US child height and weight measurements

Subset of growth data from the collaborative perinatal project (CPP).
health
child
Author

DS 150

Published

November 5, 2023

Data details

There are 500 rows and 11 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/birth_us.

Variable description

  • subjid: unique identifyer of each child
  • sex: Male or Female
  • agedays: Age in days
  • gagebrth: Gestational age at birth (days)
  • htcm: Length/height in cm (34-102)
  • wtkg: Weight measurement in kg (0.8-20.5)
  • haz: Height in SDS relative to WHO child growth standard
  • waz: Weight in SDS relative to WHO child growth standard
  • mrace: Race of the mother
  • mage: Mother age at child birth
  • meducyrs: Educational years of mother
  • ses: Socioeconomic status of mother

Variable summary

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
subjid 0 1.00 250.86 144.88 1 125.75 250.5 376.25 501 ▇▇▇▇▇
birthwt 0 1.00 3407.64 492.56 1531 3090.00 3402.0 3742.00 5386 ▁▃▇▂▁
birthlen 6 0.99 50.84 2.67 31 49.00 51.0 52.00 59 ▁▁▁▇▂
apgar1 19 0.96 7.71 1.89 1 7.00 8.0 9.00 10 ▁▁▂▆▇
apgar5 45 0.91 8.78 1.17 1 9.00 9.0 9.00 10 ▁▁▁▁▇
mage 0 1.00 26.86 6.09 13 22.00 26.0 31.00 43 ▂▇▆▃▁
smoked 4 0.99 0.48 0.50 0 0.00 0.0 1.00 1 ▇▁▁▁▇
meducyrs 61 0.88 11.30 1.99 1 10.00 12.0 12.00 17 ▁▁▃▇▁

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
sex 0 1 4 6 0 2 0
mrace 0 1 5 5 0 2 0
ses 0 1 3 12 0 6 0
Explore generating code using R 
pacman::p_load(tidyverse, fs, sf, arrow, googledrive, downloader, fs, glue, rvest, pins, connectapi)

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

load(tdat)

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

board <- board_connect()

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

pin_name <- "childhealth_dutch"
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: birth_us.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/birth_us/"
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("birth_us")

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/birth_us")
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/birth_us")