TidyTuesday - Weather Forecast Accuracy - Week 51

The @R4DScommunity welcomes you to week 51 of #TidyTuesday!

Weather Forecast Accuracy

The data this week comes from the USA National Weather Service, as emailed by the University of Illinois list server and processed by Sai Shreyas Bhavanasi, Harrison Lanier, Lauren Schmiedeler, and Clayton Strauch at the Saint Louis University Department of Mathematics and Statistics. Thank you to Darrin Speegle for bringing the data to our attention!

The goal of this data science capstone project has been to acquire national weather data to learn which areas of the U.S. struggle with weather prediction and the possible reasons why. Specifically, we focused on the error in high and low temperature forecasting.

The data includes 16 months of forecasts and observations from 167 cities, as well as a separate data.frame of information about those cities and some other American cities.

Get the data here

# Get the Data

# Read in with tidytuesdayR package 
# Install from CRAN via: install.packages("tidytuesdayR")
# This loads the readme and all the datasets for the week of interest

# Either ISO-8601 date or year/week works!

tuesdata <- tidytuesdayR::tt_load('2022-12-20')
tuesdata <- tidytuesdayR::tt_load(2022, week = 51)

weather_forecasts <- tuesdata$weather_forecasts
cities <- tuesdata$cities
outlook_meanings <- tuesdata$outlook_meanings

# Or read in the data manually

weather_forecasts <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-12-20/weather_forecasts.csv')
cities <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-12-20/cities.csv')
outlook_meanings <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-12-20/outlook_meanings.csv')

Learn more on the TidyTuesday Repo at tidytuesday/data/2022/2022-12-20 at master · rfordatascience/tidytuesday · GitHub

And here's the original dataset:

New to TidyTuesday?

Welcome to the weekly social data project in R. All are welcome!
TidyTuesday Guide - bit.ly/tidyreadme.
⬡ The event is organized by the R4DS Online Learning Community, join the slack community.
⬡ For the latest datasets, follow R4DS on Twitter , Mastodon, LinkedIn, or Posit Community #TidyTuesday (click the :bell: icon and choose Watching First Post).

Where should I share?

⬡ On Mastodon at #TidyTuesday.
⬡ On LinkedIn with the #TidyTuesday hashtag.
⬡ Reply in the Posit Community Post, below.
⬡ On twitter, with the hashtag #TidyTuesday

1 Like

This topic was automatically closed 360 days after the last reply. New replies are no longer allowed.

If you have a query related to it or one of the replies, start a new topic and refer back with a link.