Child height and weight HAZ summaries for multiple countries

Data from three different research studies. Each study had different research objectives.
health
child
Author

DS 150

Published

November 5, 2023

Data details

There are 2,851 rows and 12 columns. The data source1 is used to create our data that is stored in our pins table. You can access this pin from a connection to posit.byui.edu using hathawayj/childhealth_summary.

Variable description

  • subjid: unique identifier of each child
  • sex: Male or Female
  • country: Label for the varied countries
  • haz_mean: The average HAZ score over all measurements
  • waz_mean: The average WAZ score over all measurements
  • observations: Number of observations for that subject
  • agedays_last: The age in days for the HAZ and WAZ last variable
  • agedays_first: The age in days for the HAZ and WAZ first variable
  • haz_last: The first HAZ measurement on the subject for age in days
  • haz_first: The last HAZ measurement on the subject for age in days
  • waz_last: The last WAZ measurement on the subject for age in days
  • waz_first: The first WAZ measurement on the subject for age in days

Variable summary

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
haz_mean 280 0.90 -0.85 1.23 -5.99 -1.70 -0.91 -0.02 3.10 ▁▂▇▆▁
waz_mean 47 0.98 -0.52 1.13 -4.61 -1.26 -0.47 0.22 4.03 ▁▃▇▂▁
observations 0 1.00 18.43 9.61 1.00 8.00 25.00 26.00 27.00 ▃▁▁▁▇
agedays_last 3 1.00 895.35 691.06 0.00 729.00 730.00 734.00 2558.00 ▂▇▁▁▂
agedays_first 0 1.00 1.29 3.34 0.00 0.00 0.00 1.00 17.00 ▇▁▁▁▁
haz_last 305 0.89 -1.13 1.45 -6.72 -2.13 -1.24 -0.12 5.33 ▁▅▇▂▁
haz_first 1996 0.30 0.26 1.52 -9.82 -0.71 0.37 1.12 4.72 ▁▁▂▇▂
waz_first 0 1.00 -0.43 1.15 -5.98 -1.15 -0.40 0.35 3.60 ▁▁▇▇▁
waz_last 8 1.00 -0.63 1.33 -5.83 -1.53 -0.64 0.23 4.42 ▁▂▇▃▁

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
subjid 0 1 1 18 0 2851 0
sex 0 1 4 6 0 2 0
country 0 1 4 13 0 10 0
Explore generating code using R 
pacman::p_load(tidyverse, fs, sf, arrow, googledrive, downloader, fs, glue, rvest, pins, connectapi)

# get three sources
hbgd_temp <- tempfile()
download('https://github.com/HBGDki/hbgd/raw/master/data/cpp.rda', hbgd_temp, mode = 'wb')
load(hbgd_temp) #cpp object

tdat <- tempfile()
download("https://github.com/stefvanbuuren/brokenstick/raw/71dc99e62ce57b58d5c1d2a1074fbd4bf394e559/data/smocc_hgtwgt.rda",tdat, mode = "wb") #smocc_hgtwgt object
load(tdat)

sdrive <- shared_drive_find("byuids_data")
maled_file <- drive_ls(sdrive)  |>
    filter(stringr::str_detect(name, "MALED"))
tempf <- tempfile()
drive_download(maled_file, tempf)
dat <- read_csv(tempf)

# Format source data
childhealth_dutch <- smocc_hgtwgt |>
  select(subjid, sex, agedays, gagebrth, htcm, wtkg, haz, waz)

childhealth_us <- cpp %>%
  select(subjid, sex, agedays, gagebrth, htcm, wtkg, haz, waz, mrace, mage, meducyrs, ses)


childhealth_maled <- dat %>%
  select(
    subjid = `Participant ID`, sex = Sex, country = Country,
    agedays = `Age (days)`, wtkg = `Weight (kg)`, stcm = `Stature (cm)`,
    htcm = `Height (cm)`, lncm = `Recumbent length (cm)`,
    lh_used = `Recumbent length or height used for stature`,
    hccm = `Head circumference (cm)`,
    lhaz = `Length- or height-for-age z-score`,
    haz = `Height-for-age z-score`, laz= `Length-for-age z-score`,
    waz = `Weight-for-age z-score`, hcaz = `Head circumference-for-age z-score`,
    whz = `Weight-for-length or -height z-score`)


# combine to one file
childhealth_summary <- bind_rows(
  childhealth_dutch %>%
    select(subjid, agedays, sex, htcm, wtkg, haz, waz) %>%
    mutate(country = "Netherlands", subjid = as.character(subjid)),

  childhealth_maled %>%
    select(subjid, agedays,  sex, htcm = stcm, wtkg, lhaz, waz, country) %>%
    rename(haz = lhaz),

  childhealth_us %>%
    select(subjid, agedays, sex, htcm, wtkg, haz, waz) %>%
    mutate(country = "United States", subjid = as.character(subjid))
) %>%
  as_tibble() %>%
  group_by(subjid, sex, country) %>%
  arrange(agedays) %>%
  summarise(haz_mean = mean(haz, na.rm = TRUE), waz_mean = mean(waz), 
            observations = n(), agedays_last = agedays[n()], agedays_first = agedays[1],
            haz_last = haz[n()], haz_first = haz[1], waz_first = waz[1], waz_last = waz[n()]) %>%
  ungroup()

# push to board
board <- board_connect()
pin_write(board, childhealth_summary, type = "parquet", access_type = "all")

pin_name <- "childhealth_summary"
meta <- pin_meta(board, paste0("hathawayj/", pin_name))
client <- connect()
my_app <- content_item(client, meta$local$content_id)
set_vanity_url(my_app, paste0("data/", pin_name))

Access data

This data is available to all.

Direct Download: childhealth_summary.parquet

R and Python Download:

URL Connections:

For public data, any user can connect and read the data using pins::board_connect_url() in R.

library(pins)
url_data <- "https://posit.byui.edu/data/childhealth_summary/"
board_url <- board_connect_url(c("dat" = url_data))
dat <- pin_read(board_url, "dat")

Use this custom function in Python to have the data in a Pandas DataFrame.

import pandas as pd
import requests
from io import BytesIO

def read_url_pin(name):
  url = "https://posit.byui.edu/data/" + name + "/" + name + ".parquet"
  response = requests.get(url)
  if response.status_code == 200:
    parquet_content = BytesIO(response.content)
    pandas_dataframe = pd.read_parquet(parquet_content)
    return pandas_dataframe
  else:
    print(f"Failed to retrieve data. Status code: {response.status_code}")
    return None

# Example usage:
pandas_df = read_url_pin("childhealth_summary")

Authenticated Connection:

Our connect server is https://posit.byui.edu which you assign to your CONNECT_SERVER environment variable. You must create an API key and store it in your environment under CONNECT_API_KEY.

Read more about environment variables and the pins package to understand how these environment variables are stored and accessed in R and Python with pins.

library(pins)
board <- board_connect(auth = "auto")
dat <- pin_read(board, "hathawayj/childhealth_summary")
import os
from pins import board_rsconnect
from dotenv import load_dotenv
load_dotenv()
API_KEY = os.getenv('CONNECT_API_KEY')
SERVER = os.getenv('CONNECT_SERVER')

board = board_rsconnect(server_url=SERVER, api_key=API_KEY)
dat = board.pin_read("hathawayj/childhealth_summary")