The full set of runners for the Jerusalem marathon.

Data details

There are 2,438 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_jerusalem.

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	786	0.68	43.65	11.51	18.00	36.00	44.00	52.00	76.00	▃▆▇▅▁
split_half	1611	0.34	112.78	14.99	68.62	103.58	111.38	121.94	189.10	▁▇▅▁▁
clocktime	1600	0.34	255.81	39.17	134.68	231.41	255.25	280.72	394.18	▁▅▇▃▁
chiptime	0	1.00	258.11	41.31	134.45	230.48	256.00	283.42	410.03	▁▆▇▂▁
year	0	1.00	2011.98	0.82	2011.00	2011.00	2012.00	2013.00	2013.00	▇▁▇▁▇
split_10k	864	0.65	58.43	8.79	34.72	53.20	57.57	62.88	150.37	▇▇▁▁▁
split_30k	73	0.97	177.42	26.27	96.78	159.73	176.37	193.78	273.48	▁▆▇▂▁
split_40k	76	0.97	243.69	36.85	128.97	218.78	242.22	266.74	349.32	▁▃▇▅▁
finishers	0	1.00	814.25	22.44	786.00	786.00	814.00	841.00	841.00	▇▁▇▁▇
meantime	0	1.00	257.90	3.05	253.79	253.79	259.24	260.88	260.88	▇▁▁▇▇
female	2438	0.00	NaN	NA	NA	NA	NA	NA	NA
us	0	1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	▁▁▇▁▁
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	1.00	0.00	1.00	1.00	1.00	1.00	1.00	▁▁▇▁▁
age_gender	0	1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	▁▁▇▁▁

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
gender	2438	0	NA	NA	0	0	0
marathon	0	1	18	18	0	1	0
country	0	1	6	6	0	1	0
marathon2	0	1	23	23	0	3	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 for the Jerusalem Marathon only
marathon_jerusalem <- dat %>%
  filter(marathon == "Jerusalem Marathon")




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

pin_name <- "marathon_jerusalem"
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_jerusalem.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_jerusalem/"
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_jerusalem")

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_jerusalem")

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_jerusalem")

Footnotes

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