Climate is what we expect, weather (data) is what we get from APIs

Abstract No matter where we are, weather shapes our lives. We are all familiar with ordinary everyday weather concerns, do I need an umbrella when I step out the door today or maybe how cold will it be, will I need to wear a jacket later? Businesses use it to track long-term patterns and understand historical trends. Major media organisations use historical weather data to tell stories by visualising the data to show the effects of climate change to the public. Agricultural researchers use weather data in their analyses to help explain experimental results or build complex models that simulate farming systems. And governments use the data to prepare and plan for future disasters or understand seasonal trends to ensure adequate infrastructure is in place. While the uses are often critical, and the data may be freely or openly available, getting the data quickly and easily into R can be frustrating. There are 193 members of the World Meteorological Organisation (WMO), many of which offer some sort of programmatic access to historical weather data or forecasted weather data via APIs, but some do not, while there are other non-member organisations that do. I'll present the good, the bad and the ugly of different weather data sources and getting the data wrangled and tamed ready to go in your R session with what you need to think about for end users of the data when you make a weather data API client R package to help make our world more understandable.