DASL Taste Test Scores

In testing food products for palatability, General Foods employed a 7-point scale from -3 (terrible) to +3 (excellent) with 0 representing “average”. Their standard method for testing palatability was to conduct a taste test with 50 persons - 25 men and 25 women. The experiment reported here involved the effects on palatability of a course versus fine screen and of a low versus high concentration of a liquid component. Four groups of 50 consumers each were recruited from local churches and club groups. Persons were assigned randomly to the four treatment groups as they were recruited. The experiment was replicated four times, so that there were 16 groups of 50 consumers each in the entire experiment.
MATH221
food
Author

MATH 221

Published

April 27, 2024

Data details

There are 16 rows and 3 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_taste_test_scores.

This data is available to all.

Variable description

  • Score: Palatability score
  • Screen: Screen type (Coarse, Fine)
  • Liquid: Concentration of a liquid component (High, Low)

Variable summary

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
Score 0 1 64.62 33.39 16 38 64.5 88 129 ▆▅▅▇▂

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
Screen 0 1 4 6 0 2 0
Liquid 0 1 3 4 0 2 0
Explore generating code using R 
library(tidyverse)
library(pins)
library(connectapi)

dasl_taste_test_scores <- read_csv('https://github.com/byuistats/data/raw/master/DASL-TasteTestScores/DASL-TasteTestScores.csv') %>% 
  select(!Description)


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

pin_name <- "dasl_taste_test_scores"
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_taste_test_scores.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_taste_test_scores/"
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_taste_test_scores")

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_taste_test_scores")
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_taste_test_scores")