Climate Zone FIPS

Data details

There are 3,221 rows and 19 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/climate_zone_fips.

This data is available to all.

Variable description

• state: State FIPS code
• StateAbbr: State abbreviation
• county: County FIPS code
• countyname: County name
• variable: Variable name
• year: Year
• value: Value

Variable summary

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
FIPS.state	0	1	31.31	16.27	1.00	19.00	30.00	46.00	72.00	▅▇▆▆▁
Region	0	1	2.82	1.26	1.00	2.00	3.00	3.00	9.00	▆▇▁▁▁
Division	0	1	5.07	2.10	0.00	4.00	5.00	7.00	9.00	▁▃▇▆▃
FIPS.county	0	1	103.45	108.17	1.00	35.00	79.00	133.00	900.00	▇▁▁▁▁
LandSQMI	0	1	1276.88	10676.85	1.99	419.90	606.11	916.61	571951.26	▇▁▁▁▁
AreaSQMI	0	1	1385.50	12264.33	1.99	434.88	640.15	974.49	663267.26	▇▁▁▁▁
Pop100	0	1	88748.06	289178.19	54.86	11400.00	25277.00	61382.00	9519338.00	▇▁▁▁▁
HU100	0	1	36505.47	109493.49	13.84	5188.00	11111.00	26544.00	3270909.00	▇▁▁▁▁
IntPtLat	0	1	37.96	6.08	17.98	34.35	38.22	41.69	70.60	▁▇▆▁▁
IntPtLon	0	1	-91.65	13.40	-172.45	-98.06	-89.96	-82.96	-65.29	▁▁▂▇▆
doe_climate_zone	0	1	4.20	1.38	1.00	3.00	4.00	5.00	8.00	▂▃▇▂▁
FIPS.long	0	1	31415.47	16288.64	1001.00	19035.00	30027.00	46111.00	72153.00	▅▇▆▆▁

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
State_name	0	1	4	20	0	52	0
State_abb	0	1	2	2	0	52	0
AreaName	0	1	6	43	0	1954	0
County	0	1	5	41	0	3221	0
doe_moisture_regime	0	1	0	6	130	4	0
is_warm_humid	0	1	1	1	0	2	0
merged	0	1	5	39	0	3221	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()


climate_zone_fips <- buildings::climate_zone_fips

climate_zone_fips <- climate_zone_fips |>
  mutate(across(where(is.character),
                ~ iconv(.x, from = "", to = "UTF-8", sub = "byte")))

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

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/climate_zone_fips")

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/climate_zone_fips")

Footnotes

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