Protein Requirement Campbell

The protein requirement of an individual is the amount of protein they must consume daily to stay in equilibrium. This number varies from individual to individual. Nutritionists often give the protein requirement in terms of grams of good quality protein per kilogram of body weight per day (g P * kg-1 * d-1). It is very difficult and expensive to measure the protein requirement in humans, but research into this area is very important. For example, how much protein should you give to a patient in a health care facility who must be fed enterally (i.e., through a feeding tube)? There are several ways in which nutritionists have tried to measure the protein requirement. Traditionally, they have used a method called Nitrogen Balance. In a nitrogen balance experiment, researchers provide a carefully controlled diet containing prescribed amounts of protein to each subject for an extended period of time. They then collect data on the amount of protein utilized by the body. This includes collecting and analyzing samples of urine, feces, blood, sweat, tears, exfoliated skin, etc. Most researchers collect urine and fecal samples and estimate other losses. The protein requirement is estimated as the level of intake required so that the amount of protein consumed is exactly equal to the losses. Because of the difficulty of measuring protein losses, and since protein is essentially the only source for dietary nitrogen, nitrogen is used as a marker for protein. A nitrogen balance experiment was conducted to determine if there is a difference in the mean protein requirement of individuals in four groups: 1. Old men (age 63-81) 2. Old women (age 63-81) 3. Young men (age 21-46) 4. Young women (age 21-46) Subjects were provided with a controlled diet for three months and were required to comply with study protocol. The data set gives the measured protein requirements for each of the subjects.
MATH221
health
biology
nutrition
Author

MATH 221

Published

May 1, 2024

Data details

There are 38 rows and 4 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/protein_requirement_campbell.

This data is available to all.

Variable description

  • Group: Group number (1 = elderly male, 2 = elderly woman, 3 = young male, 4 = young female)
  • Gender: Gender (Female, Male)
  • Age: Age (Elderly, Young)
  • ProteinRequirement: The protein requirement determined from the experiment in grams of protein per kilogram of body weight per day

Variable summary

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
Group 0 1 2.55 1.11 1.00 2.00 2.50 3.75 4.00 ▆▇▁▆▇
ProteinRequirement 0 1 0.59 0.12 0.31 0.51 0.62 0.67 0.86 ▂▃▇▅▂

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
Gender 0 1 4 6 0 2 0
Age 0 1 5 7 0 2 0
Explore generating code using R 
library(tidyverse)
library(pins)
library(connectapi)

protein_requirement_campbell <- read_csv('https://github.com/byuistats/data/raw/master/ProteinRequirement-Campbell/ProteinRequirement-Campbell.csv')


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

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

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/protein_requirement_campbell")
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/protein_requirement_campbell")