---
title: "User Guide: 1 Grammar of Graphics"
subtitle: "'ggspectra' `r packageVersion('ggspectra')`"
author: "Pedro J. Aphalo"
date: "`r Sys.Date()`"
output: 
  rmarkdown::html_vignette:
    toc: yes
vignette: >
  %\VignetteEncoding{UTF-8}
  %\VignetteIndexEntry{User Guide: 1 Grammar of Graphics}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  markdown: 
    wrap: 72
---

## 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

```{r, message=FALSE}
library(ggplot2)
library(scales)
library(photobiology)
library(photobiologyWavebands)
library(ggspectra)
library(ggrepel)
```

```{r, include=FALSE, echo=FALSE}
library(knitr)
opts_chunk$set(fig.align = 'center', 
               fig.show = 'hold', fig.width = 7, fig.height = 4,
               cache = FALSE)
options(warnPartialMatchArgs = FALSE)
```

Create a collection of two source_spct objects.

```{r}
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.

```{r}
two_suns.spct <- rbindspct(two_suns.mspct)
```

We change the default theme.

```{r}
theme_set(theme_bw())
```

## ggplot() methods for spectra

The only difference between these specializations and the base
`ggplot()` method is that the aesthetics for $x$ and $y$ have suitable
defaults. These are just defaults, so if needed they can still be
supplied with a `mapping` argument with an user-defined `aes()`.

```{r}
ggplot(sun.spct) + geom_line()
```

It is possible to add to the defaults by means of `+` and `aes()` as
shown below.

```{r}
ggplot(two_suns.spct) + aes(color = spct.idx) + geom_line()
```

If a mapping is supplied directly through `ggplot`, $x$ and $y$ should
be included.

```{r}
ggplot(two_suns.spct, aes(w.length, s.e.irrad, color = spct.idx)) + geom_line()
```

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 $y$ 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.

```{r}
ggplot(sun.spct, unit.out = "photon") + geom_line()
```

After evaluation of `photon_as_default()`, a new default is in effect,
but we can override it with an explicit argument if needed.

```{r}
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.

```{r}
unset_user_defaults()
```

The next example is for spectral properties of filters.

```{r}
ggplot(yellow_gel.spct) + geom_line()
```

In the case of `ggplot.filter_spct()` the additional parameter is called
`plot.qty` and allows choosing between `"transmittance"` and
`"absorbance"`.

```{r}
ggplot(yellow_gel.spct, plot.qty = "absorbance") + geom_line()
```

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.

```{r}
Afr_as_default()
ggplot(yellow_gel.spct) + geom_line()
unset_user_defaults()
```

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.

```{r}
ggplot(two_suns.mspct) + 
  aes(linetype = spct.idx) +
  wl_guide(ymax = -0.05) +
  geom_line()
```

Using facets.

```{r}
ggplot(two_suns.mspct) + 
  wl_guide(ymax = -0.05) +
  geom_spct() +
  geom_line() +
  facet_wrap(facets = vars(spct.idx), ncol = 1L)
```

```{r}
ggplot(two_suns.mspct) + 
  wl_guide(ymax = -0.05) +
  geom_spct() +
  geom_line() +
  facet_wrap(vars(spct.idx), ncol = 1L, scales = "free_y")
```

## 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.

```{r}
ggplot(sun.spct) + 
  geom_line() +
  scale_x_wl_continuous() +
  scale_y_s.e.irrad_continuous()
```

Here we change the `unit.exponent` from its default value of `-9`
(nanometres) to `-6` (micrometers) for wavelengts, 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.**

```{r}
ggplot(sun.spct) + 
  geom_line() +
  scale_x_wl_continuous(unit.exponent = -6) +
  scale_y_s.e.irrad_continuous(unit.exponent = -3)
```

```{r}
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.

```{r}
nearest_SI_exponent(-4)
```

We can call this function on-the-fly.

```{r}
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.)

```{r}
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.

```{r}
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).

```{r}
ggplot(sun.spct) + 
  geom_line() +
  scale_x_wl_continuous(label.text = "Longitud de onda,") +
  scale_y_s.e.irrad_continuous(label.text = "Irradiancia,")
```

If the data are normalized, we can pass the normalization wavelength as
an argument. In this example, we retrieve this wavelength from the metadata.

```{r}
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.

```{r}
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 wavenumber, 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._

```{r}
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.

```{r}
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.

```{r}
ggplot(sun.spct) + 
  geom_line() +
  scale_x_wl_continuous(sec.axis = sec_axis_w_frequency())
```

```{r}
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.

```{r}
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.

```{r}
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.

```{r}
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.

```{r}
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.

```{r}
ggplot(sun.spct) + 
  geom_line() +
  scale_x_wl_continuous() +
  scale_y_s.e.irrad_continuous()
```

```{r}
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.

```{r}
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.

```{r}
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.

```{r}
ggplot(yellow_gel.spct) + 
  geom_line() +
  scale_x_wl_continuous() +
  scale_y_Tfr_continuous(Tfr.type = getTfrType(yellow_gel.spct))
```

```{r}
ggplot(yellow_gel.spct) + 
  geom_line() +
  scale_x_wl_continuous() +
  scale_y_Tfr_continuous(Tfr.type = getTfrType(yellow_gel.spct),
                         labels = percent)
```

```{r}
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))
```

### 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.*

```{r}
ggplot(gel_internal.spct, plot.qty = "absorbance") + 
  geom_line() +
  scale_x_wl_continuous() +
  scale_y_A_continuous(Tfr.type = getTfrType(gel_internal.spct))
```

### Absorptance

Absorptance has only one definition, at least within this package.

```{r}
ggplot(yellow_gel.spct, plot.qty = "absorptance") + 
  geom_line() +
  scale_x_wl_continuous() +
  scale_y_Afr_continuous()
```

### 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.

```{r}
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 $x$ 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()`.

```{r}
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.

```{r}
ggplot(sun.spct, unit.out = "photon") + geom_line() + stat_peaks(color = "red")
```

The complement to `stat_peaks()` is `stat_valleys`.

```{r}
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.

```{r}
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.

```{r}
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.

```{r}
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.

```{r}
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`.

```{r}
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.

```{r}
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.

```{r}
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`.

```{r}
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()
```

```{r}
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.

```{r}
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()
```

```{r}
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.

```{r}
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.

```{r}
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*.

```{r}
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))
```

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'.)

```{r}
ggplot(white_led.raw_spct, aes(w.length, counts_3)) + 
  geom_line() + 
  stat_spikes(color = "red", z.threshold = 8, max.spike.width = 7)
```

```{r}
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 $x$-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.

```{r}
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.

```{r}
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.

```{r}
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.

```{r}
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.

```{r}
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()
```

```{r}
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.

```{r}
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 $x$-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.

```{r}
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.

```{r}
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.

```{r}
ggplot(sun.spct) + geom_line() + 
  stat_wb_hbar(w.band = VIS_bands(), size = 1.2) +
  scale_color_identity()
```

These graphical summaries are frequently used together with text and
label elements with names or numerical summaries.

### Name labels

```{r}
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"`.

```{r}
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.

```{r}
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`.

```{r}
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.

```{r}
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

```{r}
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 $y$, still working as
expected.

```{r}
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.

```{r}
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()
```

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.

```{r}
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.

```{r}
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.

```{r}
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()
```

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.

```{r}
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.

```{r}
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 $x$ and $y$ *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`.

```{r}
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()
```

```{r}
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.

```{r}
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.

```{r}
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.

```{r}
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.

```{r}
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.

```{r}
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()
```

```{r}
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()
```

```{r}
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()
```

```{r}
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()
```

```{r}
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

```{r}
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()
```

```{r}
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()
```

```{r}
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
$y$ is not mapped, as only the $x$ aesthetic is required.

```{r}
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.

```{r}
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.

```{r}
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.

```{r}
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.

```{r}
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.

```{r}
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.

```{r}
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.

```{r}
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.

```{r}
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()
```

## 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.

```{r}
ggplot(sun.spct) + geom_spct()
```

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.

```{r}
ggplot(sun.spct) + 
  geom_spct(fill = color_of(sun.spct))
```

```{r}
ggplot(sun.spct * yellow_gel.spct) + 
  geom_spct(fill = color_of(sun.spct * yellow_gel.spct))
```

## 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.

```{r}
ggplot(sun.spct) + 
  wl_guide(alpha = 0.4) +
  geom_line()
```

```{r}
ggplot(sun.spct) + 
  wl_guide(ymax = -0.025) +
  geom_line() 
```

This example produces a plot with a very different look than earlier
ones, but can also be achieved with a very succinct statement.

```{r}
ggplot(sun.spct) + 
  wl_guide() +
  geom_spct(alpha = 0.75, colour = "white", size = 1) 
```

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.

```{r}
ggplot(sun.spct) + 
  wl_guide() +
  geom_line(size = 2, colour = "white") +
  geom_line(size = 1, colour = "black") +
  geom_hline(yintercept = 0, colour = "grey92") +
  theme_bw()
```

## Color charts

```{r}
color_chart(colors())
```

```{r}
color_chart(grep("blue", colors(), value = TRUE), ncol = 5, text.size = 4)
```

```{r}
 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)")
```
