Pine Beetle

Data details

There are 170 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/pine_beetle.

This data is available to all.

Variable description

• TreeStand: A unique key that identifies each treestand from which pines were observed
• Before: Number of lodgepole pines per hectare before a mountain pine beetle outbreak
• After: Number of lodgepole pines per hectare after a mountain pine beetle outbreak

Variable summary

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
TreeStand	0	1	85.50	49.22	1.0	43.25	85.50	127.75	170.0	▇▇▇▇▇
Before	0	1	1028.41	57.03	894.6	989.18	1029.85	1060.02	1195.9	▂▅▇▂▁
After	0	1	592.87	45.31	429.9	563.72	596.45	627.50	687.2	▁▂▆▇▃

NULL

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

pine_beetle <- read_csv('https://github.com/byuistats/data/raw/master/PineBeetle/PineBeetle.csv') %>% 
  select(!Notes) # Drop notes column because it contains description in qmd


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

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

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/pine_beetle")

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/pine_beetle")

Footnotes

1. Jennifer G. Klutsch, Jose F. Negron, Sheryl L. Costello, Charles C. Rhoades, Daniel R. West, John Popp, and Rick Caissie. Stand characteristics and downed woody debris accumulations associated with a mountain pine beetle (dendroctonus ponderosae hopkins) outbreak in colorado. Forest Ecology and Management, 258:641-649, 2009. Data extracted from summary statistics presented in Table 3 of the paper.