Commercial Building Details

Data details

There are 1,248 rows and 18 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/buildings0809.

This data is available to all.

Variable description

• Year: Year
• Month: Month
• Census.FIPS: Census FIPS code
• FIPS.county: County FIPS code
• FIPS.state: State FIPS code
• ProjectTitle: Project title
• Address: Address
• City: City
• State: State
• Zipcode: Zip code
• County: County
• Type: Type
• Ownership: Ownership
• Strys: Number of stories
• SqFt: Square footage
• Value1000: Value in thousands
• Dwellings: Number of dwellings
• sizegroup: Size group

Variable summary

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
FIPS.county	0	1	26.57	28.47	1	1	17.0	55	87	▇▂▁▃▂
FIPS.state	0	1	16.00	0.00	16	16	16.0	16	16	▁▁▇▁▁
Strys	0	1	0.68	0.84	0	0	1.0	1	8	▇▁▁▁▁
SqFt	0	1	10227.88	32256.71	0	0	1300.0	7300	700000	▇▁▁▁▁
Value1000	0	1	1403.46	5600.58	50	150	338.5	975	150000	▇▁▁▁▁
Dwellings	0	1	0.99	9.09	0	0	0.0	0	180	▇▁▁▁▁

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
Year	0	1	4	4	0	2	0
Month	0	1	2	2	0	12	0
Census.FIPS	0	1	5	5	0	39	0
ProjectTitle	0	1	4	60	0	901	0
Address	0	1	3	30	0	1128	0
City	0	1	4	19	0	101	0
State	0	1	2	2	0	1	0
Zipcode	0	1	0	5	51	130	0
County	0	1	7	14	0	39	0
Type	0	1	6	39	0	48	0
Ownership	0	1	5	7	0	4	0
sizegroup	0	1	5	7	0	5	0

devtools::install_github("hathawayj/buildings")
library(buildings)

pacman::p_load(tidyverse, fs, sf, arrow, googledrive, downloader, fs, glue, rvest, pins, connectapi)

board <- board_connect()

buildings0809 <- buildings::buildings0809
pin_write(board, buildings0809, type = "parquet", access_type = "all")
pin_name <- "buildings0809"
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: buildings0809.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/buildings0809/"
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("buildings0809")

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/buildings0809")

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/buildings0809")

Footnotes

1. https://github.com/hathawayj/buildings