WHO coeficients for height Z-score calculations

Data details

There are 1,800 rows and 6 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/height_coef.

Variable description

• agedays: Age of child in days
• sex: The gender of the child
• mean: The mean for a child at agedays
• cv: The coefficient of variation for children at age in days
• l: The box-cox transformation for children at age in days see documentation
• sd: The standard deviation of weights for children at age age in days

Variable summary

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
agedays	0	1	449.50	259.88	0.00	224.75	449.50	674.25	899.00	▇▇▇▇▇
mean	0	1	76.24	10.60	49.15	68.77	78.08	85.47	91.59	▂▃▅▆▇
sd	0	1	2.65	0.45	1.86	2.25	2.62	3.04	3.51	▇▇▇▇▆
cv	0	1	0.03	0.00	0.03	0.03	0.03	0.04	0.04	▆▂▇▅▂
l	0	1	1.00	0.00	1.00	1.00	1.00	1.00	1.00	▁▁▇▁▁

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
sex	0	1	4	6	0	2	0

# both need to be installed
# remotes::install_github("bokeh/rbokeh")
# remotes::install_github("ki-tools/growthstandards")

pacman::p_load(tidyverse, fs, sf, arrow, googledrive, downloader, fs,
  glue, rvest, pins, connectapi, growthstandards)


height_coef <- growthstandards::who_coefs$htcm_agedays$Female$data %>%
  as_tibble() %>%
  filter(x < 900) %>%
  select(agedays = x, mean = m, cv = s, l = l) %>%
  mutate(sex = "Female") %>%
  bind_rows(
    growthstandards::who_coefs$htcm_agedays$Male$data %>%
      as_tibble() %>%
      filter(x < 900) %>%
      select(agedays = x, mean = m, cv = s, l = l) %>%
      mutate(sex = "Male")
  ) %>%
  mutate(sd = mean * cv) %>%
  select(agedays, sex, mean, sd, cv, l)


board <- board_connect()
pin_write(board, height_coef, type = "parquet", access_type = "all")

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

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/height_coef")

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/height_coef")

Footnotes

1. https://github.com/hafen/growthstandards