The 50% sample of male/female runners for all years of the Berlin marathon that recorded gender.

Data details

There are 226,713 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_berlin.

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	35934	0.84	41.05	9.86	6.00	34.00	41.00	48.00	86.0	▁▆▇▂▁
split_half	226713	0.00	NaN	NA	NA	NA	NA	NA	NA
clocktime	39	1.00	253.82	44.35	123.38	222.10	250.62	282.50	466.4	▁▇▆▁▁
chiptime	0	1.00	246.68	40.84	123.38	217.77	242.73	272.63	466.4	▁▇▅▁▁
year	0	1.00	2006.64	4.18	1999.00	2003.00	2007.00	2010.00	2013.0	▅▆▇▇▇
split_10k	226713	0.00	NaN	NA	NA	NA	NA	NA	NA
split_30k	226713	0.00	NaN	NA	NA	NA	NA	NA	NA
split_40k	226713	0.00	NaN	NA	NA	NA	NA	NA	NA
finishers	0	1.00	31030.40	4550.66	19128.00	28022.00	32486.00	34375.00	36543.0	▁▃▁▇▇
meantime	0	1.00	246.66	4.54	235.22	245.31	246.50	249.15	256.2	▁▃▇▂▂
female	0	1.00	0.24	0.43	0.00	0.00	0.00	0.00	1.0	▇▁▁▁▂
us	0	1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.0	▁▁▇▁▁
canada	0	1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.0	▁▁▇▁▁
europe	0	1.00	1.00	0.00	1.00	1.00	1.00	1.00	1.0	▁▁▇▁▁
other	0	1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.0	▁▁▇▁▁
age_gender	0	1.00	0.84	0.37	0.00	1.00	1.00	1.00	1.0	▂▁▁▁▇

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
gender	0	1	1	1	0	2	0
marathon	0	1	15	15	0	1	0
country	0	1	7	7	0	1	0
marathon2	0	1	20	20	0	15	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)




# Wrangling for Berlin marathon data
marathon_berlin <- dat %>%
  filter(marathon == "Berlin Marathon", !is.na(gender)) %>%
  group_by(year, gender) %>%
  sample_frac(size = .5) %>%
  ungroup()




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

pin_name <- "marathon_berlin"
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_berlin.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_berlin/"
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_berlin")

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_berlin")

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_berlin")

Footnotes

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