Interactive Application to Plot and Evaluate CW Isotropic EPR Spectra

Launch this app in order to quickly check recorded EPR spectra with their instrumental parameters. Additionally, you may try to interactively simulate the isotropic continuous wave (CW) EPR spectra using the simple user interface based on the R Shiny and several functions from the package (see Details).

Usage

plot_eval_ExpSim_app()

Details

Based on the acquisition EPR spectrum origin, in user interface, one can load a spectrum and its instrumental/experimental parameters by the readEPR_Exp_Specs as well as by the readEPR_params_tabs functions. An EPR is immediately depicted in the interactive {plotly} format by plot_EPR_Specs2D_interact. The magnetic flux density \(B\) of such EPR spectrum can be changed from mT to G and to g-values. Moreover, if the compound/radical structure is already known one can easily show its structure by entering its corresponding SMILES code, using the draw_molecule_by_rcdk. Uploaded ASCII data (spectrum + parameters) are applied to interactively simulate the corresponding experimental spectrum by the eval_sim_EPR_iso and to visualize it by the present_EPR_Sim_Spec. Additionally, one can also print and download the corresponding data frame/table in several formats (.csv,.pdf or .xls(x)) and thus use the data not only in R environment but also in other programs.

Examples

if (FALSE) { # \dontrun{
## run the app just by the following
## command in R console:
plot_eval_ExpSim_app()
#
## this is an examples of the raw
## shiny R code to run the app,
## code example from the `SERVER` part:
server <- function(input, output,session) {
## redefinition of `norm.vec.add`
norm_vec <- reactiveValues(val = NULL)
observe({
 if (isTRUE(input$normVec)){
   norm_vec$val <- as.numeric(unlist(strsplit(input$vecNorm,",")))
 } else {
   norm_vec$val <- NULL
 }
})
#
## Load experimental spectrum data frame
expr_data <- reactive({
#
req(input$ASCIIfile)
#
 spectr.data <-
   readEPR_Exp_Specs(
     path_to_ASC = input$ASCIIfile$datapath,
     col.names = switch(
       3-origin.cond(orig = input$origin),
       c("index","B_G", "dIepr_over_dB"),
       c("B_G", "dIepr_over_dB"),
       c("B_mT","dIepr_over_dB")
     ),
     x.unit = switch(
       3-origin.cond(orig = input$origin),
       "G",
       "G",
       "mT"
     ),
     qValue = input$qValue,
     origin = input$origin,
     norm.vec.add = norm_vec$val
   )
 spectr.data
})
#
# -------------------- INTERACTIVE SPECTRUM ---------------------
#
output$plot <- plotly::renderPlotly({
 #
 ## add g-Value condition:
 if (isTRUE(input$gval)) {
   expr_data_g <- expr_data() %>%
     dplyr::mutate(g_Value =
                     eval_gFactor(
                       nu.val = input$MWnuGHz,
                       B.val = .data$B_mT
                     ))
 }
...})
 }
} # }