Weeks 5-6

[bpbond]

At this point we have a big data frame (6K rows?) with weekly Q10 values, for Rh, by dataset, year, week, day/night, and soil temperature depth.

Next steps:

• @bpbond needs to give a feedback PR
• We'd like to add a few Q10 calculation statistics - R2, P, and N
• Also calculate for Rs but only for datasets with Rh - this will let us make nice comparison. So now there will be a column in the output dataset saying whether it's Rh or Rs
• Effective visualization of these data - let's dive into this around August 10th - with @jinshijian and @stephpenn1

QA/QC thoughts:

• summary() for the entire dataset
• summary statistics for the entire dataset

[bpbond]

A lot of the R2 values are very low, notes @10aDing . This is somewhat expected, but may also be a function of the time window we're using.

• [x] Try re-running with a monthly window (using lubridate::month()) and make histogram etc. of R2 and Q10 values
• [x] Histogram of R2, Q10, for each dataset, coloring by day/night
• [x] geom_tile() with month on x axis and dataset/year on y, fill color is Q10
• [ ] line graphs of Q10, month on x, color of line shows day/night and Rs/Rh
• [x] We would also like to add MAT and MAP (mean annual temp and precipitation) for each site. This would involve pulling from the worldclim2 dataset. @10aDing consider whether you'd like to do write this from scratch or, we would suggest, take Max/Stephanie's code, understand it, and implement in your Rmd
• [ ] You will want to assemble a list of latitude and longitude values, one row per dataset, and then pulling data from worldclim -> data frame with Dataset, Lat, Lon, MAT, MAP. Then you will use a left_join() to add it to your dataset

We may also, if time allows, want to pull out monthly data from worldclim!