Vertebral Heights

When an x-ray or lateral radiograph of the spine is taken, it is not immediately clear whether a vertebra is fractured. Experts may disagree on their interpretations, and in order for the dimensions of the vertebrae to be used, they need a standard against which they can be compared. A group of researchers wanted to try a new approach to define what this standard of normal vertebral dimensions could be. Using lateral spinal radiographs, the heights of the vertebrae of 2992 women between the ages of 65 and 70 were recorded. Anterior, middle, and posterior measurements were taken. The data in the file represent the middle measurements. All the vertebrae were measured, but only the data on the T4 vertebra are represented. Note: Due to SPSS student version limitations, a random sample of 1000 values (rather than 2992) was selected).
health
anthropology
Author

MATH 221

Published

May 2, 2024

Data details

There are 1,000 rows and 1 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/vertebral_heights.

This data is available to all.

Variable description

  • VertebralHeight: Height of the T4 vertebra of the subject (centimeters)

Variable summary

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
VertebralHeight 0 1 2.16 0.16 1.62 2.05 2.16 2.27 2.63 ▁▃▇▅▁ 
NULL
Explore generating code using R 
library(tidyverse)
library(pins)
library(connectapi)

vertebral_heights <- read_csv('https://github.com/byuistats/data/raw/master/VertebralHeights/VertebralHeights.csv') %>% 
  rename(VertebralHeight = x) # Rename column to match documentation


# Publish the data to the server with Bro. Hathaway as the owner.
board <- board_connect()
pin_write(board, vertebral_heights, type = "parquet", access_type = "all")

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

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/vertebral_heights")
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/vertebral_heights")