Manatees

Data details

There are 35 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/manatees.

This data is available to all.

Variable description

• Fiscal.Year Year
• Power.Boats..in.1000.s. Powerboat registrations in Florida in thousands
• Manatees Count of manatees killed by powerboats

Variable summary

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
Fiscal.Year	0	1	1994.00	10.25	1977	1985.5	1994	2002.50	2011.00	▇▇▇▇▇
Power.Boats..in.1000.s.	0	1	738.16	190.04	436	589.5	703	933.65	1017.94	▆▃▆▂▇
Manatees	0	1	52.80	26.18	14	30.5	50	75.50	97.00	▇▅▆▆▅

NULL

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

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


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

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

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/manatees")

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/manatees")

Footnotes

1. Brigitte Baldi and David S. Moore. The Practice of Statistics in the Life Sciences, pages 67-68. W. H. Freeman and Company, New York, NY, 2009