The key objective of this open source
{eprscope}, as the first complex
{eprscope} tries to
reduce such many steps/programs if the above-mentioned software
combination would be adopted. In order to achieve the goal it uses
superior power of the open source
Prior to own {eprscope} 📦 installation a minimal system setup is
required and summarized into the following steps:
-
Installation of JDK Development Kit where the corresponding package, suitable for your operating system, can be downloaded from the official Oracle website (this is needed in order to properly run the
draw_molecule_by_rcdk()function). -
Please, follow the instructions for the the R installation procedure.
-
Download and install RStudio IDE (Integrated Development Environment). Alternatively, one may also try the corresponding cloud version without the need for an
installation. Any other
-
Download and install the latest release of Rtools (WINDOWS only) or on macOS/Linux follow the instructions to install CMake. Alternatively, on macOS, CMake can be set up via the Homebrew repository (see also https://formulae.brew.sh/formula/cmake#default). This step is required to use the
{nloptr}📦 for the fitting of isotropic EPR spectra. -
In the
# run code in the R console # # after the initial R environment setup it's always # good to install essential collection of packages for data science # with all their dependencies: install.packages("tidyverse",dependencies = TRUE) install.packages( c("DT","vctrs","npreg","patchwork","kableExtra", "htmlwidgets","webshot2","tinytable","gsignal", "shinythemes","future","future.apply", "progressr","qqplotr","animation"), dependencies = TRUE )
There are two main options how to install the {eprscope} 📦 via
the
# from the GitHub repository:
if (!require(devtools)) {install.packages("devtools")}
devtools::install_github("jatanRT/eprscope")or
# from the R-Universe (https://r-universe.dev/search)
# and CRAN (https://cran.r-project.org/) repositories:
install.packages(
"eprscope",
repos = c(
"https://jatanrt.r-universe.dev",
"https://cloud.r-project.org"
)
)If one wants to install the entire 📦 including all the documentation vignettes/articles ➨
# alternatively, install package together with all the vignettes/articles:
if (!require(devtools)) {install.packages("devtools")}
devtools::install_github("jatanRT/eprscope",build_vignettes = TRUE)Additionally, the open-source scientific and technical publishing
system Quarto together with the
Pandoc, a document converter system, may be
required for sharing the results coming from {eprscope} in desired
formats like pdf , html , docx , pptx or tex (details may be
found in the create_qmdReport_proj() documentation).
To update the {eprscope} 📦, just run the following code in the
devtools::install_github("jatanRT/eprscope")
#
# update package together with vignettes/articles:
# devtools::install_github("jatanRT/eprscope",build_vignettes = TRUE)In this section, couple of examples are shown in order to briefly demonstrate the package functionality. More detailed description can be found within the articles/vignettes or documentation examples.
# loading the package/library
library(eprscope)
#
# loading the built-in example file => "TMPD_specelchem_accu_b.par"
tmpd.params.file <-
load_data_example(file = "TMPD_specelchem_accu_b.par")
#
# parameters into interactive table (data frame)
tmpd.params.dt <-
readEPR_params_tabs(
path_to_dsc_par = tmpd.params.file,
origin = "winepr",
interact = "params"
)
#
# table preview
tmpd.params.dt# Pphenalenyl (Perinaphthenyl or PNT) radical by `SMILES` code:
# "C1([C.]23)=CC=CC2=CC=CC3=CC=C1"
draw_molecule_by_rcdk(
molecule = "C1([C.]23)=CC=CC2=CC=CC3=CC=C1",
mol.label = "Phenalenyl",
mol.label.color = "black",
mol.label.xy.posit = c(8.8, 1.2)
)# simulation of the phenalenyl (perinaphthenyl or PNT) radical,
# see also https://pubs.rsc.org/en/content/articlelanding/2006/CS/b500509b,
# the additional experimental/instrumental parameters are not shown,
# they possess their default values => see corresponding documentation
# of the `eval_sim_EPR_iso()` function.
simulation.iso <-
eval_sim_EPR_iso(
g.iso = 2.0027,
B.unit = "G",
nuclear.system = list(
list("1H", 3, 5.09), # 3 x A(1H) = 5.09 MHz
list("1H", 6, 17.67) # 6 x A(1H) = 17.67 MHz
),
lineGL.DeltaB = list(0.24, NULL) # linewidth in G
)
#
# simulated spectrum preview, in the region from 3470 G to 3522 G
simulation.iso$plot +
ggplot2::coord_cartesian(xlim = c(3470, 3522))# just run the following command in R console
plot_eval_ExpSim_app()# decay of a triarylamine radical cation right after
# its generation by electrochemical potentiostatic oxidation
# in TBAPF6/CH3CN, double integrals (Areas) vs time were
# obtained by data pre-processing within the continuous
# wave (CW) EPR spectrometer acquisition/processing software.
#
# loading the built-in example file with the instrumental parameters
triarylamine_rc_decay_dsc <-
load_data_example(file = "Triarylamine_radCat_decay_a.DSC")
#
# loading the built-in example file with "Area" vs "time" data frame
triarylamine_rc_decay_txt <-
load_data_example(file = "Triarylamine_radCat_decay_a.txt")
triarylamine_rc_decay_data <-
readEPR_Exp_Specs(
path_to_ASC = triarylamine_rc_decay_txt,
header = TRUE,
fill = TRUE,
select = c(3, 7),
col.names = c("time_s", "Area"),
x.unit = "s",
x.id = 1,
Intensity.id = 2,
qValue = 1700,
data.structure = "others"
) %>% na.omit()
#
# fitting the experimental decay by 2R --> B kinetic model
# with "k1" rate constant and the corresponding partial
# rection order "alpha". "qvar0R" refers to the initial
# "quantitative variable" (such as concentration, double integral
# or number of radicals) of the triarylamine radical cation "R".
triarylamine_rc_decay_model <-
eval_kinR_EPR_modelFit(
data.qt.expr = triarylamine_rc_decay_data,
model.react = "(r=2)R --> [k1] B",
elementary.react = FALSE,
params.guess = c(
qvar0R = 0.019,
k1 = 0.04,
alpha = 1.9
),
time.correct = TRUE,
path_to_dsc_par = triarylamine_rc_decay_dsc,
origin = "xenon"
)
#
# graph preview
triarylamine_rc_decay_model$plot#
# data frame/table, showing the obtained kinetic parameters
# by the non-linear fit and numeric solution
# of the Ordinary Differential Equations
triarylamine_rc_decay_model$df.coeffs
#> Estimate Std. Error t value Pr(>|t|)
#> qvar0R 0.018570037 5.7203136e-05 324.633198 4.3809413e-149
#> k1 0.060438055 5.4514583e-03 11.086585 6.1614969e-19
#> alpha 2.038206072 1.9676205e-02 103.587358 3.9216714e-101There are several ways how to get help. If the users are already
familiar with the R statistical language
please, follow either the individual package function documentation or
the corresponding articles/vignettes. These might be also considered as
a kind of EPR spectroscopy and
-
R Packages and Related Links for Optimization and Mathematical Programming
-
Reproducible Research in R and Guides for the R-Cubed Courses
-
Quarto - An Open Source Scientific and Technical Publishing System
-
YouTube Video Tutorials ➨
Even though the EPR spectroscopy is a quite complex field there are some introductory on-line materials which may help to start with this special magnetic resonance method ➨
Any additional questions, comments, remarks or issues can be addressed
through several discussion channels like 📧 e-mail
jantar40@protonmail.com or github 
{eprscope} related topics. If somebody is able and interested in
the package development, please refer to contributing
guide.
I would like to express a deep gratitude to my colleagues from the NMR
Spectroscopy Group of the Institute of
Organic Chemistry and Biochemistry especially,
Dr. Radek Pohl
