elco

Overview

‘elco’ provides functions to handle and (rudimentary) analyze element content data and isotope signature data. Features are:

Import of XRF and IRMS data as they can be exported from X-ray fluorescence spectrometers (Rigaku ZSX Primus II) and EA-IRMS devices (HEKATech’s Eurovector EA3000 coupled with Nu Instruments Nu Horizon).
Graphical display for easy data checking.
Correction of IRMS-measured element content data and isotope signatures in cases of a mismatch in the signal areas between samples and standards.
Unit conversion of element content data (e.g. g/g to mol).
Computation of the nominal oxidation state of carbon (NOSC), oxidative ratio (OR), and degree of unsaturation (DU) (Worrall et al. 2016; Masiello et al. 2008).

How to install

elco can be installed from GitHub:

remotes::install_github("henningte/elco")

How to use

Here is a short overview on elco general-purpose capabilities (handling element content ratios). Examples of how to import and correct data are not described here, but in the vignettes.

library(elco)
#> Loading required package: quantities
#> Loading required package: units
#> udunits database from C:/Users/henni/AppData/Local/R/win-library/4.2/units/share/udunits/udunits2.xml
#> Loading required package: errors

# load other required packages
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
library(quantities)
library(magrittr)

First, we have a short look at the sample data and its structure:

d <- elco::chno
d
#> # A tibble: 5 × 5
#>              C            H             N            O sample_mass
#>    (err) [g/g]  (err) [g/g]   (err) [g/g]  (err) [g/g]  (err) [mg]
#> 1 0.4928286(0)  0.177478(0) 0.09203495(0) 0.1705965(0)     3.78(1)
#> 2 0.4970892(0) 0.1692088(0) 0.09216568(0) 0.1640896(0)     3.81(1)
#> 3 0.5218047(0)  0.162892(0) 0.09125226(0) 0.1606575(0)    3.494(4)
#> 4 0.5008897(0) 0.1642393(0) 0.08918042(0)  0.165484(0)    4.251(3)
#> 5 0.5016323(0) 0.1738859(0) 0.09247861(0) 0.1609109(0)    4.046(9)

Contents for one element are stored in a numeric vector. Measurement units and errors are tracked using the quantities package. elco adds to this an identifier for the element:

# show exemplary structure for the C content
d$C
#> Element: C
#> Units: [g/g]
#> Errors: 0 0 0 0 0
#> [1] 0.4928286 0.4970892 0.5218047 0.5008897 0.5016323

New elco objects can be defined with elco_new_elco (by providing a quantities object and an element symbol). For instance, this specifies two new C content values:

x <- 
  quantities::set_quantities(c(0.3, 0.43), unit = "g/g", errors = c(0.2, 0.12)) %>%
  elco_new_elco(el_symbol = "C")
x
#> Element: C
#> Units: [g/g]
#> Errors: 0.20 0.12
#> [1] 0.30 0.43

elco supports unit conversion (using the functions of the quantities package and molar masses from PeriodicTable):

# g/g to mol
x %>% 
  elco_elco_convert(to = "mol", 
                    sample_mass = quantities::set_quantities(1, unit = "g", errors = 0))
#> Element: C
#> Units: [mol]
#> Errors: 0.016651819 0.009991091
#> [1] 0.02497773 0.03580141

# g/g to mg
x %>% 
  elco_elco_convert(to = "mg", 
                    sample_mass = quantities::set_quantities(1, unit = "g", errors = 0))
#> Element: C
#> Units: [mg]
#> Errors: 200 120
#> [1] 300 430

… even with complete data.frames!

# g/g to mol
d %>% elco_elco_convert_df(to = "mol", sample_mass = d$sample_mass)
#> # A tibble: 5 × 5
#>             C           H           N           O sample_mass
#>   (err) [mol] (err) [mol] (err) [mol] (err) [mol]  (err) [mg]
#> 1 1.552(5)e-4  6.66(2)e-4 2.485(7)e-5  4.03(1)e-5     3.78(1)
#> 2 1.578(5)e-4  6.40(2)e-4 2.509(8)e-5  3.91(1)e-5     3.81(1)
#> 3 1.518(2)e-4 5.647(7)e-4 2.276(3)e-5 3.508(4)e-5    3.494(4)
#> 4 1.773(1)e-4 6.927(5)e-4 2.707(2)e-5 4.397(3)e-5    4.251(3)
#> 5 1.690(4)e-4  6.98(2)e-4 2.671(6)e-5 4.069(9)e-5    4.046(9)

elco helps computing element ratios:

d %>%
  elco_elco_convert_df(to = "mol", sample_mass = d$sample_mass) %>%
  dplyr::mutate(cn_molar = C/N,
                ch_molar = C/H,
                co_molar = C/O)
#> # A tibble: 5 × 8
#>             C         H        N        O sample_mass cn_molar ch_molar co_molar
#>   (err) [mol] (err) [m… (err) [… (err) […  (err) [mg] (err) [… (err) [… (err) […
#> 1 1.552(5)e-4 6.66(2)e… 2.485(7… 4.03(1)…     3.78(1)  6.24(3) 0.233(1)  3.85(2)
#> 2 1.578(5)e-4 6.40(2)e… 2.509(8… 3.91(1)…     3.81(1)  6.29(3) 0.247(1)  4.04(2)
#> 3 1.518(2)e-4 5.647(7)… 2.276(3… 3.508(4…    3.494(4)  6.67(1) 0.2688(… 4.327(7)
#> 4 1.773(1)e-4 6.927(5)… 2.707(2… 4.397(3…    4.251(3) 6.550(6) 0.2559(… 4.032(4)
#> 5 1.690(4)e-4 6.98(2)e… 2.671(6… 4.069(9…    4.046(9)  6.33(2) 0.2421(…  4.15(1)

Finally, elco provides functions to compute the nominal oxidation state of carbon (NOSC), oxidative ratio (OR), and degree of unsaturation (DU) (Worrall et al. 2016; Masiello et al. 2008)

d <- 
  d %>%
  elco::elco_elco_convert_df(to = "mol", sample_mass = d$sample_mass) %>%
  dplyr::mutate(
    nosc = elco_nosc(C, H, N, O),
    or = elco_or(C, H, N, O),
    du = elco_du(C, H, N)
  )

Further information can be found in the vignettes:

How to cite

Please cite this compendium as:

Teickner, H. and Knorr, K.-H., (2022). elco: Handling data on chemical element contents and isotope signatures.. Accessed 28 Apr 2022. Online at https://github.com/henningte/elco

Acknowledgments

We thank Gałka et al. (2019) for the provision of test data.

Licenses

Text and figures : CC-BY-4.0

Code : See the DESCRIPTION file

Data : CC-BY-SA-4.0. All external data (files “xrf1.csv”, “xrf2.csv”, “irms1.csv”, and “irms2.csv”) for elco were provided by Gałka et al. (2019). elco::irms_standards was collected from different data sources (Coplen et al. 2006; IVA Analysentechnik GmbH & Co. KGt 2016) and own measurements (see the documentation for details). elco::chno was created by the package’s authors.

Contributions

We welcome contributions from everyone. Before you get started, please see our contributor guidelines. Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

Sources

All data for this package are from Gałka et al. (2019).

Coplen, Tyler B., Willi A. Brand, Matthias Gehre, Manfred Gröning, Harro A. J. Meijer, Blaza Toman, and R. Michael Verkouteren. 2006. “New guidelines for $\delta^{13}$ C measurements.” Analytical Chemistry 78 (7): 2439–41. https://doi.org/10.1021/ac052027c.

Gałka, M., T. Broder, H. Teickner, and K.-H. Knorr. 2019. “Randomized dummy IRMS and XRF data for peat for the R package elco.”

IVA Analysentechnik GmbH & Co. KGt. 2016. “Elementaranalyse Eurovector/ HEKAtech.” Edited by IVA Analysentechnik GmbH & Co. KGt. https://www.iva-analysentechnik.de/wp-content/uploads/2016/05/EA_Eurovector_2016_Rev01.pdf.

Masiello, C. A., M. E. Gallagher, J. T. Randerson, R. M. Deco, and O. A. Chadwick. 2008. “Evaluating Two Experimental Approaches for Measuring Ecosystem Carbon Oxidation State and Oxidative Ratio.” Journal of Geophysical Research 113 (G3): G03010. https://doi.org/10.1029/2007JG000534.

Worrall, Fred, Gareth D. Clay, Catherine S. Moody, Tim P. Burt, and Rob Rose. 2016. “The effective oxidation state of a peatland.” Journal of Geophysical Research: Biogeosciences 121 (1): 145–58. https://doi.org/10.1002/2015JG003182.

henningte / elco