Introduction
Purpose
The purpose of this vignette is to describe the structure and methods of objects of class ir. ir objects are used by the ‘ir’ package to store spectra and their metadata. This vignette could be helpful if you want to understand better how the ‘ir’ package works, how to handle metadata, how to manipulate ir objects, or if you want to construct a subclass based on the irclass.
This vignette does not give an overview on how to use the ‘ir’ package, on functions for spectral preprocessing, and on how to plot ir objects. For this, see vignette Introduction to the ‘ir’ package.
Structure
This vignette has three parts:
- The
irclass - Subsetting and modifying
irobjects - Special functions to manipulate
ir objects
In part The ir class, I will describe the structure of ir objects and list available methods for it.
In part Subsetting and modifying ir objects, I will show how ir objects can be subsetted and modified (including tidyverse functions).
In part Special functions to manipulate ir objects, I will present some more specialized functions to manipulate the data in ir objects (including the spectra).
The ir class
Objects of class ir are in principle data frames (or tibbles):
ir_sample_dataEach row represents one measurement for a spectrum. The ir object must a column spectra which is a list of data frames, each element representing a spectrum.
Besides this, ir objects may have additional columns with metadata. This is useful to analyze spectra of samples in an integrated way with other data, for example nitrogen content (see part Subsetting and modifying ir objects).
The spectra column is a list of data frames, each element representing a spectrum. The data frames have a row for each intensity values measured for a spectral channel (“x axis value”, e.g. wavenumber) and a column x storing the wavenumber values and a column y storing the respective intensity values. No additional columns are allowed:
head(ir_sample_data$spectra[[1]])
#> # A tibble: 6 × 2
#> x y
#> <int> <dbl>
#> 1 4000 0.000361
#> 2 3999 0.000431
#> 3 3998 0.000501
#> 4 3997 0.000571
#> 5 3996 0.000667
#> 6 3995 0.000704If there is no spectrum available for a sample, an empty data frame is a placeholder:
d <- ir_sample_data
d$spectra[[1]] <- d$spectra[[1]][0, ]
d$spectra[[1]]
ir_normalize(d, method = "area")#> # A tibble: 0 × 2
#> # … with 2 variables: x <int>, y <dbl>Currently, the following methods are available for ir objects:
methods(class = "ir")
#> [1] [ [[ [[<- [<- $ $<- cbind filter
#> [9] ir_as_ir max median min Ops plot range rbind
#> [17] rep
#> see '?methods' for accessing help and source codeSubsetting and modifying ir objects
Subsetting works as for data frames
Since ir objects are data frames, subsetting and modifying works the same way as for data frames. For example, specific rows (= measurements) can be filtered:
ir_sample_data[5:10, ]The advantage of storing spectra as list columns is that filtering spectral data and metadata and other data can be performed simultaneously.
One exception is that while subsetting, one must not remove the spectra column. If it is removed, the ir class attribute is dropped:
d1 <- ir_sample_data
class(d1[, setdiff(colnames(d), "id_sample")])
#> [1] "ir" "tbl_df" "tbl" "data.frame"
d1$spectra <- NULL
class(d1)
#> [1] "tbl_df" "tbl" "data.frame"Another exception is that when the spectra column contains unsupported elements (e.g. wrong column names, additional columns, duplicated “x axis values”), the object also loses its ir class:
Tidyverse methods are supported
Tidyverse methods for manipulating ir objects are also supported. For example, we can use mutate to add new variables and we can use pipes (%>%) to make coding and reading code easier:
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
d <- ir_sample_data
d <-
d %>%
mutate(a = rnorm(n = nrow(.)))
head(ir_sample_data)Or, a another example, we can summarize spectra for some defined groups (here the maximum intensity value for each “x axis value” and unique sample_type value):
library(purrr)
library(ggplot2)
d2 <-
d %>%
group_by(sample_type) %>%
summarize(
spectra = {
res <- map_dfc(spectra, function(.x) .x[, 2, drop = TRUE])
spectra[[1]] %>%
dplyr::mutate(
y =
res %>%
rowwise() %>%
mutate(y = max(c_across(everything()))) %>%
pull(y)
) %>%
list()
},
.groups = "drop"
)
plot(d2) +
facet_wrap(~ sample_type)
Special functions to manipulate ir objects
There are some more special functions to manipulate ir objects which are not described in vignette Introduction to the ‘ir’ package. These will be described here.
Replicating data
Sometimes, it is useful to replicate one or multiple measurements. This can be done with the rep() method for ir objects. For example, we can replicate the second spectrum in ir_sample_data:
Calculating with spectra
The ir packages supports arithmetic operations with spectra, i.e. addition, subtraction, multiplication, and division of intensity values with the same “x axis values”. For example, we can subtract the third spectrum in ir_sample_data from the second:
ir_sample_data %>%
slice(2) %>%
ir_subtract(y = ir_sample_data[3, ]) %>%
dplyr::mutate(id_sample = "subtraction_result") %>%
rbind(ir_sample_data[2:3, ]) %>%
plot() + facet_wrap(~ id_sample)
Note that all metadata of the first argument (x) will be retained, but not of the second (y). This is why we had to manually change id_sample before rbinding the other spectra above. Note also that x can contain multiple spectra, y must either only contain one spectrum or the same number of spectra as x in which case spectra of matching rows are subtracted (added, multiplied, divided):
# This will not work
ir_sample_data %>%
slice(6) %>%
ir_add(y = ir_sample_data[3:4, ])
#> Error in `ir_add()`:
#> ! `y` must either have only one row or as many rows as `x`.
# but this will
ir_sample_data %>%
slice(2:6) %>%
ir_add(y = ir_sample_data[3, ])
#> # A tibble: 5 × 7
#> id_measurement id_sample sample_type sample_comment klason_lignin
#> * <int> <chr> <chr> <chr> <units>
#> 1 2 GN 11-400 needles Cupressocyparis leylandii … 0.339405
#> 2 3 GN 11-407 needles Juniperus chinensis Chines… 0.267552
#> 3 4 GN 11-411 needles Metasequoia glyptostroboid… 0.350016
#> 4 5 GN 11-416 needles Pinus strobus Torulosa 0.331100
#> 5 6 GN 11-419 needles Pseudolarix amabili Golden… 0.279360
#> # … with 2 more variables: holocellulose <units>, spectra <list>Note that arithmetic operations are also available as infix operators, i.e. it is possible to compute:
ir_sample_data[2, ] + ir_sample_data[3, ]
#> # A tibble: 1 × 7
#> id_measurement id_sample sample_type sample_comment klason_lignin
#> * <int> <chr> <chr> <chr> <units>
#> 1 2 GN 11-400 needles Cupressocyparis leylandii … 0.339405
#> # … with 2 more variables: holocellulose <units>, spectra <list>
ir_sample_data[2, ] - ir_sample_data[3, ]
#> # A tibble: 1 × 7
#> id_measurement id_sample sample_type sample_comment klason_lignin
#> * <int> <chr> <chr> <chr> <units>
#> 1 2 GN 11-400 needles Cupressocyparis leylandii … 0.339405
#> # … with 2 more variables: holocellulose <units>, spectra <list>
ir_sample_data[2, ] * ir_sample_data[3, ]
#> # A tibble: 1 × 7
#> id_measurement id_sample sample_type sample_comment klason_lignin
#> * <int> <chr> <chr> <chr> <units>
#> 1 2 GN 11-400 needles Cupressocyparis leylandii … 0.339405
#> # … with 2 more variables: holocellulose <units>, spectra <list>
ir_sample_data[2, ] / ir_sample_data[3, ]
#> # A tibble: 1 × 7
#> id_measurement id_sample sample_type sample_comment klason_lignin
#> * <int> <chr> <chr> <chr> <units>
#> 1 2 GN 11-400 needles Cupressocyparis leylandii … 0.339405
#> # … with 2 more variables: holocellulose <units>, spectra <list>Further information
Many more functions and options to handle and process spectra are available in the ‘ir’ package. These are described in the documentation. In the documentation, you can also read more details about the functions and options presented here.
To learn more about how ir objects can be useful can be plotted, and the spectral preprocessing functions, see the vignette Introduction to the ‘ir’ package.
Sources
The data contained in the csv file used in this vignette are derived from Hodgkins et al. (2018)
Session info
#> R version 4.2.0 (2022-04-22)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 20.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
#>
#> locale:
#> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
#> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
#> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
#> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] ggplot2_3.3.6 purrr_0.3.4 dplyr_1.0.9 ir_0.3.0
#>
#> loaded via a namespace (and not attached):
#> [1] lattice_0.20-45 tidyr_1.2.0 png_0.1-7
#> [4] hyperSpec_0.100.0 rprojroot_2.0.3 digest_0.6.29
#> [7] utf8_1.2.2 R6_2.5.1 evaluate_0.15
#> [10] highr_0.9 pillar_1.7.0 Rdpack_2.3.1
#> [13] rlang_1.0.2 lazyeval_0.2.2 SparseM_1.81
#> [16] limSolve_1.5.6 jquerylib_0.1.4 rmarkdown_2.14
#> [19] pkgdown_2.0.4 labeling_0.4.2 textshaping_0.3.6
#> [22] desc_1.4.1 stringr_1.4.0 munsell_0.5.0
#> [25] compiler_4.2.0 xfun_0.31 pkgconfig_2.0.3
#> [28] systemfonts_1.0.4 baseline_1.3-1 htmltools_0.5.2
#> [31] tidyselect_1.1.2 tibble_3.1.7 lpSolve_5.6.15
#> [34] quadprog_1.5-8 fansi_1.0.3 crayon_1.5.1
#> [37] withr_2.5.0 MASS_7.3-57 rbibutils_2.2.8
#> [40] brio_1.1.3 grid_4.2.0 jsonlite_1.8.0
#> [43] gtable_0.3.0 lifecycle_1.0.1 magrittr_2.0.3
#> [46] scales_1.2.0 cli_3.3.0 stringi_1.7.6
#> [49] cachem_1.0.6 farver_2.1.0 fs_1.5.2
#> [52] testthat_3.1.4 latticeExtra_0.6-29 xml2_1.3.3
#> [55] bslib_0.3.1 ellipsis_0.3.2 ragg_1.2.2
#> [58] generics_0.1.2 vctrs_0.4.1 RColorBrewer_1.1-3
#> [61] tools_4.2.0 glue_1.6.2 jpeg_0.1-9
#> [64] fastmap_1.1.0 yaml_2.3.5 colorspace_2.0-3
#> [67] memoise_2.0.1 knitr_1.39 sass_0.4.1References
Hodgkins, Suzanne B., Curtis J. Richardson, René Dommain, Hongjun Wang, Paul H. Glaser, Brittany Verbeke, B. Rose Winkler, et al. 2018. “Tropical peatland carbon storage linked to global latitudinal trends in peat recalcitrance.” Nature communications 9 (1): 3640. https://doi.org/10.1038/s41467-018-06050-2.