Child height and weight measurements for all data from three studies at one year of age.

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 2,337 rows and 7 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/days_365.

Variable description

  • subjid: unique identifyer of each child
  • sex: Male or Female
  • wtkg: Weight measurement in kg (0.8-20.5)
  • htcm: Height in cm
  • haz: Height for age in SDS relative to WHO child growth standard
  • waz: Weight for age in SDS relative to WHO child growth standard
  • country: Label for the varied countries

Variable summary

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
htcm 255 0.89 72.60 3.41 62.00 70.20 72.30 75.00 89.00 ▁▇▆▁▁
wtkg 2 1.00 8.95 1.46 4.55 7.94 8.81 9.85 16.18 ▁▇▅▁▁
haz 255 0.89 -0.93 1.35 -4.94 -1.92 -1.02 -0.02 5.56 ▁▇▆▁▁
waz 2 1.00 -0.45 1.35 -5.23 -1.36 -0.48 0.44 5.08 ▁▅▇▂▁

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
subjid 0 1 1 18 0 2337 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)

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)


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)

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)

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`)


days_365 <- bind_rows(
  childhealth_dutch %>%
    filter(agedays %in% c(363:369)) %>%
    select(subjid, sex, htcm, wtkg, haz, waz) %>%
    mutate(country = "Netherlands", subjid = as.character(subjid)),
  
  childhealth_maled %>%
    filter(agedays %in% c(363:369)) %>%
    select(subjid, sex, htcm = stcm, wtkg, lhaz, waz, country) %>%
    rename(haz = lhaz),
  
  childhealth_us %>%
    filter(agedays == 366) %>%
    select(subjid, sex, htcm, wtkg, haz, waz) %>%
    mutate(country = "United States", subjid = as.character(subjid))
) %>%
  as_tibble()


board <- board_connect()

pin_write(board, days_365, type = "parquet", access_type = "all")

pin_name <- "days_365"
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: days_365.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/days_365/"
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("days_365")

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/days_365")
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/days_365")