DASL Waste Run Up

The Levi-Strauss clothing manufacture plant in Albuquerque, New Mexico gets its cloth supplies from other supplying plants. These data were collected in order to determine whether there was a difference in waste management between five supplier plants. The quality control department collects data weekly on percentage waste (run-up). These values are relative to what computer pattern layouts would achieve. Negative values indicate that the employees did better than the computer at minimizing waste.
MATH221
production
efficiency
Author

MATH 221

Published

April 27, 2024

Data details

There are 95 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/dasl_waste_run_up.

This data is available to all.

Variable description

  • Source: Plant number
  • RunUp: Percentage waste (run-up)

Variable summary

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
RunUp 0 1 6.98 9.89 -11.6 2.55 5.2 9.95 70.2 ▇▇▁▁▁

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
Source 0 1 7 7 0 5 0
Explore generating code using R 
pacman::p_load(tidyverse, pins, connectapi, googledrive, readxl)

# Download the file from google drive (because the github copy only had one column)
sdrive <- shared_drive_find("byuids_data") # This will ask for authentication.
google_file <- drive_ls(sdrive) |>
  filter(stringr::str_detect(name, "DASL-WasteRunUp"))
tempf <- tempfile()
drive_download(google_file, tempf)
dasl_waste_run_up <- read_excel(tempf)


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

pin_name <- "dasl_waste_run_up"
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_waste_run_up.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_waste_run_up/"
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_waste_run_up")

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_waste_run_up")
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_waste_run_up")