Variable type: numeric
skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
---|---|---|---|---|---|---|---|---|---|---|
Count | 0 | 1 | 38.93 | 23.11 | 9 | 21.25 | 31 | 60.5 | 89 | ▇▃▂▃▂ |
MATH 221
April 25, 2024
There are 30 rows and 3 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/book_of_mormon_wordprint
.
This data is available to all.
Variable type: numeric
skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
---|---|---|---|---|---|---|---|---|---|---|
Count | 0 | 1 | 38.93 | 23.11 | 9 | 21.25 | 31 | 60.5 | 89 | ▇▃▂▃▂ |
Variable type: character
skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
---|---|---|---|---|---|---|---|
Author | 0 | 1 | 4 | 6 | 0 | 5 | 0 |
Word | 0 | 1 | 2 | 4 | 0 | 6 | 0 |
library(tidyverse)
library(pins)
library(connectapi)
book_of_mormon_wordprint <- read_csv('https://github.com/byuistats/data/raw/master/BookOfMormonWordprint/BookOfMormonWordprint.csv')
# Publish the data to the server with Bro. Hathaway as the owner.
board <- board_connect()
pin_write(board, book_of_mormon_wordprint, type = "parquet", access_type = "all")
pin_name <- "book_of_mormon_wordprint"
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))
This data is available to all.
Direct Download: book_of_mormon_wordprint.parquet
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/book_of_mormon_wordprint/"
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("book_of_mormon_wordprint")
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.