bdsreader

The bdsreader package is a lightweight package that

  • Defines JSON data schema’s for electronic data exchange of child-level data based on the Basisdataset JGZ 4.0.1;
  • Reads and parses child data coded according to the JSON data schema;
  • Compares the child data against the JSON validation schema;
  • Calculates the child’s D-score from the Van Wiechen Schema (DDI) responses;
  • Adds Z-scores for height, weight, head circumference, BMI, weight-for-height and D-score;
  • Converts the result into a a list with person-level and time-level data for use in JAMES.

The bdsreader translates child data (incoming via an API request) into a data object useful for R processing. The package is part of Joint Automatic Measurement and Evaluation System (JAMES) developed by the Netherlands Organisation for Applied Scientific Research TNO.

Installation

Install the jamesdemodata and bdsreader packages as

install.packages("remotes")
remotes::install_github("growthcharts/jamesdemodata")
remotes::install_github("growthcharts/bdsreader")

There is no CRAN release of these packages.

Examples

read_bds() and write_bds() are the main functions of the package.

Reading JSON child data

We work with an example dataset maria.json, containing the data of one child, from the jamesdemodata package. Find the filename and show the first 10 lines:

fn <- system.file("json", "examples", "maria.json", package = "jamesdemodata")
cat(paste(readLines(fn, n = 10), collapse = "\n"), "\n...")
#> {
#>   "Format": "3.0",
#>   "organisationCode": 1234,
#>   "reference": "Maria",
#>   "clientDetails": [
#>     {
#>       "bdsNumber": 19,
#>       "value": "2"
#>     },
#>     { 
#> ...

The following commands illustrate the use of the `read_bds()`` function:

library(bdsreader)
child <- read_bds(fn)
class(child)
#> [1] "bdsreader" "list"

read.bds() returns a bdsreader object. We print it as

child
#> <bdsreader>
#> $persondata:
#> # A tibble: 1 × 17
#>      id name  src   dnr   sex      gad    ga   smo    bw  hgtm  hgtf  agem  blbf
#>   <int> <chr> <chr> <chr> <chr>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1    -1 Maria 1234  <NA>  female   189    27     1   990   167   190    NA    NA
#> # ℹ 4 more variables: blbm <int>, eduf <int>, edum <int>, par <int>
#> 
#> $timedata:
#> # A tibble: 13 × 8
#>       age xname yname zname zref                        x     y      z
#>     <dbl> <chr> <chr> <chr> <chr>                   <dbl> <dbl>  <dbl>
#>  1 0.0849 age   hgt   hgt_z nl_2012_hgt_female_27  0.0849 38    -0.158
#>  2 0.167  age   hgt   hgt_z nl_2012_hgt_female_27  0.167  43.5   0.047
#>  3 0      age   wgt   wgt_z nl_2012_wgt_female_27  0       0.99  0.19 
#>  4 0.0849 age   wgt   wgt_z nl_2012_wgt_female_27  0.0849  1.25 -0.203
#>  5 0.167  age   wgt   wgt_z nl_2012_wgt_female_27  0.167   2.1   0.015
#>  6 0.0849 age   hdc   hdc_z nl_2012_hdc_female_27  0.0849 27    -0.709
#>  7 0.167  age   hdc   hdc_z nl_2012_hdc_female_27  0.167  30.5  -0.913
#>  8 0      age   bmi   bmi_z nl_1997_bmi_female_nl  0      NA    NA    
#>  9 0.0849 age   bmi   bmi_z nl_1997_bmi_female_nl  0.0849  8.66 -5.72 
#> 10 0.167  age   bmi   bmi_z nl_1997_bmi_female_nl  0.167  11.1  -3.77 
#> 11 0      hgt   wfh   wfh_z nl_2012_wfh_female_   NA       0.99 NA    
#> 12 0.0849 hgt   wfh   wfh_z nl_2012_wfh_female_   38       1.25 -0.001
#> 13 0.167  hgt   wfh   wfh_z nl_2012_wfh_female_   43.5     2.1   0.326

The object is a list with two elements: psn and zyx:

  • The psn element contains the person-level information;
  • The xyz element contains time-varying measurements for the child.

See ?init_bdsreader for more details on the structure of the bdsreader class.

Writing JSON child data

The write_bds() function perform the inverse operation. By default, it returns minified JSON:

js <- write_bds(child)
cat(paste(js, collapse = "\n"), "\n...")
#> {"Format":"3.0","organisationCode":0,"reference":"Maria","clientDetails":[{"bdsNumber":19,"value":"2"},{"bdsNumber":20,"value":"20181011"},{"bdsNumber":82,"value":189},{"bdsNumber":91,"value":"1"},{"bdsNumber":110,"value":990},{"bdsNumber":238,"value":1670},{"bdsNumber":240,"value":1900}],"clientMeasurements":[{"bdsNumber":235,"values":[{"date":"20181111","value":380},{"date":"20181211","value":435}]},{"bdsNumber":245,"values":[{"date":"20181011","value":990},{"date":"20181111","value":1250},{"date":"20181211","value":2100}]},{"bdsNumber":252,"values":[{"date":"20181111","value":270},{"date":"20181211","value":305}]}],"nestedDetails":[{"nestingBdsNumber":62,"nestingCode":"01","clientDetails":[{"bdsNumber":63,"value":"19950704"}]},{"nestingBdsNumber":62,"nestingCode":"02","clientDetails":[{"bdsNumber":63,"value":"19901202"}]}]} 
#> ...

You can write the data in a pretty format using the indent and to a file using file argument.