# User Guide: 2 Autoplot Methods

## Introduction

Package ggspectra extends ggplot2 with stats, geoms and annotations suitable for light spectra. It also defines ggplot() and autoplot() methods specialized for the classes defined in package photobiology for storing different types of spectral data. This vignette describes the use of these autoplot() methods.

The package uses ‘ggplot2’, ‘photobiology’ and ‘photobiologyWavebands’. More importantly it defines specializations of methods defined in ‘ggplot2’ and exports methods epecialized for classes defined in package ‘photobiology’, consequently both ‘ggplot2’ and ‘photobiology’ are loaded automatically when package ‘ggspectra’ is loaded. This is the behaviour from version 0.9.26 onwards, when using earlier versions these packages needed to be loaded explicitly.

library(dplyr)
library(photobiologyWavebands)
library(ggspectra)

good_label_repel <- packageVersion('ggrepel') != "0.8.0" #||
#  packageVersion('ggplot2') >= "3.1.0"

We bind two spectra into an object to be used later to demonstrate grouping.

two_suns.spct <- rbindspct(list(sun1 = sun.spct, sun2 = sun.spct * 2))

We change the default theme.

theme_set(theme_bw(10))

## Plotting spectra

The most automatic way of plotting spectral data stored in one of the classes defined in package photobiology is to use the autoplot() methods (for compatibility with versions < 0.9.25 plot() remains as an additional name for the same methods). As autoplot() methods defined by ‘ggplot2’ they take advantage of all the data and metadata stored in objects of the special classes used to store spectra. This allows the automatic construction of axis labels as quantities and units are well defined.

Here we use the same example source_spct object from package ‘photobiology’ used in the User Guide 1. In contrast to the examples in the User Guide 1, here we obtain with a very simple statement a complete annotated plot of the solar spectrum at ground level.

autoplot(sun.spct)

We can change the basis of expression of spectral irradiance and irradiance from "energy" to "photon". Be aware that by design, all values, summaries and spectral data will always use the same base of expression.

autoplot(sun.spct, unit.out = "photon")

If a single object contains multiple spectra in long form, they are all plotted. In addition no summary values are displayed as they would overlap.

autoplot(two_suns.spct)

Although the defaults provide a useful plot, the autoplot() methods accepts arguments for several parameters that allow to tweak the looks and contents of the plots without explicit manipulation of the spectral data.

### Summary quantities in labels

In this first example we pass "mean" as argument to label.qty, to print means instead of integrals in the labels. Note that the units and quantity labels for the waveband summaries have also changed.

autoplot(sun.spct, label.qty = "mean")

Two label.qty values need explanation. The first one, "relative" displays in labels the relative contribution of the integral of each waveband, to the sum of the integrals of all wavebands. The second one, "contribution" displays in labels the integral of each waveband divided by the integral of the whole spectrum. Consequently, this second option should be interpreted with caution, as the spectral data is unlikely in many cases to include the whole emission or absorption spectrum of a source. Using "relative.pc" or "contribution.pc" results in the corresponding values being expressed as percentages.

autoplot(sun.spct, label.qty = "contribution")

autoplot(sun.spct, label.qty = "relative")

### Automatic annotations

Which annotations are included can be controlled through parameter annotations of the autoplot() methods, taking a vector of character strings as argument. Three values have special meaning if at the head of the vector (with index = 1): "=" means override the current defaults with the annotations that follow in the vector, "+" means add to the current default the annotations that follow, and "-" means remove from the current default the annotations that follow. A NULL value means use package-defined defaults as is, "" means no annotations, and "reserve.space" means no annotations, but expand axis limits and set identity scales ready for manually adding annotations. Used together with "-", "title*", "peaks*" and "valleys*" are wildcards that remove all flavours of each of these annotations. In cases when the intention is to both add and remove annotations from the default, the argument to parameter annotations can be a list of character vectors, which are interpreted as above but operated upon sequentially.

Which annotations are included by default can be changed by setting R option "photobiology.plot.annotations" to a character vector with the names of the desired default annotations. Function set_annotations_default() allows to set this option using the same syntax as described for parameter annotations.

annotation default for classes overrides
“boxes” all “segments”
“segments” none “boxes”
“color.guide” all
“peaks” all “peak.labels”
“peak.labels” none “peaks”
“valleys” none “valley.labels”
“valley.labels” none “valleys”
“wls” none “wls.labels”
“wls.labels” none “wls”
“labels” all
“summaries” source_spct, response_spct, filter_spct, reflector_spct
“boundaries” raw_spct
“title” none
“title:type none
“title:type:type none

Titles come in different flavours. Package ‘ggplot2’ supports titles, subtitles and captions. The "title" as argument to parameter annotation of autoplot() methods and of function autotitle() takes up to three optional modifiers separated by colons that can be used to specify the contents of the automatic title, subtitle and caption. Currently supported modifiers are shown in the table below. If the metadata item is not stored in the plotted spectral object, the title or subtitle will show NA. See the documentation of packages ‘photobiology’ and ‘ooacquire’ for information on how to set and unset attributes of spectral objects. To add an arbitrary title and/or subtitle to a plot, use either function ggtitle() or function labs() from package ‘ggplot2’.

Modifier text source used
objt name of the object plotted
class class of the plotted object
what what.measured attribute
when when.measured attribute
where where.measured attribute
how how.measured attribute
inst.name spectrometer name
inst.sn spectrometer serial number
comment comment attribute
none no title, no subtitle or no caption

For example "title:objt", the default for "title", adds a title with the name of the object being plotted. what, when, where and how use these attributes if available. For example "title:what:when" will use thewhat.measuredattribute for the title and thewhen.measured attribute for the subtitle.

We can add an annotation to the defaults.

autoplot(sun.spct,
annotations = c("+", "title:when:where"))

We use “none” as a filler.

autoplot(sun.spct,
annotations = c("+", "title:none:what:where"))

In this case "boundaries", an annotation that adds a horizontal dashed line and does not override any other annotation.

autoplot(sun.spct,
annotations = c("+", "boundaries"))

We can list all the annotations to be included in a plot, in which case "=" is optional so as to maintain compatibility with earlier versions.

autoplot(sun.spct,
annotations = c("=", "labels", "summaries", "color.guide", "peaks", "boundaries"))

As indicated in the table above, some annotations override other annotations which fulfil a similar role. Here "segments" overrides "boxes", in the default.

autoplot(sun.spct,
annotations = c("+", "segments"))

We can also remove some of the default annotations on a case by case basis.

autoplot(sun.spct, annotations = c("-", "summaries", "peaks"))

The behaviour of some annotations can be tweaked. Below we add "valleys" as annotations, and control with span how close to each other are the peaks and valleys found.

autoplot(sun.spct, annotations = c("+", "valleys"), span = 41)

The annotations "peak.labels" and "valley.labels" override "peaks" and "valleys". They use the repulsive geometry geom_label_repel from package ‘ggrepel’.

autoplot(sun.spct, annotations = c("+", "peak.labels", "valley.labels"), span = 51)

Passing "" as argument to annotations results in a plot with no annotations, and no scales set.

autoplot(sun.spct, annotations = "")

Passing "reserve.space" as argument to annotations results in a plot with no annotations, but scales set and expanded so as to receive annotations.

autoplot(sun.spct, annotations = "reserve.space")

The size of the font used for the annotations is controlled by argument text.size.

autoplot(sun.spct, annotations = c("=", "segments", "labels", "color.guide"),
text.size = 3.5)

Argument ylim allows to mannually set the limits of the $$y$$ axis using the same syntax as in package ‘ggplot2’. The annotations are still automatically possitioned, and the range extended to make space for them. In other words the values passed to ylim still give the “space” available for plotting data.

autoplot(sun.spct, ylim = c(NA, 1))

Arguments range and w.band play very different roles. The first one determines the range of wavelengths to include in the data plotted, which is slightly different to the effect of ggplot2::xlim() as range is used to trim the spectral data before passing it to ggplot, using interpolation when needed (see photobiology::trim_wl()). The second one, w.band is only used for the annotations and decorations. The default for range is always the range of the spectrum being plotted.

Which wavebands are used by default can be changed by setting R option "photobiology.plot.bands" to a single waveband object or to a list waveband objects. Function set_w.band_default() allows to set this option using the same syntax as described for parameter w.band.

autoplot(sun.spct, range = VIS())

autoplot(sun.spct, w.band = PAR())

autoplot(sun.spct, w.band = CIE())

NULL as argument for w.band is replaced by a waveband covering the full range of the spectral data.

autoplot(sun.spct, w.band = NULL)

The whole range is the range plotted, which is controlled by the argument passed to range.

autoplot(sun.spct, w.band = NULL, range = c(400,700))

The argument passed to range can also be a waveband or and spectrum, in which case their wavelength range is used.

autoplot(sun.spct, w.band = NULL, range = PAR())

autoplot(sun.spct, w.band = PAR(), range = PAR())

autoplot(sun.spct, w.band = VIS_bands(), range = VIS())

The time unit is also stored in the metadata, as demonstrated here. The units in axis labels have changed to the units used in the spectral data.

getTimeUnit(sun.daily.spct)
## [1] "day"
autoplot(sun.daily.spct)

Even though the autoplot() methods can return a finished plot, the returned object is a ggplot object and can be built upon by adding additional elements like facets, aesthetics and even additional layers. We pass idfactor = NA to suppress the mapping of the spectra to linetype.

autoplot(two_suns.spct, idfactor = NA) + facet_wrap(~spct.idx)

It is possible to construct and bind the spectra on-the-fly, and to use arbitrary variable names for the index factor. This works automatically as long as row binding is done with function rbindspct() which saves the name of the factor.

filter_no_yes.spct <-
rbindspct(list(sun = sun.spct, filtered = yellow_gel.spct * sun.spct),
idfactor = "Source")
autoplot(filter_no_yes.spct)

In the examples above the source_spct object sun.spct was used. The autoplot() methods for other spectral classes have slight differences. We show some examples for filter_spct objects. For a long-pass filter the wavelength at half maximum is more interesting than peaks or valleys.

autoplot(yellow_gel.spct,
annotations = list(c("-", "peaks"), c("+", "wls")))

By default transmittance, reflectance and absorptance are expressed as fractions of one, as in the plot above, but optionally percents can be plotted.

autoplot(yellow_gel.spct, pc.out = TRUE,
annotations = list(c("-", "peaks"), c("+", "wls")))

In many cases it is possible to convert on-the-fly the quantity plotted. In this case, given that the data are clipped as absorbance, a fixed target of A = 2 is appropriate.

autoplot(yellow_gel.spct, plot.qty = "absorbance", wls.target = 2,
annotations = list(c("-", "peaks"), c("+", "wls")))

If one needs to, one can add a suitable stat using ‘local’ data. A peak annotation could be added manually.

autoplot(sun.spct) + geom_spct(fill = color_of(sun.spct)) +
geom_spct(data = yellow_gel.spct * sun.spct, color = "black",
fill = color_of(yellow_gel.spct * sun.spct))

The examples above use an emission spectrum. All the spectral classes defined in package ‘photobiology’ are supported, however.

In the case of quantities like transmittance which have a certain range of valid values, both upper and lower boundaries are highlighted, but in other cases only one, or even none depending on the possible valid ranges for the spectral quantities.

autoplot(yellow_gel.spct, annotations = c("+", "boundaries"))

In the case of raw instrument counts data, if the spectral object contains an instrument descriptor as metadata, the upper boundary is set to the maximum counts of the detector.

autoplot(white_led.raw_spct, annotations = c("+", "boundaries"))

Both raw_spct and cps_spct objects contain multiple data columns when integration time bracketing has been used during data acquisition. In such cases, if one wants to plot only one of the raw spectra, method select() from package ‘dplyr’ should be used instead of subscripting ([ ]) so as to preserve the metadata. In addition renaming of the variable is needed as shown below when a single column is extracted.

autoplot(dplyr::select(white_led.raw_spct, w.length, counts = counts_1),
annotations = c("+", "boundaries"))
autoplot(dplyr::select(white_led.raw_spct, w.length, counts_1, counts_3),
annotations = c("+", "boundaries"))

By defaulf color definitions are generated using the color matching function, "CMF", for human vision. It is possible to pass a different definition as a chroma_spct object or a character name. Bees have trichromic vision, but see green, blue and ultraviolet (GBU) instead of red, green and blue (RGB). The luminance is matched to wavelengths, but the colors shifted so that green becomes red, blue becomes green, and ultraviolet becomes blue. Here instead of removing or adding specific annotations from/to the default, we set them using "=" as the first string.

autoplot(sun.spct,
chroma.type = beesxyzCMF.spct,
annotations = c("=", "color.guide"))

### Handling of off-range data

If the supplied data include off-range values such as negative irradiance or fractional transmittance, reflectance, or absorptance outside the zero to one range the exceeded boundary is highlighted.

# We disable range check to avoid warning during CRAN checks!!
options(photobiology.strict.range = NA)
autoplot(yellow_gel.spct - 0.01, annotations = c("+", "boundaries"))
## Warning in range_check_Tfr(x, strict.range = strict.range): Off-range
## transmittance values [-0.00999..0.89250] instead of [0..1]
## Off-range transmittance values [-0.00999..0.89250] instead of  [0..1]
## Off-range transmittance values [-0.00999..0.89250] instead of  [0..1]
## Off-range transmittance values [-0.00999..0.89250] instead of  [0..1]
## Off-range transmittance values [-0.00999..0.89250] instead of  [0..1]
## Off-range transmittance values [-0.00999..0.89250] instead of  [0..1]
## Off-range transmittance values [-0.00999..0.89250] instead of  [0..1]
options(photobiology.strict.range = FALSE)

## Ploting collections of spectra

autoplot() methods are available for collections of spectra. We use in the examples a simple collection of two spectra. The output and acceptable arguments are the same as those in the autoplot() methods for multiple spectra in long form described above.

two_suns.mspct <- source_mspct(list(sun1 = sun.spct, sun2 = sun.spct / 2))
mixed.mspct <- generic_mspct(list(sun = sun.spct, filter = polyester.spct))

Plot accepting all defaults.

autoplot(two_suns.mspct)

Facets in one column, automatically.

autoplot(two_suns.mspct, facets = 1)

Plot using one panel per spectrum. Setting idfactor = NA suppresses the default mapping to linetype.

autoplot(two_suns.mspct, idfactor = NA) +
facet_wrap(~spct.idx, ncol = 1)

Plot as separate plots. This approach is specially useful with heterogeneous generic_mspct objects, although it works with any collection of spectra.

multiplot(plotlist = mslply(mixed.mspct, autoplot))

Using colors is slightly more involved as the identity scale is in use for color in annotations. For this same reason no color key in generated automatically. (In the case of more than two spectra it is easier to use plyr::mapvalues instead of several nested ifelse calls.)

autoplot(two_suns.mspct) +
aes(color = ifelse(spct.idx == "sun1", "darkgreen", "darkred"))

Plots created with autoplot() method are ggplot objects and can be customized, bearing in mind that any added layers will be plotted on top or existing layers.

autoplot(two_suns.mspct, annotations = c("color.guide", "valleys", "peaks"), span = 51)

Here we replace the default peaks annotations with a custom one, but still take advantage of other defaults like nice axis labels and other annotations.

autoplot(two_suns.mspct, annotations = c("-", "peaks")) +
stat_peaks(span = NULL, color = "red") +
stat_peaks(span = NULL, geom = "text",
label.fmt = "max at %3.1f nm",
vjust = -0.4, color = "red")

## Ploting wavebands

A autoplot() method for waveband objects is also provided.

autoplot(VIS())

autoplot(CIE(), range = CIE(), annotations = c("-", "color.guide"))

autoplot(DNA_N(), range = c(270, 420), annotations = c("-", "color.guide"))`