Rewrite README and style code

[peterdesmet]

This PR includes updates to prepare for rOpenSci onboarding (#148). The main changes are:

• Run styler package over the code to clean up whitespace etc.: this is why most R files are updated
• Add codemeta.json (for rOpenSci)
• Add URLs for authors (website for @adokter, twitter for @peterdesmet and @stijnvanhoey)
• Update intro to package in bioRad.R and DESCRIPTION (as discussed in Review README #138)
• Generate README.md from README.Rmd (allows to have code examples)
• Rewrite the README

✨New README ✨

The new README now has:

• Intro
• Installation
• Usage
• Meta (was already there)

All elements of the original README are included in one way or another, except:

• Mention of Flysafe example : http://www.flysafe-birdtam.eu/profile.php?radar=herwijnen
• Mention of ggmap2
• rhdf5: no longer needed Add biocViews to automatically look for rhdf5 #151
• Special installation notes: see Installation notes #154 to discuss this
• ?bioRad

What has been added:

• Docker instructions are collapsed by default
• Usage example for radar data
  • Where you can find open NEXRAD data (including AWS)
  • Plot of VRADH + explain a bit
• Usage example for vp data
  • How these were created
  • Where to find open ENRAM vp data
  • That vp data can be bound into time series
  • Plot of time series + explain gray bars
  • Show how to calculate number of birds
  • Link to https://adokter.github.io/bioRad/articles/functionality_overview.html for further examples (can be updated later)
• All examples use easier to read (dplyr) pipe format

@adokter @CeciliaNilsson709 can you review the README to see if it is clear and that especially for the examples I'm not saying something incorrect? 😅

[adokter]

Not sure I added comments/review to this PR properly, but the bottom line:

In README.md:

• I made a few edits to the example of height-integrated time series. I want to avoid people averaging mtr's. Instead they should use the time-integrated column mt (accounts properly for the potentially varying time intervals in the time series)
• Thought it would be nice to also include a plot of the height-integrated time series. Added it to the readme, but not yet the figure
• regularize_vpts no longer essential, though without it you do not see the gray bars without data
• should we really use tidyverse piping in the examples? I haven't tested yet whether all package functions are compatible

[adokter]

#128 documents another windows-specific issue that some users ran into, which would be good to document together with other known Docker issues

[peterdesmet]

Will have a look. 👌 Should have let you know to edit the README.Rmd rather that the automatically created README.md.

[peterdesmet]

@adokter

• Rewrote vpi example a bit, explaining it is a dataframe
• Mentioned as comment that integrate_profile also regularizes it (at least, that's how I understood your comment)
• Included plot (I had it in a first draft, but it makes sense to include it)
• Yes, I think we should include piping even though it hasn't been fully tested. It's a concept that makes code much more readable, so it's good to have it in the examples (and people might learn from it).

I'll include the installation gotchas in #154 and #128 (which still need a summary) in a later PR if that's ok.

[adokter]

Mentioned as comment that integrate_profile also regularizes it (at least, that's how I understood your comment)

Sorry I wasn't clear. Without regularizing you don't see data gaps in the plot, it just fills the area between consecutive profiles, even though they may be widely spaced in time during radar downtime. Often that's fine, especially if there is no missing data.

Regularizing has the benefit that you specify at which interval you expect profiles, so I can replace periods with missing data with empty profiles, that show up as gray.

[peterdesmet]

@adokter So, without regularize a data point in the plot is carried through (straight line) until the chart continues? While with regularize, datetimes are added, resulting in more rows (and actual gaps NA in the plot)?

> example_vpts %>% integrate_profile() %>% nrow()
[1] 1934
> example_vpts %>% regularize_vpts %>% integrate_profile() %>% nrow()
projecting on 300 seconds interval grid...
[1] 2735

If yes, than I think it is good to still include regularize_vpts in the first example (to see the gray bars). Also, not using it gives a warning when plotting.

Not sure what we should say about or include it in the vpi plot... but my incorrect comment needs to be updated:

Note: integrated profiles are regularized automatically

[adokter]

@adokter So, without regularize a data point in the plot is carried through (straight line) until the chart continues? While with regularize, datetimes are added, resulting in more rows (and actual gaps NA in the plot)?

Yes

[adokter]

@peterdesmet should I merge this PR?

[peterdesmet]

I removed my incorrect remark: PR can be merged now. 👌

Might be good to give some more explanation in the regularize_vpts function documentation, explaining its effect on plots.

[adokter]

Had to make a few additional commits to master, since some commits entered the develop branch instead of this PR request. Also and ran devtools check and rebuilt pkgdown site. Consider 7451eb1 the end of this PR.