Changes from version0.9.27, the most recent CRAN release, are:
Fix bug in getTimeUnit() method. This method would fail for "source_spct" objects containing spectral fluence data (a recent enhancement) and cause failure of the print() method for these same objects.
Add setHowMeasured() and getHowMeasured() methods, adding support for a new metadata attribute.
This update is in part the result of comparing the values returned by functions sun_angles() and day_night() to those returned by equivalent functions from other R packages and to the online NOAA Solar Calculator (considered the best implementation of Meuus astronomical algirithms, which are currently the most precise available). To see the whole story as it develops, please, have a look at my post describing these tests.
Changes from version0.9.26, the most recent CRAN release, are:
Improve performance of sun_angles() and day_night() and implement Meuus algorithm for julian day. End result is slightly slower performance but higher precision and broader range of dates that are handled correctly.
Make sure functions work correctly when a tibble is passed as argument to parameter geocode. In addition tolerate address column being a factor.
Values returned by sun_angles() and day_night() have changed very slightly as a result of the improved julian day calculation.
As far as I know there are in CRAN four R packages implementing the computations for the position of the sun and times of sunrise and sunset: ‘photobiology’, ‘fishmethods’, ‘solartime’ and ‘suncalc’.
The functions sun_angles() and day_night() from package ‘photobiology’ use Meeus equations as used by NOAA Solar Calculator https://www.esrl.noaa.gov/gmd/grad/solcalc/ which could be more precise than those in NOAA’s Excel worksheet which implement a simplified version of the Meeus equations especially for far into the past or far into the future calculations. The approximations based on Meuus equations are very good for years between 1800 and 2100 and results should still be sufficiently accurate for the range from -1000 to 3000 as long as the computation of Julian dates is correct. The Excel implementation is only valid for dates between 1901 and 2099 because of how Julian dates are computed in Excel.
Function astrocalc4r() from package ‘fishmethods’ also implements Meeus equations (the authors work at NOAA). Function computeSunPosition() from package ‘solartime’ uses unspecified equations and function getSunlightPosition() is an R interface to the ‘suncalc.js’ library, part of the ‘SunCalc.net’ project <http://suncalc.net>.
Function computeSunPosition() from package ‘solartime’ uses unspecified equations and function getSunlightPosition() is an R interface to the ‘suncalc.js’ library, part of the ‘SunCalc.net’ project <http://suncalc.net>.
UPDATED on 2019-04-24
I have noticed significant differences in the values returned by equivalent functions from different packages. Up to now the tests on the functions of my own package ‘photobiology’ have revealed only very small mismatches to the NOAA Solar Calculator. These small errors, noticeable for dates far from the present, were due to the use of base R’s julian() function, which is not designed to be precise enough for astronomical calculations. The code now in the repository at Bitbucket has been revised to use Meuus’ algorithm for the calculation of Julian days removing this source of small discrepancies.
In contrast, while testing ‘photobiology’ against other packages, I seem to have found a bug in function astrocalc4r() from R package ‘fishmethods’.
A minimal example follows:
By only changing the hour passed as argument different times for sunrise, sunset and daylength are returned even though the day is the same. The differences are larger at high than at low latitudes. The maximum difference for the example above is 1/4 h for daylength. Comparison against the NOAA Solar Calculator shows even larger differences.
(The bug has been reported to the maintainer of package ‘fishmethods’.)
After the previous major update to ‘ggpmisc’ (0.3.0), a follow up (0.3.1) with multiple new features and smoothed rough edges for some of the features added in version 3.0.0. Package documentation is available at https://docs.r4photobiology.info/ggpmisc/ as a web site.
The enhancements in this and the previous update to ‘ggpmisc’ are made possible by changes to ‘ggplot2’ (>= 3.0.0) made while adding support for sf (simple features).
The functions in ‘ggpmisc’ (>= 0.3.1) are related to different kinds of annotations and insets in ggplots. Annotations of plots with fitted-model equations, fit diagnosis, ANOVA and summary tables, highlighting and labelling of peaks and valleys in curves, and local density-based highlighting or labelling in scatter plots. Additionally specializations of the ggplot() constructor allow on-the-fly conversion of time-series. Two new geoms join geom_table() added in version 0.3.0, geom_plot() and geom_grob(). These three geoms make it possible to add tables, ggplots and arbitrary graphical objects (grobs) as insets to plots respecting the the Grammar of Graphics paradigm.
A set of new geometries produce marginal annotations: geom_x_margin_point(), geom_y_margin_point(), geom_x_margin_arrow(), geom_y_margin_arrow(), geom_x_margin_grob() and geom_y_margin_grob().
Another novel feature is based on the addition of two new aesthetics npcx and npcy, the corresponding scales scale_npcx_continuous() and scale_npcy_continuous(), and several new geometries that make use of then: geom_text_npc(), geom_label_npc(), geom_table_npc(), geom_plot_npc() and geom_grob_npc(). These allow to position insets and annotations relative to the dimensions of the plotting area instead of using native data units. Using "npc" units is more natural for labels or insets that are not directly related to data but that look better if positioned consistently across multiple panels or multiple separately produced plots. One improvement to the stats from earlier versions of ‘ggpmisc’ is the use of these new geoms to achieve more consistent location for insets and labels.
As in the announcement of the previous version, I have included some example plots taken from the documentation of the package. In all cases annotations are generated automatically, but formatting is flexible.
NOTE: The new version of ‘ggpmisc’ will be soon submitted to CRAN .