User Guide: 2 Autoplot Methods

‘ggspectra’ 0.3.12

Pedro J. Aphalo



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.


good_label_repel <- packageVersion('ggrepel') != "0.9.0" # >= 0.9.0 required
options(ggrepel.max.overlaps = Inf) # needed for 'ggrepel' (>= 0.9.0)

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

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

We change the default theme.


Using the autoplot() methods

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 a deprecated 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.

The basics

Here we use the same example source_spct object from package ‘photobiology’ used in User Guide 1 to demonstrate parameters common to all autoplot() methods for spectra. Methods for plotting of objects of all the classes defined in package ‘photobiology’, except solute_spct are available. Function spct_classes() lists these classes.

##  [1] "calibration_spct" "raw_spct"         "cps_spct"         "filter_spct"     
##  [5] "reflector_spct"   "source_spct"      "object_spct"      "response_spct"   
##  [9] "chroma_spct"      "solute_spct"      "generic_spct"

The simplest possible call to autoplot() needs only one argument, the object to plot.


In contrast to the examples in the User Guide 1, here we obtain directly an annotated plot of the solar spectrum at ground level. This would be of limited use unless without the possibility to adjust the design and components of the ggplot object created. One possibility is to use the grammar of graphics to add to the plot. However, it is also possible to control many features of the plot by passing additional arguments in the call to autoplot().

When plotting source_spct and response_spct objects we can change the basis of expression of spectral 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")

We can build and area + line ("spct") plot instead the default line plot.

autoplot(sun.spct, geom = "spct")

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


In addition to the autoplot() methods for individual spectra, there are autoplot() methods available for collections of spectra. Collections of spectra like two_suns.mspct can be plotted also automatically, as they are combined on-the-fly and plotted as shown above in the example for two_suns.spct.

We use in the examples a simple collection of two spectra. The output and accepted arguments are the same as those in the autoplot() methods for multiple spectra in long form described above. The plotting of objects of all the classes for collections of spectra defined in package ‘photobiology’ is similar. However, classes solute_mspct and generic_mspct are currently not supported. Function mspct_classes() lists all the classes for collections of spectra defined in package ‘photobiology’.

##  [1] "calibration_mspct" "raw_mspct"         "cps_mspct"        
##  [4] "filter_mspct"      "reflector_mspct"   "source_mspct"     
##  [7] "object_mspct"      "solute_mspct"      "response_mspct"   
## [10] "chroma_mspct"      "generic_mspct"
two_suns.mspct <- source_mspct(list(sun1 = sun.spct, sun2 = sun.spct / 2))

The plot using defaults is identical to that above.


We can choose a different name for the factor identifying the spectra in the collection.

autoplot(two_suns.mspct, idfactor = "Spectra")

With facets, there is only one spectrum per panel. Summaries per waveband are shown.

autoplot(two_suns.spct, facets = TRUE)

Above we showed the default faceting by passing facets = TRUE. We can set the number of columns by passing a number. For panels in one column we pass facets = 1.

autoplot(two_suns.mspct, facets = 1)

For two columns we pass facets = 2.

autoplot(two_suns.mspct, facets = 2)


We can zoom into the spectrum, by providing a wavelength range in nanometres. Here we pass a vector of two numbers as argument, but any object for which a range() method is available can also be used, including longer numeric vectors, objects of one the spectrum classes, such as class source_spct, or a waveband definition.

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

We can show summaries for different wavebands, by passing them to parameter We can pass either individual wavebands or lists of wavebands. First a list of wavebands created with a constructor, here showing the limits based on the ISO standard.

autoplot(sun.spct, = photobiologyWavebands::VIS_bands("ISO"))

A single waveband can also be created with a generic constructor.

autoplot(sun.spct, = waveband(c(380, 760)))

NULL as argument for is replaced by a waveband covering the full range of the spectral data. The whole range is the range plotted, which is controlled by the argument passed to range.

autoplot(sun.spct, = NULL)

Summarizing the examples above, arguments passed to range and play very different roles. Parameter range, gives the wavelengths to include in the plot, and the default for range is always the range of wavelengths in the spectrum being plotted. The argument to is only used for the annotations and decorations, described in a leater section in more detail.

The effect of range 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()). In contrast xlim discards data for all wavelengths not within the range.

The default argument to parameterw.bandcan be changed by setting the 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 See package ‘photobiologyWavebands’ for waveband constructors for ISO and other waveband definitions in common use, and package ‘photobiology’ for defining your own.

set_w.band_default( = Plant_bands())

“Parallel” summaries

All plotting of collections of spectra shown above were done with the default = "". We can also plot a row-wise summary (wavelength by wavelength, or parallel summaries) of the spectra in the collection, here the mean of the spectra. Currently implemented only for spectra with identical wavelength vectors and of the same class.

autoplot(two_suns.mspct, = "mean")

Summary quantities by waveband

Although the defaults result in the addition of frequently useful annotations, the autoplot() methods accepts arguments for several parameters that make possible flexible control of the annotations. Currently, numerical summaries can be added automatically to plots only when a single spectrum is plotted per plot panel.

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 displayed in the plot. The second one, "contribution" displays in labels the integral of each waveband divided by the integral of the whole spectrum displayed in the plot. 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. Adding .pc to the arguments, that is, using "relative.pc" or "contribution.pc", results in the corresponding values being displayed as percentages.

When using "contribution.pc" unless the summarized wavebands cover the whole range of wavelengths in the spectrum, the sum of the summary values is less than 100.

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

When using "relative.pc" the sum adds to 100.

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

Automatic annotations

Which annotations are included can be controlled through parameter annotations. It accepts a character vector, or a list of character vectors 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 "" 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 or modify this option using the same syntax as described for parameter annotations.

annotation default for classes overrides
“boxes” all “segments”
“segments” none “boxes”
“” 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
“title:type: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 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 spectrometer name 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 the what.measured, when.measured, where.measured and how.measured attributes if available. For example "title:what:when" will use the what.measured attribute for the title and the when.measured attribute for the subtitle.

We add a title, subtitle and caption.

     annotations = c("+", "title:objt:when:where"))

We use “none” as a filler so that only subtitle and caption are added to the plot.

     annotations = c("+", "title:none:what:where"))

With "boundaries", we adds horizontal dashed line(s) showing the valid range of values. It does not override any other annotation. (The boundary line is shown in red when a plot displays out-of-range spectral data.)

     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.

     annotations = c("=", "labels", "summaries", "", "peaks", "boundaries"))

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

     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’.

         annotations = list(c("+", "peak.labels"), 
                            c("-", "boxes", "summaries", "labels")), 
         span = 21)

         annotations = list(c("+", "valley.labels"), 
                            c("-", "peaks")), 
         span = 31)

autoplot(sun.spct, annotations = c("+", "peak.labels", "valley.labels"), span = 31)

Passing "" as argument to annotations results in a plot with no annotations, and no extra expansion of scale limits.

autoplot(sun.spct, annotations = "")

Passing "" as argument to annotations results in a plot with no annotations, but with scale limits expanded so as to receive annotations.

autoplot(sun.spct, annotations = "")

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

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

Argument ylim allows to manually set the limits of the \(y\) axis using the same syntax as in package ‘ggplot2’. The annotations are still automatically positioned, 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))

The time base of the spectral unit or the duration of the exposure is stored as metadata. As demonstrated here using spectral data integrated over 24 h, a one-day-long exposure, the units in the axis labels change according to the value stored in the metadata.

## [1] "day"

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(facets = vars(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")

In the examples above the source_spct object sun.spct was used. The autoplot() methods for other spectral classes have only 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.

         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 for the cut-off to be labelled with the wavelength is appropriate.

autoplot(yellow_gel.spct, plot.qty = "absorbance", = 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))

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"))

Differently from other classes raw_spct and cps_spct objects can contain multiple columns of data, normally measured different integartion times, and meant to be combined before conversion into physical quantities. 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, one can extract the columns as usual in R using the extraction operator ([ ]). The single remaining column of spectral data is automatically renamed.

autoplot(white_led.raw_spct[ , c("w.length", "counts_1")],
         annotations = c("+", "boundaries"))
autoplot(white_led.raw_spct[ , c("w.length", "counts_1", "counts_3")],
     annotations = c("+", "boundaries"))

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 in red.

# Not run so as to pass CRAN checks!!
autoplot(yellow_gel.spct - 0.01, annotations = c("+", "boundaries"))

Editing plots and adding layers

Using colors when adding to a plot generated by the autoplot() methods is more involved than usual, one has to take into account that the identity scale is in use for color in annotations, and a ggplot can make use of only one scale for a given aesthetic. For this same reason no color key in generated automatically.

autoplot(two_suns.mspct, annotations = c("-", "peaks")) +
  aes(color = ifelse(spct.idx == "sun1", "darkgreen", "darkred"), linetype = "solid") +
  theme(legend.position = "none")

Plots created with autoplot() methods are ggplot objects and can be customized, bearing in mind that any added layers will be plotted on top or existing layers (that is unless we make use of methods from package ‘gginnards’ that allow insertion of layers at any position in a ggplot object).

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 = -0.5, hjust = 0,
             color = "red", size = 3)

Ploting wavebands

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


autoplot(PAR(), range = c(200, 1000), geom = "spct",  = "photon", unit.out = "energy")

autoplot(CIE(), range = CIE(), annotations = c("-", ""))

autoplot(DNA_N(), range = c(270, 420), annotations = c("-", ""))