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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.
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")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 1The 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
)
)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: 60-71 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: 60-119 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.60-119 mo 32 32 81.2500
#> 23 Age + Sex: Male.60-119 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 NAUsing the function with it is easy to apply
anthroplus_prevalence to a full dataset.
To look at all parameters, type
?anthroplus_prevalence.
Contributions in the form of issues are very welcome. In particular if you find any bugs or cannot reproduce results obtained with other implementations.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.