Introduction to spectrolab

Jose Eduardo Meireles, Anna K. Schweiger, and Jeannine Cavender-Bares

January 2025

spectrolab provides methods to read, process, and visualize data from portable spectroradiometers. The package also establishes a common interface for spectra by introducing a spectra S3 class.

spectrolab packs a ton of functionality:

The source code can be found on our GitHub repository. Please report any bugs and ask us your questions through the issue tracker.

Installing and loading spectrolab

The latest stable version of spectrolab is on CRAN. Install it with:

install.packages("spectrolab")

You can also install it directly from GitHub using the devtools package.

library("devtools")
install_github("meireles/spectrolab")

Assuming that everything went smoothly, you should be able to load spectrolab like any other package.

library("spectrolab")

Reading spectra

There are two ways to get spectra into R using spectrolab:

  1. Reading spectra from raw data files (formats: SVC’s sig, Spectral Evolution’s sed and ASD’s asd).
  2. Converting a matrix or data.frame to spectra.

Reading spectra from raw data files: Example with SVC’s .sig

Use the function read_spectra() to read your spectroradiometer’s files. You can pass a vector of file names to read_spectra() but it is usually easier to pass the path to the folder where your data are. Note that spectrolab cannot read nested folders or read a mix of file types at once (i.e. having .sig and .sed files in the same folder will produce an error).

# `dir_path` is the directory where our example datasets live
dir_path = system.file("extdata/Acer_example", package = "spectrolab")

# Read spectra from .sig files inside the "extdata/Acer_example" folder
acer_spectra = read_spectra(path = dir_path)

spectrolab guesses the file format automatically (but you can provide the format using the format argument if needed). The package reads the target’s relative reflectance – the ratio between the target’s radiance and the reference’s radiance – by default. However, you can read the target’s or reference’s radiances using the argument type.

# Reading the target's radiance
acer_spectra_rad = read_spectra(path = dir_path, format = "sig", type = "target_radiance")

# And the white reference's radiance
acer_white_ref = read_spectra(path = dir_path, type = "reference_radiance")

You can avoid importing undesirable spectra if those were flagged in the field. For example, we usually add the suffixes “_WR” to denote white reference and “_BAD” to denote bad measurements, so we can pass those flags to the argument exclude_if_matches in read_spectra().

# Use the `exclude_if_matches` argument to excluded flagged files
acer_spectra = read_spectra(path = dir_path, exclude_if_matches = c("BAD","WR"))

Finally, spectrolab lets you read the metadata from sig and sed files if you need to. Simply set extract_metadata to TRUE.

# Use the `exclude_if_matches` argument to excluded flagged files
acer_spectra_with_meta = read_spectra(path = dir_path,
                                      exclude_if_matches = c("BAD","WR"),
                                      extract_metadata = TRUE)

# Here are fields 2 to 6 of the metadata for the first 3 scans.
# More on the `meta` function later.

meta(acer_spectra_with_meta)[1:3, 2:6]
##               instrument integration1 integration2 integration3 scan method
## 1 HI: 1152050 (HR-1024i)          200           30            7  Time-based
## 2 HI: 1152050 (HR-1024i)          200           30            7  Time-based
## 3 HI: 1152050 (HR-1024i)          150           30            7  Time-based

Create spectra from a matrix or data.frame

If you already have your spectra in a matrix or data frame (e.g. when you read your data from a .csv file), you can use the function as_spectra() to convert it to a spectra object. The matrix must have samples in rows and bands in columns. The header of the bands columns must be (numeric) band labels. You also should declare which column has the sample names (which are mandatory) using the name_idx argument. If other columns are present (other than sample name and values), their indices must be passed to as_spectra as the meta_idxs argument.

Here is an example using a dataset matrix named spec_matrix_meta.csv provided by the package.

dir_path = system.file("extdata/spec_matrix_meta.csv", package = "spectrolab")

# Read data from the CSV file. If you don't use `check.names` = FALSE when reading
# the csv, R will usually add a letter to the column names (e.g. 'X650') which will 
# cause problems when converting the matrix to spectra.
spec_csv = read.csv(dir_path, check.names = FALSE)

# The sample names are in column 3. Columns 1 and 2 are metadata
achillea_spec = as_spectra(spec_csv, name_idx = 3, meta_idxs = c(1,2) )

# And now you have a spectra object with sample names and metadata...
achillea_spec
## spectra object 
## number of samples: 10 
## bands: 400 to 2400 (2001 bands)
## metadata (2): ident, ssp
## 
##         400        401        402        403        404        405       
## ACHMI_1 0.03734791 0.03698631 0.03804012 0.03948724 0.03952211 0.038474  
## ACHMI_2 0.04608409 0.04536371 0.04436544 0.04355212 0.04415447 0.04424556
## ACHMI_3 0.04058113 0.04025678 0.03958125 0.03969706 0.03978786 0.03818825
## ACHMI_4 0.0406373  0.0399342  0.03793455 0.03742931 0.03798945 0.03791815
## ACHMI_5 0.03900349 0.04013091 0.04104385 0.04088704 0.03885306 0.03675817
##         406        ...
## ACHMI_1 0.03805137    
## ACHMI_2 0.04209803    
## ACHMI_3 0.03519675    
## ACHMI_4 0.03656809    
## ACHMI_5 0.03693254

Inspecting and querying spectra

You can check out your spectra object in several ways. For instance, You may want to know how many spectra and how many bands are in there, retrieve the file names, etc. Of course you will need to plot the data, but that topic gets its own section further down.

# Simply print the object
acer_spectra
## spectra object 
## number of samples: 7 
## bands: 340.5 to 2522.8 (1024 bands, **overlap not matched**)
## metadata: none 
## 
##                        340.5  342    343.4  344.9  346.3  347.8  349.3  ...
## ACPL_D2_P1_B_1_001.sig 0.0613 0.075  0.1011 0.1    0.066  0.0614 0.0574    
## ACPL_D2_P1_B_2_001.sig 0.0613 0.0833 0.1167 0.1143 0.0991 0.0737 0.0459    
## ACPL_D2_P1_M_1_000.sig 0.07   0.0889 0.083  0.0762 0.0616 0.0655 0.0765    
## ACPL_D2_P1_M_2_000.sig 0.0963 0.0917 0.0544 0.05   0.0528 0.0614 0.0459    
## ACPL_D2_P1_T_1_000.sig 0.0788 0.0917 0.0778 0.0857 0.0925 0.0737 0.0746
# Get the dataset's dimensions
dim(acer_spectra)
## n_samples   n_bands 
##         7      1024

spectrolab also lets you access the individual components of the spectra. This is done with the functions names() for sample names, bands() for band labels, value() for the value matrix, and meta() for the associated metadata (in case you have any).

# Vector of all sample names. Note: Duplicated sample names are permitted
n = names(achillea_spec)

# Vector of bands
w = bands(achillea_spec)

# value matrix
r = value(achillea_spec)

# Metadata. Use simplify = TRUE to get a vector instead of a data.frame
m = meta(achillea_spec, "ssp", simplify = TRUE)

Subsetting spectra

You can subset the spectra using a notation similar to the [i, j] function used in matrices and data.frames. The first argument in [i, ] matches sample names, whereas the second argument [ , j] matches the band names. Here are some examples of how [ works in spectra:

Subsetting lets you, for instance, exclude noisy regions at the beginning and end of the spectrum or limit the data to specific entries.

# Subset band regions. Here we know that bands are integers (e.g. 400, 401, ...)
spec_sub_vis = achillea_spec[ , 400:700 ]

# Subset spectra to all entries where sample_name matches "ACHMI_7" or
# get the first three samples
spec_sub_byname = achillea_spec["ACHMI_7", ]
spec_sub_byidx  = achillea_spec[ 1:3, ]

The resolution of some spectra may be different from 1nm. In those cases, the best way to subset spectra is using the min and max arguments for bands:

acer_spectra_trim = acer_spectra[ , bands(acer_spectra, 400, 2400) ]

Note that you can (1) subset samples using indexes and (2) use characters or numerics to subset bands. As said before, you cannot use indexes to subset bands though.

# Subsetting samples by indexes works and so does subsetting bands by numerics or characters.
spec_sub_byidx[1, "405"] == spec_sub_byidx[1, 405]
## ACHMI_1 
##    TRUE

But remember that you CANNOT use indexes to subset bands!

# Something that is obvioulsy an index, like using 2 instead of 401 (the 2nd band 
# in our case), will fail.
spec_sub_byidx[ , 2]
## Error: 1 labels not found: 2
`Error in i_match_ij_spectra(this = this, i = i, j = j) : band subscript out of bounds. Use band labels instead of raw indices.`
## Error: object 'Error in i_match_ij_spectra(this = this, i = i, j = j) : band subscript out of bounds. Use band labels instead of raw indices.' not found

Plotting

The workhorse function for statically plotting spectra is plot(). It will jointly plot each spectrum in the spectra object. You should be able to pass the usual plot arguments to it, such as col, ylab, lwd, etc.

You can also plot the quantiles of a spectra object with plot_quantile(). It’s second argument, total_prob, is the total “mass” that the quantile encompasses. For instance, a total_prob = 0.95 covers 95% of the variation in the spectra object, i.e. it is the 0.025 to 0.975 quantile. The quantile plot can stand alone or be added to a current plot if add = TRUE.

The function plot_regions() helps shading different spectral regions. spectrolab provides a default_spec_regions() matrix as an example, but you obviously can customize it for your needs (see the help page for plot_regions for details).

# Simple spectra plot
oldpar = par(no.readonly = TRUE)

par(mfrow = c(1, 3))
plot(achillea_spec, lwd = 0.75, lty = 1, col = "grey25", main = "All Spectra")

# Stand along quantile plot
plot_quantile(achillea_spec, total_prob = 0.8, col = rgb(1, 0, 0, 0.5), lwd = 0.5, border = TRUE)
title("80% spectral quantile")

# Combined individual spectra, quantiles and shade spectral regions
plot(achillea_spec, lwd = 0.25, lty = 1, col = "grey50", main="Spectra, quantile and regions")
plot_quantile(achillea_spec, total_prob = 0.8, col = rgb(1, 0, 0, 0.25), border = FALSE, add = TRUE)
plot_regions(achillea_spec, regions = default_spec_regions(), add = TRUE)

par(oldpar)

Last but not least, spectrolab also allows you to interactively explore spectra through a shiny app with the plot_interactive() function.

Manipulating samples names, band labels, metadata and value

You may want to edit certain simple attributes of spectra, such as making all sample names lowercase This is easily attainable in spectrolab.

spec_new = achillea_spec

# Replace names with a lowercase version
names(spec_new) = tolower(names(achillea_spec))

# Check the results
names(spec_new)[1:5]
## [1] "achmi_1" "achmi_2" "achmi_3" "achmi_4" "achmi_5"

If you want to fiddle with the value itself, this is easy, too.

# Scale value by 0.75
spec_new = spec_new * 0.75

# Plot the results
plot(achillea_spec, col = "blue", lwd = 0.75, cex.axis = 0.75)
plot(spec_new, col = "orange", lwd = 0.75, add = TRUE)

Or you can also edit or add new metadata to the spectra object.

## Adding metadata to a spectra object: a dummy N content
n_content = rnorm(n = nrow(achillea_spec), mean = 2, sd = 0.5)
meta(achillea_spec, label = "N_percent") = n_content

Converting a spectra object into a matrix or data.frame

It is also possible to convert a spectra object to a matrix or data.frame using the as.matrix() or as.data.frame() functions. This is useful if you want to export your data in a particular format, such as csv.

If you’re converting spectra to a matrix, spectrolab will (1) place bands in columns, assigning band labels to colnames, and (2) samples in rows, assigning sample names to rownames. Since R imposes strict rules on column name formats and sometimes on row names, as.matrix() will try to fix potential dimname issues if fix_names != "none". Note that as.matrix() will not keep metadata.

Conversion to data.frame is similar, but keeps the metadata by default (unless you set the metadata argument to FALSE).

# Make a matrix from a `spectra` object
spec_as_mat = as.matrix(achillea_spec, fix_names = "none")
spec_as_mat[1:4, 1:3]
##                400        401        402
## ACHMI_1 0.03734791 0.03698631 0.03804012
## ACHMI_2 0.04608409 0.04536371 0.04436544
## ACHMI_3 0.04058113 0.04025678 0.03958125
## ACHMI_4 0.04063730 0.03993420 0.03793455
# Make a matrix from a `spectra` object
spec_as_df = as.data.frame(achillea_spec, fix_names = "none", metadata = TRUE)
spec_as_df[1:4, 1:5]
##   sample_name ident                   ssp N_percent        400
## 1     ACHMI_1 10526 Achillea millefolium   1.745752 0.03734791
## 2     ACHMI_2 10527 Achillea millefolium   1.431044 0.04608409
## 3     ACHMI_3 10528 Achillea millefolium   2.376328 0.04058113
## 4     ACHMI_4 10529 Achillea millefolium   2.399807 0.04063730