User Guide: 1 Grammar of Graphics
‘ggspectra’ 0.3.14.9000
Pedro J. Aphalo
2024-11-30
Source:vignettes/userguide1-grammar.Rmd
userguide1-grammar.Rmd
Introduction
Package ‘ggspectra’ extends ‘ggplot2’ with stats, geoms, scales 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. The autoplot()
methods
are described separately in vignette ‘Autoplot Methods’.
The new elements can be freely combined with methods and functions defined in packages ‘ggplot2’, ‘scales’ and extensions like ‘ggrepel’, ‘cowplot’, ‘ggpp’, ‘gginnards’ and ‘patchwork’.
What are autoplot()
method specializations for?
The autoplot()
generic method is defined in package
‘ggplot2’. Package ‘ggspectra’ provides specializations of this method
that construct fully annotated plots as ggplot objects, which can be
further manipulated if so desired. These methods use the metadata stored
in spectral objects of classes defined in package ‘photobiology’ to
automatically generate suitable axis labels, scales and annotations.
(Please, see vignette “Autoplot methods” for details.)
What are ggplot
specializations for?
The ggplot()
specializations set default
aes
according to the type of spectral object. They also add
support for unit.out
arguments allowing on-the-fly
conversion of units of expression or spectral quantities. These are only
defaults and can be overridden by explicit use of aes()
to
set the mapping of aesthetics.
What are the different stats useful for?
The stats defined in this package help with with the
generation of annotations and decorations of plots of spectral data.
They are meant to be used only when the x aesthetic is mapped
to a variable containing wavelength values expressed in nanometres. They
are designed to work with spectral objects of the classes defined in
package ‘photobiology’. Many of them also work well with any data frame
as long as the x aesthetic mapping fulfils the expectations.
Package ‘ggpmisc’ contains some equivalent stats which do not
assume that x is mapped to wavelength, accepting
numeric
and datetime values.
stat | default geom (used for) | other uses |
---|---|---|
stat_peaks |
point (highlight maxima) | wavelength label, spectral quantity label |
stat_valleys |
point (highlight minima) | wavelength label, spectral quantity label |
stat_label_peaks |
text (wavelength label) | spectral quantity label (support ‘ggrepel’) |
stat_label_valleys |
text (wavelength label) | spectral quantity label (support ‘ggrepel’) |
stat_find_wls |
point (highlight wls at qty) | wavelength label, spectral quantity label |
stat_find_qtys |
point (highlight qty at wl) | wavelength label, spectral quantity label |
stat_color |
color, fill | |
stat_wb_label |
text (waveband name) | rect (showing range of waveband and its color) |
stat_wb_total |
text (y integral) | label(s) with waveband integral |
stat_wb_mean |
text (y mean) | label(s) with waveband mean |
stat_wl_summary |
text (y mean) | label with wavelength range mean |
stat_wb_contribution |
text (contribution) | label(s) with waveband integral / whole spectrum integral |
stat_wb_relative |
text (relative) | label(s) with waveband integral / sum of integrals of all wavebands |
stat_wb_e_irrad |
text (energy irradiance) | rect (showing range of waveband and its color) |
stat_wb_q_irrad |
text (photon irradiance) | rect (showing range of waveband and its color) |
stat_wb_e_sirrad |
text (spectral energy irradiance) | rect (showing range of waveband and its color) |
stat_wb_q_sirrad |
text (spectral photon irradiance) | rect (showing range of waveband and its color) |
stat_wl_strip |
rect (fill of wavelength or waveband) | text (label with waveband name) |
stat_wb_box |
rect (fill of waveband) | |
stat_wb_hbar |
errorbarh (color of waveband) | |
stat_wb_column |
rect (fill of waveband) |
What is geom_spct
useful for?
The geom_spct
geometry is a special case of
geom_area
, but with the minimum of the y range
fixed to 0, but with stacking not enabled.
What are the new scales for?
The new scales are convenience wrapper functions built on top of the scales exported by package ‘ggplot2’, but with default arguments that are suitable for spectral data.
Functions for automatic generation of secondary x-axes in the case when a variable containing wavelength data (nm) is mapped to the x aesthetic simplify the task of adding an axis with frequencies or wave numbers to the plot of a spectrum.
scale | unit.exponent | name | labels | breaks |
---|---|---|---|---|
scale_y_cps_continuous | 0 | cps_label() | SI_pl_format() | |
scale_y_counts_continuous | 3 | counts_label() | SI_pl_format() | |
scale_y_counts_tg_continuous | 3 | counts_label() | SI_tg_format() | |
scale_y_A_internal_continuous | 0 | A_internal_label() | SI_pl_format() | |
scale_y_A_total_continuous | 0 | A_total_label() | SI_pl_format() | |
scale_y_Tfr_internal_continuous | 0 | Tfr_internal_label() | SI_pl_format() | |
scale_y_Tfr_total_continuous | 0 | Tfr_total_label() | SI_pl_format() | |
scale_y_Rfr_internal_continuous | 0 | Rfr_internal_label() | SI_pl_format() | |
scale_y_Rfr_total_continuous | 0 | Rfr_total_label() | SI_pl_format() | |
scale_y_s.e.irrad_continuous | 0 | s.e.irrad_label() | SI_pl_format() | |
scale_y_s.q.irrad_continuous | -6 | s.q.irrad_label() | SI_pl_format() | |
scale_y_s.e.response_continuous | 0 | s.e.response_label() | SI_pl_format() | |
scale_y_s.q.response_continuous | 0 | s.q.response_label() | SI_pl_format() | |
scale_x_wl_continuous | -9 | w_length_label() | SI_pl_format() | pretty_breaks(n=7) |
scale_x_wavenumber_continuous | -6 | w_number_label() | SI_pl_format() | pretty_breaks(n=7) |
scale_x_energy_eV_continuous | 0 | w_energy_eV_label() | SI_pl_format() | pretty_breaks(n=7) |
scale_x_energy_J_continuous | -18 | w_energy_J_label() | SI_pl_format() | pretty_breaks(n=7) |
scale_x_frequency_continuous | 12 | w_frequency_label() | SI_pl_format() | pretty_breaks(n=7) |
In addition secondary axis definitions,
sec_axis_w_number()
, sec_axis_w_frequency()
,
sec_axis_w_energy_eV()
, sec_axis_w_energy_J()
and sec_axis_wl(), and SI system formatters SI_pl_format
,
and SI_tg_format
are exported, together with auxiliary
functions for finding the nearest SI multiplier based on an arbitrary
exponent.
What are functions color_chart()
and
black_or_white()
for?
Function color_chart()
makes a color chart of
rectangular tiles from a vector R color definitions. The chart returned
is a ggplot
object. Function black_or_white()
accepts a vector of color definitions and returns a vector with colors
"white"
or "black"
depending on the
approximate luminosity of each color in the input. The main use is to
automatically achieve suitable contrast between text plotted on top of a
color background.
What is function autotitle()
for?
Function autotitle()
adds a title, subtitle and/or a
caption to a plot. The difference with ggtitle()
from
package ‘ggplot2’ is that autotitle()
automatically
retrieves metadata from an spectral object based on keys. It is
used internally by all autoplot()
methods defined in
package ‘ggspectra’ and allowed syntax and key values are described in
User Guide 2: Autoplot Methods together with plot
annotations.
Set up
library(ggplot2)
library(scales)
library(photobiology)
library(photobiologyWavebands)
library(ggspectra)
library(ggrepel)
Create a collection of two source_spct objects.
two_suns.mspct <- source_mspct(list(sun1 = sun.spct, sun2 = sun.spct / 2))
We bind the two spectra in the collection into a single spectral
object. This object includes an indexing factor, by default names
spct.idx
. We use this new object to later on demonstrate
grouping in ggplots.
two_suns.spct <- rbindspct(two_suns.mspct)
We change the default theme.
ggplot() methods for spectra
The only difference between these specializations and the base
ggplot()
method is that the aesthetics for
and
have suitable defaults. These are just defaults, so if needed they can
still be supplied with a mapping
argument with an
user-defined aes()
.
It is possible to add to the defaults by means of +
and
aes()
as shown below.
If a mapping is supplied directly through ggplot
,
and
should be included.
In the case of ggplot.source_spct()
an additional
parameter allows setting the type of units to use in the plot. This not
only sets a suitable aes()
for
but also if needed converts the spectral data. The two possible values
are "energy"
and "photon"
and the default,
depends on option photobiology.radiation.unit
. This
parameter has a default value that can be modified through option
"photobiology.radiation.unit"
. Package ‘photobiology’
defines convenience functions for this.
After evaluation of photon_as_default()
, a new default
is in effect, but we can override it with an explicit argument if
needed.
photon_as_default()
ggplot(sun.spct) + geom_line()
ggplot(sun.spct, unit.out = "energy") + geom_line()
This new default will remain active for the rest of the R session, unless changed. We can easily either unset this default, or all photobiology-package related user set defaults.
The next example is for spectral properties of filters.
In the case of ggplot.filter_spct()
the additional
parameter is called plot.qty
and allows choosing between
"transmittance"
and "absorbance"
.
In the case of ggplot.object_spct()
three values
("reflectance"
, "transmittance"
and
"all"
are accepted. Passing "all"
as argument
results in the spectral data being molten into a long form, using
value
and variable
as value and key columns,
respectively. The column variable
has three levels
Tfr
, Rfr
and Afr
indexing the
Tfr
and Rfr
from the object_spct
object plus newly calculated absorptance values.
This parameter has a default value that can be modified through
option "photobiology.filter.qty"
. Package ‘photobiology’
defines convenience functions for this.
Afr_as_default()
ggplot(yellow_gel.spct) + geom_line()
## Warning in T2Afr.filter_spct(x, action = action, clean = clean, byref = FALSE):
## Conversion from internal Tfr to Afr possible only if Rfr or Rfr.constant are
## known.
## Warning: Removed 425 rows containing missing values or values outside the scale range
## (`geom_line()`).
The names of the additional parameters are consistent with those used
in the autoplot()
methods defined in this package.
ggplot() methods for collections fof spectra
The ggplot()
methods for collections of spectra work
similarly to the methods for spectra when used with an spectral object
containing concatenated spectra, as that shown in the previous
section.
Plotting a collection of spectra using an aesthetic.
Using facets.
Scales
The scales provided are all wrappers of continuous scales from
packages ‘ggplot2’ or ‘scales’. All pass non-specific parameters to the
wrapped scales. The scales defined in ‘ggspectra’ compute suitable
arguments for name
and labels
and pass them to
the wrapped scales. The default text for the labels can be also set by
the user by redefining a function, in addition than overriding the
defaults in individual calls. This is a step towards multilingual
support.
Shared features
We present here, only once for all scales, examples of how to use features common to all of them. To start with we show the defaults for the scale for wavelengths and spectral energy irradiance that we will use for this examples.
ggplot(sun.spct) +
geom_line() +
scale_x_wl_continuous() +
scale_y_s.e.irrad_continuous()
We can have also axis labels without symbols, either by passing an
argument to parameter axis.symbols
or by seeting R option
ggspectra.axis.symbols
, which sets the default.
ggplot(sun.spct) +
geom_line() +
scale_x_wl_continuous(axis.symbols = FALSE) +
scale_y_s.e.irrad_continuous(axis.symbols = FALSE)
Here we change the unit.exponent
from its default value
of -9
(nanometres) to -6
(micrometers) for
wavelengths, and change 0
to -3
for
irradiance. When they exist SI multipliers are used, and powers of 10
otherwise. This is not a transformation of the data, it only affects the
tick labels and the axis labels in coordination. Consequently
summary values will not be affected.
ggplot(sun.spct) +
geom_line() +
scale_x_wl_continuous(unit.exponent = -6) +
scale_y_s.e.irrad_continuous(unit.exponent = -3)
ggplot(sun.spct) +
geom_line() +
scale_x_wl_continuous(unit.exponent = -7) +
scale_y_s.e.irrad_continuous()
We can find the largest valid SI-scaling factor that is smaller or equal to an arbitrary power of 10.
## [1] -6
We can call this function on-the-fly.
ggplot(sun.spct) +
geom_line() +
scale_x_wl_continuous(unit.exponent = nearest_SI_exponent(-4)) +
scale_y_s.e.irrad_continuous()
In addition to formatters defined in package ‘scales’, two additional
formatters are defined in package ‘ggspectra’,
SI_pl_format()
and SI_tg_format()
. Most of the
new scales defined use SI_pl_format()
, but
SI_tg_format()
can be passed as an argument. When it is
used as shown here, it is important to remember that the axis label
should not show scaled units, i.e., when using
SI_tg_format()
, unit.exponent
must be passed
zero as argument. I have seen this approach used in engineering
related publications. (Recent updates to packages ‘ggplot2’ and ‘scales’
have added similar functionality.)
ggplot(sun.spct) +
geom_line() +
scale_x_wl_continuous() +
scale_y_s.e.irrad_continuous(unit.exponent = 0,
labels = SI_tg_format(exponent = -3))
Transformation objects from package ‘scales’, or user defined, can be
passed as additional arguments. We replace zeros to avoid a warning from
the log10()
call, and as the scale limits
discards some observations, we use na.rm = TRUE
to silence
this additional warning.
temp.spct <- clean(sun.spct, range.s.data = c(1e-20, Inf), fill = 1e-20)
ggplot(temp.spct) +
geom_line(na.rm = TRUE) +
scale_x_wl_continuous() +
scale_y_s.e.irrad_continuous(unit.exponent = 0,
trans = "log10",
labels = trans_format("log10", math_format()),
limits = c(1e-6, NA))
The default text used can be overridden. To keep only the symbol and
units pass ""
as argument (not shown).
ggplot(sun.spct) +
geom_line() +
scale_x_wl_continuous(label.text = "Longitud de onda,") +
scale_y_s.e.irrad_continuous(label.text = "Irradiancia espectral,")
If the data are normalized, we can pass the normalization wavelength as an argument. In this example, we retrieve this wavelength from the metadata.
norm_sun.spct <- normalize(sun.spct)
ggplot(norm_sun.spct) +
geom_line() +
scale_x_wl_continuous() +
scale_y_s.e.irrad_continuous(normalized = getNormalized(norm_sun.spct))
If the data have been scaled, we can pass this information to the scale.
scaled_sun.spct <- fscale(sun.spct)
ggplot(scaled_sun.spct) +
geom_line() +
scale_x_wl_continuous() +
scale_y_s.e.irrad_continuous(scaled = is_scaled(scaled_sun.spct))
Name and labels can be passed directly as arguments, but this defeats the purpose of these wrappers.
Wavelength
Currently one x-scale function suitable for wavelengths in nanometres is exported by package ‘ggspectra’, as well as scales for wave number, frequency and energy, expecting the respective data quantities expressed in SI units with no scale factor.
These scale functions can be also used when plots are based on ordinary data frames or tibbles. However, in this case users must ensure that the data are expressed as expected as otherwise the axis labels and the power of ten multipliers plotted are wrong.
ggplot(sun.spct) +
geom_line() +
scale_x_wl_continuous()
Except scale_x_wl_continuous()
the x scales if
used with source_spct()
objects as data
require the wavelength values to be converted to the expected quantity
using aes()
to map variables to both x and
y aesthetics.
ggplot(sun.spct, aes(x = wl2frequency(w.length), y = s.e.irrad)) +
geom_line() +
scale_x_frequency_continuous()
Functions automating the addition of secondary axes are available. They expect wavelength in nanometres in data and convert it into other equivalent physical quantities: wave number, frequency and energy per photon.
ggplot(sun.spct) +
geom_line() +
scale_x_wl_continuous(sec.axis = sec_axis_w_frequency())
ggplot(sun.spct) +
geom_line() +
scale_x_wl_continuous(sec.axis = sec_axis_energy_eV())
As shown above for the main axis, it is possible to set a different SI scaling factor for the units in secondary scales.
ggplot(sun.spct) +
geom_line() +
scale_x_wl_continuous(sec.axis = sec_axis_w_frequency(15))
Raw counts
Raw counts from array detectors are expressed in counts, as “counts” is a whole word rather than a unit a power of ten multiplier is used.
ggplot(white_led.raw_spct) +
geom_line() +
scale_x_wl_continuous() +
scale_y_counts_continuous()
The tg
for tag version adds a suffix to the tick labels,
as is common in engineering.
ggplot(white_led.raw_spct) +
geom_line() +
scale_x_wl_continuous() +
scale_y_counts_tg_continuous()
Counts per second
Raw counts from array detectors are expressed as a rate, as “counts” is a whole word rather than a unit a power of ten multiplier is used.
ggplot(white_led.cps_spct) +
geom_line() +
scale_x_wl_continuous() +
scale_y_cps_continuous(unit.exponent = 3)
Spectral irradiance
Four scales are available, one for energy irradiance and one for
photon irradiance. We show those for energy irradiance. There are
equivalent scales scale_y_s.q.irrad_continuous()
and
scale_y_s.e.irrad_log10()
for photon irradiance.
ggplot(sun.spct) +
geom_line() +
scale_x_wl_continuous() +
scale_y_s.e.irrad_continuous()
ggplot(sun.spct, unit.out = "photon", range = c(293, NA)) +
geom_line() +
scale_x_wl_continuous() +
scale_y_s.e.irrad_log10(unit.exponent = -6)
Response and action
Four scales are available, for energy response and action and for
photon response and action. We show those for energy irradiance. There
are equivalent scales scale_y_q.e.resoponse_continuous()
and scale_y_s.q.action_continuous()
for photon
irradiance.
The difference between response and action spectra stems from the measurement procedure. We will wrongly use here response data for both examples.
ggplot(ccd.spct) +
geom_line() +
scale_x_wl_continuous() +
scale_y_s.e.response_continuous(unit.exponent = 6)
The figures are identical, but the text and symbol on the y-axis label are different.
ggplot(ccd.spct) +
geom_line() +
scale_x_wl_continuous() +
scale_y_s.e.action_continuous(unit.exponent = 6)
Transmittance
Two definitions of transmittance exist, total and internal. To obtain
the correct labels we query the object containing the data. If we plot
data from a data frame or tibble, then we can manualy pass one of
"total"
or "internal"
as argument.
ggplot(yellow_gel.spct) +
geom_line() +
scale_x_wl_continuous() +
scale_y_Tfr_continuous(Tfr.type = getTfrType(yellow_gel.spct))
ggplot(yellow_gel.spct) +
geom_line() +
scale_x_wl_continuous() +
scale_y_Tfr_continuous(Tfr.type = getTfrType(yellow_gel.spct),
labels = percent)
gel_internal.spct <- convertTfrType(yellow_gel.spct, Tfr.type = "internal")
ggplot(gel_internal.spct) +
geom_line() +
scale_x_wl_continuous() +
scale_y_Tfr_continuous(Tfr.type = getTfrType(gel_internal.spct))
## Warning: Removed 425 rows containing missing values or values outside the scale range
## (`geom_line()`).
Absorbance
Two definitions of absorbance exist, total and internal. To obtain
the correct labels we query the object containing the data. If we plot
data from a data frame or tibble, then we can manualy pass one of
"total"
or "internal"
as argument. This
package only supports absorbance as defined using logs on base
10.
ggplot(gel_internal.spct, plot.qty = "absorbance") +
geom_line() +
scale_x_wl_continuous() +
scale_y_A_continuous(Tfr.type = getTfrType(gel_internal.spct))
## Warning: Removed 425 rows containing missing values or values outside the scale range
## (`geom_line()`).
Absorptance
Absorptance has only one definition, at least within this package.
ggplot(yellow_gel.spct, plot.qty = "absorptance") +
geom_line() +
scale_x_wl_continuous() +
scale_y_Afr_continuous()
## Warning in T2Afr.filter_spct(x, action = action, clean = clean, byref = FALSE):
## Conversion from internal Tfr to Afr possible only if Rfr or Rfr.constant are
## known.
## Warning: Removed 425 rows containing missing values or values outside the scale range
## (`geom_line()`).
Reflectance
Two definitions of reflectance exist, total and specular. To obtain
the correct labels we query the object containing the data. If we plot
data from a data frame or tibble, then we can manualy pass one of
"total"
or "specular"
as argument.
ggplot(green_leaf.spct) +
geom_line() +
scale_x_wl_continuous() +
scale_y_Rfr_continuous(Rfr.type = getRfrType(green_leaf.spct))
Stats
Several ggplot
stats are defined by this package. All of
them target light spectra, as they expect
to represent wavelengths expressed in nanometres. However, they should
behave correctly as long as this is true, with any ggplot
object, based on any data format acceptable to ggplot
. The
name of the original variable is irrelevant, and it is the user
responsibility to supply the correct variables through
aes()
. Of course, when using the spectral classes defined
in package photobiology
the defaults easy this task.
Peaks, valleys and target values
Four stats are available for peaks and valleys, with the same formal
parameters. These stats do not fit peaks, simply search for local maxima
and local minima in the data as supplied. Stats
stat_peaks()
and stat_valleys()
subset the
original data while stat_label_peaks()
and
stat_label_valleys()
only set a boolean flag to mark the
local extremes. Two stats are available for highlighting arbitrary
locations in spectra, one of them, stat_find_wls()
accepts
a target for the spectral quantity and locates the corresponding
wavelength values while the other, stat_find_qtys()
accepts
a target wavelength value and locates the corresponding spectral
quantity value. Stats stat_find_wls()
and
stat_find_qtys()
subset the original data or generate new
data by interpolation.
All six stats set the same default aesthetics based on calculated
values. Not all of these default aesthetics are used by the default
geom, but they make using other geoms easier. Furthermore generated text
labels are formatted with sptintf()
and these six stats
accept format definitions through parameters label.fmt
,
x.label.fmt
, and y.label.fmt
. Please see the
documentation for a list of all the computed varaibles returned in
data
. These stats use internally functions
photobiology::find_peaks()
and
photobiology::find_wls()
and arguments are passed down to
them.
The examples that follow, apply with minimal changes to
stat_peaks()
, stat_valleys()
,
stat_find_wls()
and stat_find_qtys()
.
ggplot(sun.spct) + geom_line() + stat_peaks(color = "red")
Because of the conversion, the location of maxima and minima when an irradiance or response spectrum is expressed in photon- vs. energy-based units may differ. This is expected and not a bug.
ggplot(sun.spct, unit.out = "photon") + geom_line() + stat_peaks(color = "red")
The complement to stat_peaks()
is
stat_valleys
.
ggplot(sun.spct) + geom_line() + stat_valleys(color = "blue")
Stats stat_find_wls()
and stat_find_qtys()
are another complementary pair. The default target, used in this
example, is the half maximum.
ggplot(yellow_gel.spct) + geom_line() + stat_find_wls(color = "orange")
One of the values calculated and mapped is colour as seen by humans
corresponding to the wavelength at the location of peaks and valleys.
The colour is mapped to the fill
aesthetic, so using a
‘filled’ shape results in a colourful plot. The identity scale
is needed so that the correct colours are displayed using the colour
definitions in the calculated data instead of a palette.
ggplot(sun.spct) + geom_line() +
stat_peaks(shape = 21, color = "black") + scale_fill_identity()
We can use any of the aesthetics affecting the default geom,
"point"
, and also with other _geom_s.
ggplot(sun.spct) + geom_line() +
stat_peaks(span = 35, shape = 4, color = "red", size = 2) +
stat_peaks(span = 35, color = "red", geom = "rug", sides = "b")
We can use several other geoms as needed, demonstrated here
with geom "text"
. To displace the text we can use
nudging, justification or both. If we want to have the bottom edge of
the label 0.01 y-data units above its natural position we can
use.
ggplot(sun.spct) + geom_line() +
stat_peaks(geom = "text",
span = 35,
color = "red",
vjust = "bottom",
position = position_nudge(y = 0.01))
The same stat can be included more than once in the same plot, using
different geoms. We here in addition demonstrate the use of
several different parameters. The span
argument determines
the number of consecutive observations tested when searching for a local
extreme, and it should be an odd integer number. In addition we here
demonstrate the use of a geom new to ggplot2
2.0.0
called "label"
which again results in colourful labels by
default. Here we make use of the computed variable BW.color
which is set to "white"
or "white"
for maximum
contrast with the computed variable fill
.
ggplot(sun.spct) + geom_line() +
stat_peaks(shape = 21,
span = 35,
size = 2) +
stat_label_peaks(geom = "label",
span = 35,
vjust = "bottom",
size = 3,
position = position_nudge(y = 0.01)) +
scale_fill_identity() +
scale_color_identity() +
expand_limits(y = 0.9)
Using a larger number as argument to span reduces the number of peaks detected.
ggplot(sun.spct) + geom_line() +
stat_peaks(shape = 21,
span = 35,
size = 2) +
stat_label_peaks(geom = "label",
span = 35,
size = 3,
na.rm = TRUE,
vjust = "bottom",
position = position_nudge(y = 0.01)) +
scale_fill_identity() +
scale_color_identity() +
expand_limits(y = 0.9)
Setting span
to NULL
results in the span
set to the range of the data, and so in this case the stat returns the
global extreme, instead of a local one. In this case we use geoms
"vline"
and "hline"
taking advantage that
suitable aesthetics are set by the stats.
ggplot(sun.spct) + geom_line() +
stat_peaks(span = NULL, geom = "vline", linetype = "dotted", color = "red") +
stat_peaks(span = NULL, geom = "hline", linetype = "dotted", color = "red")
By default the label
aesthetic is mapped to a calculated
label x.label
giving the wavelength in nanometres. This
mapping can be changed to give to a label giving the y-value at
the peak. We cannot pass nudge_y
directly, we need to the
nudge as a position
.
ggplot(sun.spct) + geom_line() +
stat_peaks(shape = 21, span = 35, size = 2) +
stat_label_peaks(aes(label = after_stat(y.label)),
span = 35, geom = "label", size = 3,
position = position_nudge(y = 0.04),
label.fmt = "%1.2f") +
expand_limits(y = 1) +
scale_fill_identity() + scale_color_identity()
ggplot(sun.spct) + geom_line() +
stat_valleys(shape = 21,
span = 35,
size = 2) +
stat_label_valleys(geom = "label",
span = 35,
size = 3,
na.rm = TRUE,
vjust = "top",
position = position_nudge(y = -0.01)) +
scale_fill_identity() +
scale_color_identity()
Above, using geom_label()
there is some overlap. We can
use geom_label_repel()
from package ‘ggrepel’ to avoid it.
These geometry has additional parameters to which we need to pass
arguments to get a satisfactory positioning of labels.
ggplot(sun.spct) + geom_line() +
stat_peaks(shape = 21, span = 35, size = 2) +
stat_label_peaks(segment.colour = "black",
span = 35, geom = "label_repel", size = 3,
max.overlaps = Inf,
position = position_nudge_repel(y = 0.12),
min.segment.length = 0,
box.padding = 0.5,
force_pull = 0) +
expand_limits(y = 1) +
scale_fill_identity() +
scale_color_identity()
ggplot(sun.spct) + geom_line() +
stat_valleys(shape = 21, span = 35, size = 2) +
stat_label_valleys(segment.colour = "black",
span = 35, geom = "label_repel", size = 3,
max.overlaps = Inf,
position = position_nudge_repel(y = -0.12),
min.segment.length = 0,
box.padding = 0.53,
force = 0.5,
force_pull = 1) +
scale_fill_identity() +
scale_color_identity()
As aesthetics can use values computed on-the-fly we can even
use paste()
to map a label that combines both values and
adds additional text.
ggplot(sun.spct) + geom_line() +
stat_peaks(span = NULL, color = "red") +
stat_peaks(span = NULL, geom = "text", vjust = -0.5, color = "red",
aes(label = paste(after_stat(y.label), "at", after_stat(x.label), "nm"))) +
expand_limits(y = c(NA, 0.9))
Finally we demonstrate that both stats can be simultaneously used. One can also choose to use different spans as demonstrated here, resulting in more maxima being marked by points than labelled with text.
ggplot(sun.spct) + geom_line() +
stat_peaks(span = 21, geom = "point", colour = "red") +
stat_valleys(span = 21, geom = "point", colour = "blue") +
stat_peaks(span = 51, geom = "text", colour = "red",
vjust = -0.3, label.fmt = "%3.0f nm") +
stat_valleys(span = 51, geom = "text", colour = "blue",
vjust = 1.2, label.fmt = "%3.0f nm")
This final example shows a few additional tricks used in this case to mark and label the maximum of the spectrum. This example also demonstrates why it is important that these are stats. The peaks are searched and labels generated once for each group, in this case each facet.
ggplot(two_suns.spct) + aes(color = spct.idx) +
geom_line() + ylim(NA, 0.9) +
stat_peaks(span = NULL, color = "black") +
stat_peaks(span = NULL, geom = "text", vjust = -0.5, size = 3,
color = "black",
aes(label = paste(stat(y.label), "at", after_stat(x.label), "nm"))) +
facet_grid(rows = vars(spct.idx))
## Warning: `stat(y.label)` was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(y.label)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
An additional statistics, stat_spikes()
, returns the
same computed variables as stat_peaks()
and
stat_valleys()
but detects only very narrow peaks and
valleys, usually called spikes. They are common in Raman spectra but can
also appear occasionally in any measurement with array spectrometers
when integration times are long or if some pixels in an array detector
are defective (e.g., hot pixels and dead pixels). Usually spikes are
considered “noise” to be removed, but occasionally we may want to
highlight in a plot the spikes. (Spikes can be replaced by values
interpolated from neighbours with function despike()
from
package ‘photobiology’.)
ggplot(white_led.raw_spct, aes(w.length, counts_3)) +
geom_line() +
stat_spikes(color = "red", z.threshold = 8, max.spike.width = 7)
ggplot(despike(white_led.raw_spct, z.threshold = 8, max.spike.width = 7),
aes(w.length, counts_3)) +
geom_line() +
stat_spikes(color = "red", z.threshold = 8, max.spike.width = 7)
Color from wavelength
This stat calculates the colour corresponding to each
-value
(assumed expressed in nanometres) and adds it to the data. It does not
summarize the data like stat_summary()
nor does it subset
the data like stat_peaks
, consequently the plot does not
require any additional geom to have all observations plotted.
It sets both color and fill aesthetics to a suitable
default.
ggplot(sun.spct) +
stat_color() + scale_color_identity()
All statistics that generate color definitions from wavelengths or
wavebands have a parameter, chroma.type
to which can be
used to select the color matching function or color coordinates to be
used. If we use chromaticity coordinates, "CC"
, instead of
the default color matching fucntion, "CMF"
, the apparent
luminance is not taken into account, only the hue.
ggplot(sun.spct) +
stat_color(chroma.type = "CC") + scale_color_identity()
We here show pseudo honey-bee vision colors. 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.
ggplot(clip_wl(sun.spct)) +
stat_color(chroma.type = beesxyzCMF.spct) + scale_color_identity()
By use of a filled shape and adding a black border by overriding the default color aesthetic and over-plotting these points on top of a line, we obtain a better separation from the background.
ggplot(sun.spct) +
geom_line() +
stat_color(shape = 21, color = "black") +
scale_fill_identity()
With a trick using many narrow bars we can fill the area under the line with a the calculated colours. This works satisfactorily as the data set has a small wavelength step, as in this case we are using a bar of uniform colour for each wavelength value in the data set.
ggplot(sun.spct) +
stat_color(geom = "bar") +
geom_line(color = "black") +
geom_point(shape = 21, color = "black", stroke = 1.2, fill = "white") +
scale_fill_identity() +
scale_color_identity() +
theme_bw()
ggplot(sun.spct) +
stat_color(geom = "bar", chroma.type = beesxyzCMF.spct) +
geom_line(color = "black") +
geom_point(shape = 21, color = "black", stroke = 1.2, fill = "white") +
scale_fill_identity() +
scale_color_identity() +
theme_bw()
As final example we demonstrate a plot with facets and shape based groups.
ggplot(two_suns.spct) + aes(shape = spct.idx) +
stat_color() + scale_color_identity() +
geom_line() +
facet_grid(cols = vars(spct.idx), scales = "free_y")
Averages and similar summaries
Our summary statistics are quite different to
ggplot2
s stat_summary()
. One could criticize
that they calculates summaries using a grouping that is not based on a
ggplot aesthetic. This is a deviation from the grammar
of graphics but allows the calculation of summaries for an arbitrary
region of the range of
-values
in the spectral data.
Summaries producing graphical elements
Three statistics generate only graphic output. First we
demonstrate stat_wb_box()
that produces a filled box for
each waveband, filled with the color corresponding to the waveband.
ggplot(sun.spct) + geom_line() +
stat_wb_box(w.band = VIS_bands(), color = "white") +
scale_fill_identity()
The statistics stat_wb_column
outputs a column
for each waveband, with an area equal to the integral for the
corresponding region of the spectrum.
ggplot(sun.spct) + stat_wb_column(w.band = VIS_bands()) + geom_line() +
scale_fill_identity()
The statistic stat_wd_hbar
outputs a horizontal
bar showing the mean spectral y-value for each waveband.
ggplot(sun.spct) + geom_line() +
stat_wb_hbar(w.band = VIS_bands(), size = 1.2) +
scale_color_identity()
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
These graphical summaries are frequently used together with text and label elements with names or numerical summaries.
Name labels
ggplot(sun.spct) + geom_line() +
stat_wb_box(w.band = PAR(), color = "white", ypos.fixed = 0.85) +
stat_wb_label(w.band = PAR(), ypos.fixed = 0.85) +
scale_fill_identity() + scale_color_identity()
Summaries by wavelength range
The summary is calculated for a range of wavelengths, and the
range
argument defaults to the range of wavelengths in each
group defined by other aesthetics. The default geom is
"text"
.
ggplot(sun.spct) + geom_line() + stat_wl_summary()
We can optionally supply an argument for range
to limit
the summary to a certain part of the spectrum, in which case the use of
the default "text"
geom is misleading. We add the
line last so that it is drawn on top of the rectangle.
ggplot(sun.spct) +
stat_wl_summary(range = c(300,350), geom = "rect") +
geom_line()
Here we show how to add horizontal line and label for the overall
mean, by adding the same stat twice, using different values for
geom
.
ggplot(sun.spct) +
geom_line() +
stat_wl_summary(geom = "hline", color = "red") +
stat_wl_summary(label.fmt = "Mean = %.3g", color = "red", vjust = -0.3)
Or we can add the aesthetic twice with two different geoms
to get the value plotted as a rectangular area and the value as
formatted text. We use vjust
to move the text above the end
of the bar, instead of it being centred on the value itself.
ggplot(sun.spct) +
stat_wl_summary(range = c(400,500), geom = "rect", alpha = 0.2, fill = color_of(450)) +
stat_wl_summary(range = c(400,500), label.fmt = "Mean = %.3g", vjust = -0.3, geom = "text") +
geom_line()
An example using the color
aesthetic for
grouping and moving the text label down. Setting
ggplot(two_suns.spct) + aes(color = spct.idx) +
geom_line() +
stat_wl_summary(geom = "hline") +
stat_wl_summary(label.fmt = "Mean = %.3g", vjust = 1.2, show.legend = FALSE) +
facet_grid(cols = vars(spct.idx))
Same example as above but using a free scale for , still working as expected.
ggplot(two_suns.spct) + aes(color = spct.idx) +
geom_line() +
stat_wl_summary(geom = "hline") +
stat_wl_summary(label.fmt = "Mean = %.3g", vjust = 1.2, show.legend = FALSE) +
facet_grid(cols = vars(spct.idx), scales = "free_y")
Means by waveband
Our stat_wb_mean()
is quite different to
ggplot2
s stat_summary()
. One could criticize
that it calculates summaries using a grouping that is not based on a
ggplot aesthetic. This is a deviation from the grammar
of graphics but allows the calculation of summaries for an arbitrary
waveband of the spectrum based on photobiology::waveband
objects, or lists of such objects. In contrast to
stat_wl_summary()
this allows the use of several ranges,
and also of different weighting functions. This function returns both
mean and total values for each waveband. It differs from
stat_wb_total()
only in the default aesthetics
set.
The first example uses a waveband
object created
on-the-fly and defining a range of wavelengths.
ggplot(sun.spct) +
geom_line() +
stat_wb_hbar(w.band = PAR(), size = 1.3) +
stat_wb_mean(aes(color = ..wb.color..), w.band = PAR(), ypos.mult = 0.95) +
scale_color_identity() +
scale_fill_identity() +
theme_bw()
## Warning: The dot-dot notation (`..wb.color..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(wb.color)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning in compute_group(...): BSWFs not supported by summary: using wavelength
## range for PAR'.
If a numeric vector or a spectrum is supplied as argument to
waveband
, its range is calculated and used to construct a
temporary waveband
object.
ggplot(sun.spct) +
stat_wb_hbar(w.band = c(400,500), size = 1.2) +
stat_wb_mean(aes(color = ..wb.color..),
w.band = c(400,500), ypos.mult = 0.95) +
geom_line() +
scale_color_identity() +
scale_fill_identity() +
theme_bw()
Lists of wavebands, either user-defined or as in this case using a list constructor can be also used.
ggplot(sun.spct) +
geom_line() +
stat_wb_hbar(w.band = list(Blue(), Red()), size = 1.2) +
stat_wb_mean(aes(color = ..wb.color..),
w.band = list(Blue(), Red()), ypos.mult = 0.95,
hjust = 1, angle = 90) +
scale_color_identity() +
scale_fill_identity() +
theme_bw()
Totals by waveband
Our stat_wb_total()
is quite different to
ggplot2
s stat_summary()
. One could criticize
that it calculates summaries using a grouping that is not based on a
ggplot aesthetic. This is a deviation from the grammar
of graphics but allows the calculation of summaries for an arbitrary
waveband of the spectrum based on photobiology::waveband
objects, or lists of such objects. In contrast to
stat_wl_summary()
this allows the use of several ranges,
and also of different weighting functions. This function returns both
mean and total values for each waveband. It differs from
stat_wb_mean()
only in the default aesthetics
set.
The first example uses a waveband
object created
on-the-fly and defining a range of wavelengths.
ggplot(sun.spct) +
stat_wb_box(w.band = PAR()) +
stat_wb_total(w.band = PAR()) +
geom_line() +
scale_color_identity() +
scale_fill_identity() +
theme_bw()
## Warning in compute_group(...): BSWFs not supported by summary: using wavelength
## range for PAR'.
If a numeric vector or a spectrum is supplied as argument to
waveband
, its range is calculated and used to construct a
temporary waveband
object.
ggplot(sun.spct) +
stat_wb_box(w.band = c(400,500)) +
stat_wb_total(w.band = c(400,500)) +
geom_line() +
scale_color_identity() +
scale_fill_identity() +
theme_bw()
Lists of wavebands, either user-defined or as in this case using
constructor defined in package photobiologyWavebands
can be
also used. In the case of totals, areas represent them graphically in a
very useful way.
ggplot(sun.spct * yellow_gel.spct) +
stat_wb_box(w.band = Plant_bands(), color = "white", ypos.fixed = 0.7) +
stat_wb_column(w.band = Plant_bands(), color = "white", alpha = 0.5) +
stat_wb_mean(w.band = Plant_bands(), label.fmt = "%1.2f",
ypos.fixed = 0.7, size = 2) +
geom_line() +
scale_fill_identity() + scale_color_identity() +
theme_bw()
Irradiances by waveband
The _stat_s in previous sections can be used for non-weighed
irradiances, but not for BSWFs. These irrad
_stat_s have
additional formal parameters for passing metadata, and use method
photobiology::irrad()
internally on a
photobiology::source_spct
object constructed from the data
for
and
aesthetics. Function stat_wb_sirrad()
is
equivalent to stat_wb_mean()
and function
stat_wb_irrad()
is equivalent to
stat_wb_total()
following the same naming convention as in
package photobiology
.
ggplot(sun.spct) +
stat_wb_box(w.band = PAR()) +
stat_wb_irrad(w.band = PAR(), unit.in = "energy", time.unit = "second") +
geom_line() +
scale_color_identity() +
scale_fill_identity() +
theme_bw()
ggplot(sun.spct, unit.out = "photon") +
stat_wb_box(w.band = PAR()) +
stat_wb_irrad(w.band = PAR(),
unit.in = "photon", time.unit = "second",
aes(label = sprintf("%s = %.3g", ..wb.name.., ..wb.yint.. * 1e6))) +
geom_line() +
scale_color_identity() +
scale_fill_identity() +
theme_bw()
Also following the same naming convention as in package
photobiology
, e
and q
versions of
these functions default to energy and photon based quantities to
"seconds"
for time unit.
ggplot(sun.spct) +
stat_wb_box(w.band = PAR()) +
stat_wb_e_irrad(w.band = PAR()) +
geom_line() +
scale_color_identity() +
scale_fill_identity() +
theme_bw()
We can also use waveband
objects describing
biological spectral weighting functions (BSWFs), such as CIE’s
erythema function.
ggplot(sun.spct) +
stat_wb_box(w.band = CIE()) +
stat_wb_e_irrad(w.band = CIE()) +
geom_line() +
scale_color_identity() +
scale_fill_identity() +
theme_bw()
For daily data we need to override the default time unit.
ggplot(sun.daily.spct) +
stat_wb_box(w.band = CIE()) +
stat_wb_e_irrad(w.band = CIE(), time.unit = "day", label.mult = 1e-3) +
geom_line() +
scale_color_identity() +
scale_fill_identity() +
theme_bw()
Here we pass a list of waveband
objects. And add areas
to facilitate comparisons as irradiances are integral over
wavelengths.
ggplot(sun.spct, unit.out = "photon") +
stat_wb_box(w.band = VIS_bands(), color = "black") +
stat_wb_column(w.band = VIS_bands(), color = NA, alpha = 0.5) +
stat_wb_q_irrad(w.band = VIS_bands(), label.mult = 1e6, size = 2) +
geom_line() +
scale_color_identity() +
scale_fill_identity() +
theme_bw()
Spectral irradiances by waveband
Spectral irradiances are equivalent to means, and can be best represented graphically as horizontal bars.
ggplot(sun.spct) +
geom_line() +
stat_wb_hbar(w.band = PAR(), size = 1.4) +
stat_wb_e_sirrad(aes(color = ..wb.color..),
w.band = PAR(), ypos.mult = 0.95) +
scale_color_identity() +
scale_fill_identity() +
theme_bw()
ggplot(sun.spct, unit.out = "photon") +
stat_wb_column(w.band = PAR(), alpha = 0.8) +
stat_wb_q_sirrad(w.band = PAR(),
mapping =
aes(label = sprintf("Total %s = %.3g",
..wb.name.., ..wb.yint.. * 1e6)),
ypos.mult = 0.55) +
stat_wb_q_sirrad(w.band = PAR(),
mapping =
aes(label = sprintf("Mean %s = %.3g",
..wb.name.., ..wb.ymean.. * 1e6)),
ypos.mult = 0.45) +
geom_line() +
scale_color_identity() +
scale_fill_identity() +
theme_bw()
ggplot(sun.spct) +
stat_wb_box(w.band = waveband(CIE()), ypos.fixed = 0.85) +
stat_wb_e_sirrad(w.band = CIE(), ypos.fixed = 0.85) +
geom_line() +
scale_color_identity() +
scale_fill_identity() +
theme_bw()
ggplot(sun.daily.spct) +
stat_wb_box(w.band = waveband(CIE()), ypos.fixed = 34e3) +
stat_wb_e_sirrad(w.band = CIE(),
label.fmt = "%.2g kj / day",
time.unit = "day",
ypos.fixed = 34e3) +
geom_line() +
scale_color_identity() +
scale_fill_identity() +
theme_bw()
my.bands <- split_bands(c(300,800), length.out = 10)
ggplot(sun.spct, unit.out = "photon") +
stat_wb_hbar(w.band = my.bands, size = 1.4) +
stat_wb_q_sirrad(geom = "label", w.band = my.bands,
size = 2.5, ypos.fixed = 3.5e-6) +
geom_line() +
scale_color_identity() +
scale_fill_identity() +
theme_bw()
Irradiances by waveband
ggplot(sun.spct) +
stat_wb_column(w.band = VIS_bands(), alpha = 0.5) +
stat_wb_e_irrad(w.band = VIS_bands(), angle = 90,
ypos.fixed = 0.05, hjust = 0,
aes(label = paste(..wb.name.., ..y.label.., sep = " = "))) +
geom_line() +
scale_color_identity() +
scale_fill_identity() +
theme_bw()
ggplot(sun.spct, unit.out = "photon") +
stat_wb_column(w.band = VIS_bands(), alpha = 0.5) +
stat_wb_q_irrad(w.band = VIS_bands(), angle = 90,
label.mult = 1e6, ypos.fixed = 1e-7, hjust = 0,
aes(label = paste(..wb.name.., ..y.label.., sep = " = "))) +
geom_line() +
scale_color_identity() +
scale_fill_identity() +
theme_bw()
my.bands <- split_bands(c(300,800), length.out = 10)
ggplot(sun.spct) +
stat_wb_column(w.band = my.bands, alpha = 0.5) +
stat_wb_e_irrad(w.band = my.bands, angle = 90,
ypos.fixed = 0.05, hjust = 0) +
geom_line() +
scale_color_identity() +
scale_fill_identity() +
theme_bw()
Labels from wavebands
Sometimes we may want to only annotate a plot with waveband names and
regions without calculating any summary quantity.
stat_wb_label()
fills this role. Although the same result
can be achieved with some of the summary statistics, if no summary is
needed this statistic can avoid unnecessary computations, and more
importantly used in plots where the
is not mapped, as only the
aesthetic is required.
ggplot(data.frame(w.length = 300:800), aes(w.length)) +
stat_wl_strip(w.band = VIS_bands(), ymax = Inf, ymin = -Inf) +
stat_wb_label(w.band = VIS_bands(), angle = 90) +
scale_fill_identity() +
scale_color_identity() +
scale_y_continuous(labels = NULL) +
scale_x_continuous(breaks = seq(from = 300, to = 800, by = 25)) +
labs(x = "Wavelength (nm)", title = "Colours according to ISO standard") +
theme_minimal()
We can also in a similar way make a plot comparing different sets of wavebands, in the next example we compare the VIS and NIR bands of the different imagers used in Landsat missions 1 to 8.
ggplot(data.frame(w.length = 300:1100), aes(w.length)) +
stat_wl_strip(w.band = RBV_bands(), ymax = 1, ymin = 3) +
stat_wb_label(w.band = RBV_bands(), ypos.fixed = 2, angle = 90, vjust = 0.3, size = 3) +
stat_wl_strip(w.band = MSS_bands(), ymax = 4, ymin = 6, na.rm = TRUE) +
stat_wb_label(w.band = MSS_bands(), ypos.fixed = 5, angle = 90, vjust = 0.3, size = 3) +
stat_wl_strip(w.band = ETM_bands(), ymax = 7, ymin = 9, na.rm = TRUE) +
stat_wb_label(w.band = ETM_bands(), ypos.fixed = 8, angle = 90, vjust = 0.3, size = 3) +
stat_wl_strip(w.band = OLI_bands(), ymax = 10, ymin = 12, na.rm = TRUE) +
stat_wb_label(w.band = OLI_bands(), ypos.fixed = 11, angle = 90, vjust = 0.3, size = 3) +
scale_fill_identity() +
scale_color_identity() +
scale_y_continuous(labels = c("RBV", "MSS", "TM/ETM", "OLI"),
breaks = c(2,5,8,11),
limits = c(0, 13),
name = "Imager",
sec.axis = dup_axis(labels = c("L1-L2", "L1-L5", "L4-L7", "L8"), name = "Landsat mission")) +
scale_x_continuous(breaks = seq(from = 300, to = 1200, by = 100),
limits = c(400, 1100),
sec.axis = dup_axis()) +
labs(x = "Wavelength (nm)", title = "Landsat imagers: VIS and NIR bands") +
theme_classic()
The different definitions of the ultraviolet regions.
ggplot(data.frame(w.length = 100:400), aes(w.length)) +
stat_wl_strip(w.band = UV_bands("ISO"), ymax = 1, ymin = 3, color = "white") +
stat_wb_label(w.band = UV_bands("ISO"), ypos.fixed = 2, size = 3) +
stat_wl_strip(w.band = UV_bands("CIE"), ymax = 4, ymin = 6, color = "white") +
stat_wb_label(w.band = UV_bands("CIE"), ypos.fixed = 5, size = 3) +
stat_wl_strip(w.band = UV_bands("plants"), ymax = 7, ymin = 9, color = "white") +
stat_wb_label(w.band = UV_bands("plants"), ypos.fixed = 8, size = 3) +
stat_wl_strip(w.band = UV_bands("none"), ymax = 10, ymin = 12, color = "white") +
stat_wb_label(w.band = UV_bands("none"), ypos.fixed = 11, size = 3) +
stat_wl_strip(w.band = UV_bands("medical"), ymax = 13, ymin = 15, color = "white") +
stat_wb_label(w.band = UV_bands("medical"), ypos.fixed = 14, size = 3) +
scale_fill_identity() +
scale_color_identity() +
scale_y_continuous(labels = c("ISO", "CIE", "plants", "none", "medical"),
breaks = c(2,5,8,11,14),
limits = c(0, 16),
name = "Definition",
sec.axis = dup_axis(labels =
c("use", "use", "use?", "avoid!", "avoid!"), name = "Recommendation")) +
scale_x_continuous(breaks = c(seq(from = 100, to = 400, by = 50), 280, 315),
limits = c(100, 400),
sec.axis =
dup_axis(breaks =
c(100, 150, 200, 220, 250, 290, 320, 340, 400))) +
labs(x = "Wavelength (nm)", title = "UV bands",
subtitle = "According to ISO standard, CIE recommendations, and non-standard use") +
theme_classic()
It should be noted that this approach is rather different from the way one would normally plot a horizontal bar plot with the grammar of graphics of ‘ggplot2’, but for plottings existing waveband definitions it is convenient and ensures that the waveband definitions agree with those used for computing summary quantities elsewhere.
Wavelength colours at new x-values
The stat_wl_summary()
and stat_wb_mean()
return a reduced data set with fewer rows than the original
data. stat_wl_strip()
depending on the input, can also
return a data set with more rows than the input data. This is the
default behaviour, with w.band
with argument
NULL
. This stat operates only on the variable
mapped to the x aesthetic, a y-mapping is not
required as input.
my.data <- data.frame(x = 300:800)
ggplot(my.data, aes(x)) + stat_wl_strip(ymin = -1, ymax = 1, color = NA) +
scale_fill_identity()
The default geom is "rect"
which with suitable
mappings of ymin
and ymax
aesthetics
can be used to add a colour guide to the x-axis (if its scale
maps wavelengths in nanometres). When w.band
is
NULL
a long series of contiguous wavebands is generated to
create the illusion of a continuous colour gradient.
ggplot(sun.spct) +
geom_line() +
stat_wl_strip(ymin = -Inf, ymax = -0.025) +
scale_fill_identity() +
theme_bw()
When a list of wavebands is supplied, it is used for the calculation of colours. These calculations do not use the spectral irradiance, they simply assume a flat spectrum, so the represent the colours of the wavelengths, rather than the colour of the light described by the spectral irradiance.
ggplot(sun.spct) +
geom_line() +
stat_wl_strip(w.band = VIS_bands(), ymin = -Inf, ymax = -0.025) +
scale_fill_identity() +
theme_bw()
stat_wl_strip()
can also used to produce a background
layer with colours corresponding to wavebands. Here we use
alpha = 0.5
to add transparency.
ggplot(sun.spct) +
stat_wl_strip(w.band = VIS_bands(), ymin = -Inf, ymax = Inf, alpha = 0.4) +
scale_fill_identity() +
geom_line() +
theme_bw()
By default an almost continuous colour gradient can be generated for the background.
ggplot(sun.spct) +
stat_wl_strip(alpha = 0.4, ymin = -Inf, ymax = Inf) +
scale_fill_identity() +
geom_line() +
theme_bw()
Multiple stats in the same plot
The stats described above can be used together to produce
more complex plots. This is just one possibility out of a vast range. Be
aware that when adding many layers to the same plot, the order of the
layers and adjusting their transparency using the alpha
aesthetic is very important, and best tuned by trial and
error.
ggplot(sun.spct, unit.out = "photon") +
stat_wl_strip(alpha = 0.4, ymin = -Inf, ymax = Inf) +
stat_wb_box(w.band = PAR()) +
stat_wb_total(w.band = PAR(), label.mult = 1e6,
aes(label = paste(..wb.name.., " = ", ..y.label.., sep = ""))) +
geom_line() +
scale_fill_identity() + scale_color_identity() +
theme_bw()
## Warning in compute_group(...): BSWFs not supported by summary: using wavelength
## range for PAR'.
Geometries
A simple geom called "spct"
is defined. It
differs from geom "area"
only in the default
position
which is "stacked"
for geom
"area"
and "identity"
for geom
"spct"
which is usually better for spectra.
Using as fill the color calculated from the spectral data is different to earlier examples in that the spectral irradiance is actually taken into account in the calculation of the colour, instead of just the wavelength range.
Packaged stats, geoms plus scales
Some of the examples in previous sections can be simplified by use of
wl_guide()
and wb_guide()
which add both the
statistics and the scale, and provide a default mapping for
both ymin
and ymax
. This is handy for simple
plots, but it can generate spurious warnings due to the replacement of
existing scales with identical ones. In general only one of
wl_guide()
or wb_guide()
should be used in a
plot, and each one, only once. They just save some typing for the
simplest uses, but are not suitable for complex
decorations.
wl_guide
The examples for stat_wl_strip
can be simplified by use
of wl_guide()
which adds both the statistics and the scale,
and provides default mappings for both ymin
(-Inf
) and ymax
(Inf
). Another
important difference is that the "rect"
geom used
cannot be set to a different one by the user.
This example produces a plot with a very different look than earlier ones, but can also be achieved with a very succinct statement.
In the next example we demonstrate a trick achieved by over-plotting
two lines of different width (size
) to make the spectrum
visible on a background of variable luminosity and colour.
Color charts
color_chart(grep("blue", colors(), value = TRUE), ncol = 5, text.size = 4)
color_chart(w_length2rgb(570:689, color.name = as.character(570:689)),
use.names = TRUE, text.size = 4) +
ggtitle("Reddish colors", subtitle = "Labels: wavelength (nm)")