Table of Information about Marathons

Data details

There are 6,888 rows and 5 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_info.

This data is available to all.

Variable description

• year: The year of the marathon
• marathon: The name of the marathon
• country: The country where the marathon occurred
• finishers: The number of finishers at the marathon
• mean_time: The average finish time in minutes

Variable summary

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
year	0	1	2007.53	4.73	1970.00	2005.00	2008	2011.0	2013.0	▁▁▁▂▇
finishers	0	1	1421.21	4166.05	1.00	87.75	224	842.5	50062.0	▇▁▁▁▁
mean_time	0	1	270.47	35.52	139.07	250.85	263	280.0	614.5	▁▇▁▁▁

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
marathon	0	1	6	64	0	1176	0
country	0	1	2	14	0	43	0

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

# Data origin. This works.
race_info <- read_html("https://faculty.chicagobooth.edu/george.wu/research/marathon/marathon_names.htm") %>%
  html_nodes("table") %>%
  html_table() %>%
  .[[1]]

# Wrangle
colnames(race_info) <- race_info[1,]

race_info <- race_info[-1,] %>%
  as_tibble() %>%
  rename_all("str_to_lower") %>%
  mutate(year = as.integer(year), finishers = as.integer(finishers),
         `mean time` = as.numeric(`mean time`)) %>%
  rename(mean_time = `mean time`)

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

pin_name <- "race_info"
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_info.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_info/"
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_info")

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_info")

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_info")

Footnotes

1. https://faculty.chicagobooth.edu/george.wu/research/marathon/marathon_names.htm