Race Location

Data details

There are 1,925 rows and 12 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/race_location.

This data is available to all.

Variable description

• marathon The name of the marathon that matches all other files
• marathon_name A cleaned name of the marathon
• state_id The two letter ID for each US state
• city The name of the city where the race is held
• finishers The number of finishers at the marathon
• mean_time The average finish time in minutes
• lat The lattitude of the city as listed at https://simplemaps.com/data/us-cities
• lng The longitude of the city as listed at https://simplemaps.com/data/us-cities
• elevation_m The elevation in meters above sea level as estimated from the elevatr R package
• date The approximate date of the marathon. The year is correct but the month and day changes every year and we have marked it the same.
• month Approximate month of the marathon
• day Approximate day of the month of the marathon
• year The year of the marathon

Variable summary

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
year	0	1	2007.95	4.16	1983.00	2005.00	2009.00	2011.00	2013.00	▁▁▁▅▇
month	0	1	6.86	3.10	1.00	4.00	7.00	10.00	12.00	▅▆▅▆▇
day	0	1	14.11	8.93	1.00	6.00	14.00	21.00	31.00	▇▅▆▅▃
finishers	0	1	908.29	2635.80	8.00	96.00	193.00	468.00	23879.00	▇▁▁▁▁
mean_time	0	1	275.71	32.81	139.07	256.36	267.71	285.24	438.89	▁▅▇▁▁
lat	0	1	39.55	5.64	19.69	36.21	39.47	42.90	64.84	▁▅▇▁▁
lng	0	1	-99.17	18.62	-159.46	-118.08	-96.05	-82.43	-71.09	▁▁▇▅▇
elevation_m	0	1	563.27	712.34	-796.00	103.00	272.00	704.00	3409.00	▂▇▂▁▁

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
marathon	0	1	8	62	0	258	0
country	0	1	2	2	0	1	0
state	0	1	2	2	0	47	0
city	0	1	3	18	0	229	0

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

# Download the file from google drive
sdrive <- shared_drive_find("byuids_data") # This will ask for authentication.
google_file <- drive_ls(sdrive) |>
  filter(stringr::str_detect(name, "race_location.csv"))
tempf <- tempfile()
drive_download(google_file, tempf)
race_location <-  read_csv(tempf)


board <- board_connect()
pin_write(board, race_location, type = "parquet")

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

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/race_location")

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/race_location")

Footnotes

1. http://faculty.chicagobooth.edu/george.wu/research/marathon/data.htm and https://simplemaps.com/data/us-cities