NULL
Data details
There are 640 rows and 4 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/tb_dictionary
.
This data is available to all.
Variable summary
Variable type: character
skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
---|---|---|---|---|---|---|---|
variable_name | 0 | 1.00 | 2 | 32 | 0 | 640 | 0 |
dataset | 0 | 1.00 | 6 | 29 | 0 | 19 | 0 |
code_list | 566 | 0.12 | 11 | 235 | 0 | 27 | 0 |
definition | 0 | 1.00 | 3 | 339 | 0 | 639 | 0 |
Explore generating code using R
library(tidyverse)
library(pins)
library(connectapi)
tb_dictionary <- read_csv("https://extranet.who.int/tme/generateCSV.asp?ds=dictionary")
# Publish the data to the server with Bro. Hathaway as the owner.
board <- board_connect()
pin_write(board, tb_dictionary, type = "parquet")
pin_name <- "tb_dictionary"
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: tb_dictionary.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)
<- "https://posit.byui.edu/data/tb_dictionary/"
url_data <- board_connect_url(c("dat" = url_data))
board_url <- pin_read(board_url, "dat") 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):
= "https://posit.byui.edu/data/" + name + "/" + name + ".parquet"
url = requests.get(url)
response if response.status_code == 200:
= BytesIO(response.content)
parquet_content = pd.read_parquet(parquet_content)
pandas_dataframe return pandas_dataframe
else:
print(f"Failed to retrieve data. Status code: {response.status_code}")
return None
# Example usage:
= read_url_pin("tb_dictionary") pandas_df
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_connect(auth = "auto")
board <- pin_read(board, "hathawayj/tb_dictionary") dat
import os
from pins import board_rsconnect
from dotenv import load_dotenv
load_dotenv()= os.getenv('CONNECT_API_KEY')
API_KEY = os.getenv('CONNECT_SERVER')
SERVER
= board_rsconnect(server_url=SERVER, api_key=API_KEY)
board = board.pin_read("hathawayj/tb_dictionary") dat