Bone Mineral Density

Kudzu is a plant that was imported to the United States from Japan and now covers over seven million acres in the South. The plant contains chemicals called isoflavones that have been shown to have beneficial effects on bones. One study used three groups of rats to compare a control group with rats that were fed either a low dose or a high dose of isoflavones from kudzu. One of the outcomes examined was the bone mineral density in the femur (in grams per square centimeter). Researchers would like to test if the mean bone mineral density is different for the three different groups.
MATH221
health
Author

MATH 221

Published

April 25, 2024

Data details

There are 45 rows and 2 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/bone_mineral_data.

This data is available to all.

Variable description

  • Treatment: Whether the rat was in the control group, the high dose group, or the low dose group.
  • Bone.Mineral.Density: Bone mineral density of the rat’s femur (g/cm2)

Variable summary

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
Bone.Mineral.Density 0 1 0.22 0.02 0.2 0.21 0.22 0.23 0.27 ▇▇▆▂▂

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
Treatment 0 1 7 9 0 3 0
Explore generating code using R 
library(tidyverse)
library(pins)
library(connectapi)

bone_mineral_data <- read_csv('https://github.com/byuistats/data/raw/master/Bone_Mineral_Data/Bone_Mineral_Data.csv') %>% 
  select(!Description) # Remove the column that contains the description because it doesn't belong in the data


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

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

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/bone_mineral_data")
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/bone_mineral_data")

Footnotes

  1. Unknown↩︎