Climacell has changed its name to Tomorrow.io. Will need to update documentation to highlight this change.

NOTE: Package name will not change as it will affect CRAN (suggestions for alternatives welcome from community).

Thanks for the great library!
While everything else seemed to work fine, the climacell_precip function seems to return incorrect data points when used with sampling intervals of less than 1d. For example, when I use the following code:

climacell_precip(api_key = Sys.getenv("CLIMACELL_API"), lat = 41.878685, long = -87.636011, timestep = '1h', start_time = Sys.time(), end_time = Sys.time() + lubridate::days(3))

I get the following result:

_

A tibble: 5 x 13

start_time precipitation_i… precipitation_p… precipitation_t… precipitation_t…

1 2021-02-21 12:00:00 4.1 95 2 Snow
2 2021-02-22 12:00:00 0 0 1 Rain
3 2021-02-23 12:00:00 0 0 1 Rain
4 2021-02-24 12:00:00 0 0 2 Snow
5 2021-02-25 12:00:00 0 0 2 Snow

_

Which looks quite similar to what you would get if you used a timestep of 1d. Can you please take a look?

Many thanks!