 2000 | BTSX | AGE1-4 |
| 15578411 | LIFE_0000000035 | 50.6 | 50.56797 | NA | NA | NA | 2017-03-31T08:39:38.317+02:00 | RWA | NA | 2000 | BTSX | AGE5-9 |
| 15578432 | LIFE_0000000035 | 47.4 | 47.35501 | NA | NA | NA | 2017-03-31T08:39:39.123+02:00 | RWA | NA | 2000 | BTSX | AGE10-14 |
| 15578453 | LIFE_0000000035 | 43.3 | 43.25390 | NA | NA | NA | 2017-03-31T08:39:40.14+02:00 | RWA | NA | 2000 | BTSX | AGE15-19 |
| 15578474 | LIFE_0000000035 | 39.2 | 39.19952 | NA | NA | NA | 2017-03-31T08:39:41.17+02:00 | RWA | NA | 2000 | BTSX | AGE20-24 |
| 15578495 | LIFE_0000000035 | 35.4 | 35.41076 | NA | NA | NA | 2017-03-31T08:39:42.293+02:00 | RWA | NA | 2000 | BTSX | AGE25-29 |
| 15578516 | LIFE_0000000035 | 32 | 31.97939 | NA | NA | NA | 2017-03-31T08:39:43.363+02:00 | RWA | NA | 2000 | BTSX | AGE30-34 |
| 15578537 | LIFE_0000000035 | 28.9 | 28.89335 | NA | NA | NA | 2017-03-31T08:39:44.717+02:00 | RWA | NA | 2000 | BTSX | AGE35-39 |
| 15578558 | LIFE_0000000035 | 26 | 25.98571 | NA | NA | NA | 2017-03-31T08:39:45.543+02:00 | RWA | NA | 2000 | BTSX | AGE40-44 |
To prevent downloading the dataset every time you request a dataset, the
dataset is saved in ~/.ghoR/. This file is only updated if the WHO
website shows that the last update date is after the last updated date
of the downloaded dataset on the user’s computer.