Climate Change: Ocean

Ocean heat content is measured relative to the 1971–2000 average, which is set at zero for reference. It is measured in 10²² joules. For reference, 10²² joules are equal to approximately 17 times the amount of energy used globally every year.
DS 350
world
climate
Author

DS 350

Published

May 8, 2024

Data details

There are 66 rows and 7 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/climate_change_ocean.

This data is available to all.

Variable description

  • Entity: Country name
  • Code: 3-letter code for each country
  • Year: Year
  • 700m_ocean_heat_content_iap: Ocean heat content found by the Chinese Academy of Sciences’ institute of Atmospheric Physics (IAP) in 1022 joules
  • 700m_ocean_heat_content_noaa: Ocean heat content found by the U.S. National Oceanic and Atmospheric Administration (NOAA) in 1022 joules
  • 700m_ocean_heat_content_csiro: Ocean heat content found by the Australia’s Commonwealth Scientific and Industrial Research Organisation (CSIRO) in 1022 joules
  • 700m_ocean_heat_content_mrijma: Ocean heat content found by the Japan Meteorological Agency’s Meteorological Research Institute (MRI/JMA) in 1022 joules

Variable summary

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
Year 0 1.00 1987.50 19.20 1955.00 1971.25 1987.50 2003.75 2020.00 ▇▇▇▇▇
700m_ocean_heat_content_iap 0 1.00 2.86 7.71 -7.57 -2.92 -0.28 9.67 20.01 ▇▇▂▃▃
700m_ocean_heat_content_noaa 0 1.00 2.65 6.64 -5.96 -2.53 0.14 8.98 17.49 ▇▆▂▃▂
700m_ocean_heat_content_csiro 10 0.85 1.96 6.21 -7.52 -2.78 1.42 6.24 15.88 ▇▇▇▃▃
700m_ocean_heat_content_mrijma 0 1.00 2.11 7.32 -9.50 -3.61 0.63 8.69 18.08 ▆▇▃▃▃

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
Entity 0 1 5 5 0 1 0
Code 0 1 8 8 0 1 0
Explore generating code using R 
pacman::p_load(tidyverse, pins, connectapi, googledrive, owidR)

# pwid() doesn't work for this dataset right now because the source site is down 5/8/2024

# # owid() function downloads current data directly from Our World in Data.
# # Use owid_search() to search for other OWID datasets.
# # For more information, see the package documentation here: https://github.com/piersyork/owidR/blob/main/README.md
# climate_change_ocean <- owid('climate-change-ocean')

# Until that issue is resolved, the data can be found in the google drive
# Download the file from google drive
sdrive <- shared_drive_find("byuids_data") # This will ask for authentication.
google_file <- drive_ls(sdrive) |>
  filter(stringr::str_detect(name, "climate-change-ocean"))
tempf <- tempfile()
drive_download(google_file, tempf)
climate_change_ocean <- read_csv(tempf)

# Publish the data to the server with Bro. Hathaway as the owner.
board <- board_connect()
pin_write(board, climate_change_ocean, type = "parquet", access_type = "all")

pin_name <- "climate_change_ocean"
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: climate_change_ocean.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/climate_change_ocean/"
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("climate_change_ocean")

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/climate_change_ocean")
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/climate_change_ocean")