BYU-I Course Project Data

Data details

There are 30 rows and 7 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/dasl_stepping.

This data is available to all.

Variable description

Order: A unique key for each row; overall performance order of the trial
Subject: A unique key for each participating subject
Height: Height of step; 0 = low height (5.75”), 1 = high height (11.5”)
Frequency: Rate of stepping; 0 = slow (14 steps/minute), 1 = medium (21 steps/minute), 2 = high (28 steps/minute)
RestHR: Resting heart rate before trial (BPM)
HR: Final heart rate after trial (BPM)
IncreaseinHR: Difference between resting heart rate and final heart rate

Variable summary

Variable type: numeric

skim_variable	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
Order	1	15.5	8.80	1	8.25	15.5	22.75	30	▇▇▇▇▇
Subject	1	3.5	1.74	1	2.00	3.5	5.00	6	▇▃▃▃▃
Height	1	0.5	0.51	0	0.00	0.5	1.00	1	▇▁▁▁▇
Frequency	1	1.0	0.83	0	0.00	1.0	2.00	2	▇▁▇▁▇
RestHR	1	80.0	9.20	60	72.75	81.0	87.00	96	▂▆▇▇▆
HR	1	107.4	20.44	75	93.00	99.0	122.25	153	▅▇▃▃▂
IncreaseinHR	1	27.4	17.51	0	15.00	24.0	39.00	66	▃▇▃▂▂

NULL

Explore generating code using R

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

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


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

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

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/dasl_stepping")

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/dasl_stepping")

Footnotes

Cornell University DASL Project. The data and story library. Website. http://lib.stat.cmu.edu/DASL/Datafiles/Stepping.html.↩︎