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 development version bdsreader by

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

There is no CRAN release.

Example

The following commands illustrate the main use of bdsreader.

library(bdsreader)
fn <- system.file("examples", "maria.json", package = "bdsreader")
tgt <- read_bds(fn)
timedata(tgt)
#> # 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

Column age holds decimal age for the measurement. Every row contains a measurement yname, the conditioning variable xname and the Z-score zname. The column named zref holds the name of the growth reference (as defined in the nlreference package) used to calculate the Z-score. Columns y, x and z store their values, respectively.

The persondata() function extracts the person-level information:

persondata(tgt)
#> # A tibble: 1 × 22
#>      id name  dob        dobm       dobf       src   dnr   sex      gad    ga
#>   <int> <chr> <date>     <date>     <date>     <chr> <chr> <chr>  <dbl> <dbl>
#> 1    -1 Maria 2018-10-11 1990-12-02 1995-07-04 1234  <NA>  female   189    27
#> # ℹ 12 more variables: smo <dbl>, bw <dbl>, hgtm <dbl>, hgtf <dbl>, agem <dbl>,
#> #   etn <chr>, pc4 <chr>, blbf <int>, blbm <int>, eduf <int>, edum <int>,
#> #   par <int>

The result of read_bds() feeds into further data processing in R.

Breakdown in steps

JSON Input Data

The example file maria.json contains Maria’s data coded in JSON format according to BDS-schema file bds_v3.0.json. Here’s the contents of the file with the child data:

{
  "Format": "3.0",
  "organisationCode": 1234,
  "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"
        }
      ]
    }
  ]
}

JSON is a lightweight format to exchange data between electronic systems. Field "bdsNumber" refers to the numbers defined in the Basisdataset JGZ. Field "value" contains the value for the "bdsNumber". See Basisdataset JGZ 4.0.1 for more details on "bdsNumber".

Read and parse input data

Validate input data

Age calculation

D-score calculation

Z-score calculation

Structure of result

Further information