BYU-I Course Project Data

Data details

There are 277 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/biggest_loser.

This data is available to all.

Variable description

contestant_gender Gender of contestant (Female, Male)
contestant_age_group Age group (< 30, 30 to 39, 40 to 49, 50 to 59, 60 Plus)
contestant_age Age (years)
initial_weight_at_start_show Initial weight at start of show (lbs)
weight_after_1_week Weight after one week (lbs)
height Height (in)
season Season of show (1 to 17)
trainer Trainer first name
prop_initial_weight_lost Unknown (appears to be a quantity of weight in lbs)
prop_initial_weight_lost_week1 Unknown (Presumes to be weight lost, but numbers are NOT the difference between the initial weight and weight after one week)
winner Whether contestant won or lost (lost, won)

Variable summary

Variable type: numeric

skim_variable	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
contestant_age	1	34.71	10.87	18.00	27.00	31.00	41.00	66.00	▆▇▃▂▁
initial_weight_at_start_show	1	311.29	71.04	167.00	252.00	297.00	365.00	526.00	▂▇▅▃▁
weight_after_1_week	1	294.46	66.85	164.00	239.00	285.00	344.00	492.00	▃▇▅▃▁
height	1	69.00	4.17	60.00	66.00	68.00	72.00	80.00	▂▇▅▃▁
season	1	9.14	4.72	1.00	5.00	9.00	13.00	17.00	▇▇▆▆▇
prop_initial_weight_lost	1	36.16	9.68	7.37	29.29	36.18	43.94	59.62	▁▃▇▆▂
prop_initial_weight_lost_week1	1	5.37	1.72	0.92	4.20	5.24	6.46	10.44	▂▆▇▃▁

Variable type: character

skim_variable	complete_rate	min	max	n_unique
contestant_gender	1	4	6	2
contestant_age_group	1	4	8	5
trainer	1	3	13	9
winner	1	3	4	2

Explore generating code using R

library(tidyverse)
library(pins)
library(connectapi)

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


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

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

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/biggest_loser")

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/biggest_loser")

Footnotes

Unknown ↩︎