DASL Cheese

Data details

There are 30 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/dasl_cheese.

This data is available to all.

Variable description

• Taste: A subjective score for the taste of each cheese, obtained by combining the scores of several tasters; higher values indicate better taste
• Acetic: Natural logarithm of concentration of acetic acid in the sample
• H2S: Natural logarithm of concentration of hydrogen sulfide in the sample
• Lactic: Lactic acid concentration

Variable summary

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
Taste	0	1	24.53	16.26	0.70	13.55	20.95	36.70	57.20	▅▇▃▃▃
Acetic	0	1	5.50	0.57	4.48	5.24	5.42	5.88	6.46	▆▆▇▇▇
H2S	0	1	5.94	2.13	3.00	3.98	5.33	7.57	10.20	▇▆▅▅▃
Lactic	0	1	1.44	0.30	0.86	1.25	1.45	1.67	2.01	▃▇▅▅▅

NULL

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

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


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

pin_name <- "dasl_cheese"
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_cheese.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_cheese/"
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_cheese")

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_cheese")

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_cheese")

Footnotes

1. Cornell University DASL Project. The data and story library. Website. http://lib.stat.cmu.edu/DASL/