Computation of the WHO 2007 References for School-Age Children and Adolescents (5 to 19 Years).
anthroplus
The goal of anthroplus is to provide R functions for the application of the WHO Reference 2007 for 5-19 years to monitor the growth of school-age children and adolescents.
It is modeled after the R Macros of the WHO Reference 2007.
Installation
You can install the released version of anthroplus from CRAN with:
install.packages("anthroplus")
And the development version from GitHub with:
# install.packages("remotes")
remotes::install_github("worldhealthorganization/anthroplus")
Example
Z-scores
This function calculates z-scores for the three anthropometric indicators, weight-for-age, height-for-age and body mass index (BMI)-for-age.
library(anthroplus)
anthroplus_zscores(
sex = c("1", "f"),
age_in_months = c(100, 110),
height_in_cm = c(100, 90),
weight_in_kg = c(30, 40)
)
#> age_in_months csex coedema cbmi zhfa zwfa zbfa fhfa fwfa fbfa
#> 1 100 1 n 30.00000 -5.04 0.87 5.03 0 0 1
#> 2 110 2 n 49.38272 -7.06 1.78 7.37 1 0 1
The returned value is a data.frame that can further be processed or saved as a .csv file.
You can also use the function with a given dataset with with
your_data_set <- read.csv("my_survey.csv")
with(
your_data_set,
anthroplus_zscores(
sex = sex_column, age_in_months = age_column,
weight_in_kg = weight_column, height_in_cm = height_column,
oedema = oedema_column
)
)
Prevalence estimates
The function to compute the prevalence estimates is similar to anthroplus_zscores in terms of the parameters.
set.seed(1)
anthroplus_prevalence(
sex = c(1, 2),
age_in_months = rpois(100, 100),
height_in_cm = rnorm(100, 100, 10),
weight_in_kg = rnorm(100, 40, 10)
)[, c(1, 4, 5, 6)]
#> Group HAZ_pop HAZ_unwpop HA_3_r
#> 1 All 64 64 79.6875
#> 2 Sex: Female 32 32 81.2500
#> 3 Sex: Male 32 32 78.1250
#> 4 Age Group 1: 61-71 mo 0 0 NA
#> 5 Age Group 1: 72-83 mo 2 2 0.0000
#> 6 Age Group 1: 84-95 mo 16 16 75.0000
#> 7 Age Group 1: 96-107 mo 35 35 80.0000
#> 8 Age Group 1: 108-119 mo 11 11 100.0000
#> 9 Age Group 1: 120-131 mo 0 0 NA
#> 10 Age Group 1: 132-143 mo 0 0 NA
#> 11 Age Group 1: 144-155 mo 0 0 NA
#> 12 Age Group 1: 156-167 mo 0 0 NA
#> 13 Age Group 1: 168-179 mo 0 0 NA
#> 14 Age Group 1: 180-191 mo 0 0 NA
#> 15 Age Group 1: 192-203 mo 0 0 NA
#> 16 Age Group 1: 204-215 mo 0 0 NA
#> 17 Age Group 1: 216-227 mo 0 0 NA
#> 18 Age Group 1: 228-228 mo 0 0 NA
#> 19 Age Group 2: 61-119 mo 64 64 79.6875
#> 20 Age Group 2: 120-179 mo 0 0 NA
#> 21 Age Group 2: 180-228 mo 0 0 NA
#> 22 Age + Sex: Female.61-119 mo 32 32 81.2500
#> 23 Age + Sex: Male.61-119 mo 32 32 78.1250
#> 24 Age + Sex: Female.120-179 mo 0 0 NA
#> 25 Age + Sex: Male.120-179 mo 0 0 NA
#> 26 Age + Sex: Female.180-228 mo 0 0 NA
#> 27 Age + Sex: Male.180-228 mo 0 0 NA
Using the function with it is easy to apply anthroplus_prevalence to a full dataset.
To look at all parameters, type ?anthroplus_prevalence.
Contributions
Contributions in the form of issues are very welcome. In particular if you find any bugs or cannot reproduce results obtained with other implementations.