The full set of runners for the Big Sur marathon.

Data details

There are 39,009 rows and 20 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/marathon_big_sur.

This data is available to all.

Variable description

• age The age of the runner
• gender The gender of the runner (M/F)
• chiptime The time in minutes for the runner
• year The year of the marathon
• marathon The name of the marathon
• country The country where the marathon was held
• finishers The number of finishers at the marathon

Variable summary

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
age	3100	0.92	40.16	10.57	9.00	32.00	40.00	48.00	80.00	▁▇▇▃▁
split_half	37434	0.04	117.32	12.09	73.17	109.13	118.45	126.77	159.72	▁▃▇▅▁
clocktime	11546	0.70	271.99	42.12	141.72	240.38	270.20	301.57	407.87	▁▅▇▅▁
chiptime	0	1.00	266.89	39.79	138.08	237.55	265.57	295.22	406.73	▁▅▇▃▁
year	0	1.00	2006.68	3.88	2000.00	2003.00	2007.00	2010.00	2013.00	▆▇▆▇▇
split_10k	39009	0.00	NaN	NA	NA	NA	NA	NA	NA
split_30k	39009	0.00	NaN	NA	NA	NA	NA	NA	NA
split_40k	39009	0.00	NaN	NA	NA	NA	NA	NA	NA
finishers	0	1.00	2866.06	437.48	1654.00	2517.00	2877.00	3246.00	3452.00	▁▁▇▇▇
meantime	0	1.00	266.89	7.82	239.18	263.94	265.99	274.12	276.10	▁▁▂▇▇
female	1	1.00	0.41	0.49	0.00	0.00	0.00	1.00	1.00	▇▁▁▁▆
us	0	1.00	1.00	0.00	1.00	1.00	1.00	1.00	1.00	▁▁▇▁▁
canada	0	1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	▁▁▇▁▁
europe	0	1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	▁▁▇▁▁
other	0	1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	▁▁▇▁▁
age_gender	0	1.00	0.92	0.27	0.00	1.00	1.00	1.00	1.00	▁▁▁▁▇

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
gender	1	1	1	1	0	2	0
marathon	0	1	16	16	0	1	0
country	0	1	2	2	0	1	0
marathon2	0	1	21	21	0	14	0

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


# Data is from master_marathon. 
sdrive <- shared_drive_find("byuids_data") # This will ask for authentication.
google_file <- drive_ls(sdrive)  |>
  filter(stringr::str_detect(name, "master_marathon"))
tempf <- tempfile()
drive_download(google_file, tempf)
dat <- read_csv(tempf)



# Filter to only the Big Sur Marathon
marathon_big_sur <- dat %>%
  filter(marathon == "Big Sur Marathon")



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

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

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/marathon_big_sur")

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/marathon_big_sur")

Footnotes

1. http://faculty.chicagobooth.edu/george.wu/research/marathon/data.htm