Transformation of Several Data Frame Objects/Variables into one Tidy (Long Table) Form

Taking several data frame objects/variables from the (local/Global) R environment and transforming them into one with the long table/tidy form. This is especially handy if a quick comparison of similar plots/EPR spectra in one graph panel is required. Difference between the readEPR_Exp_Specs_multif and the actual function lies in the object input/origin. While the readEPR_Exp_Specs_multif takes the original files/data, coming from spectrometer, the transform_dfs_2tidyDF collects temporary processed/stored data frame objects/variables created meanwhile in the R environment.

Usage

transform_dfs_2tidyDF(
  ...,
  df.names,
  which.coly.norm = "dIepr_over_dB",
  norm.vec = rep(1, times = length(df.names)),
  var2nd.series = "Spectrum"
)

Arguments

...: Data frame variable names (separated by comma ,) to be transformed into long table/tidy form in order to quickly compare plots/EPR spectra in one graph panel (see also Examples). The number of data frame variables is not limited, however for the series of \(\geq 10-12\) objects/spectra, the graph may look cluttered, depending on complexity of EPR spectra.
df.names: Character string vector (in the form of e.g. c("Spectr_01","Spectr_02") or c("283 K","273 K","263 K")), describing the individual data frames/plots in the returned tidy/long table and corresponding to series of plots/spectra (be aware of the data frames order).
which.coly.norm: Character string, pointing to name of the column (in all data frame objects) to be normalized, by the norm.vec vector. This column actually corresponds to quantity/variable presented on graph as y-axis. Default: which.coly.norm = "dIepr_over_dB" (that is the derivative intensity in CW EPR spectra).
norm.vec: Numeric vector, consisting of (division) normalization constants (see also the which.coly.norm argument) for each of the data frame objects/variables defined by the ellipsis ... argument. Default: norm.vec = rep(1, times = length(df.names)), i.e. all normalization constants equal to 1. If needed, the vector may be redefined (please, be aware of the data frames order).
var2nd.series: Character string, pointing to name of the second variable which is "common denominator" for the series of df.names vector elements like var2nd.series = "Temperature" or var2nd.series = "Spectrum" (default).

Value

Tidy (long form) data frame/table object/variable, carrying all the individual inputs, defined by the ... argument and ready for the overlay/offset plot to compare the data in desired series.

Examples

## compare TMPD*+ EPR spectrum with that of aminoxyl,
## first, define the path and variable for TMPD*+ data
tmpd.data.path <-
  load_data_example(file = "TMPD_specelchem_accu_b.asc")
tmpd.data.df <-
  readEPR_Exp_Specs(
    tmpd.data.path,
    col.names = c("B_G","dIepr_over_dB"),
    qValue = 3500,
    origin = "winepr"
   )
#
## preview
head(tmpd.data.df)
#>          B_G dIepr_over_dB      B_mT
#>        <num>         <num>     <num>
#> 1: 3439.1699 -56.047361607 343.91699
#> 2: 3439.2200 -30.506506697 343.92200
#> 3: 3439.2700 -38.522218750 343.92700
#> 4: 3439.3201   0.039208426 343.93201
#> 5: 3439.3701 -92.620223214 343.93701
#> 6: 3439.4199 -71.915075893 343.94199
#
## second, do the same for the  aminoxyl data
aminoxyl.data.path <-
  load_data_example(file = "Aminoxyl_radical_a.txt")
aminoxyl.data.df <-
  readEPR_Exp_Specs(
    aminoxyl.data.path,
    qValue = 2100
  )
#
## preview
aminoxyl.data.df %>% head()
#>    index       B_G  dIepr_over_dB      B_mT
#>    <int>     <num>          <num>     <num>
#> 1:     1 3332.7000 -1.7738564e-06 333.27000
#> 2:     2 3332.9005  1.2209461e-07 333.29005
#> 3:     3 3333.1009 -1.8403788e-07 333.31009
#> 4:     4 3333.3014 -7.1641412e-08 333.33014
#> 5:     5 3333.5019 -1.7361444e-06 333.35019
#> 6:     6 3333.7023 -5.1531169e-07 333.37023
#
## ...and the own transformation
spectra.data.tidy.df <-
  transform_dfs_2tidyDF(
    tmpd.data.df,
    aminoxyl.data.df,
    df.names = c("TMPD","Aminoxyl"),
    norm.vec = c(5e+3,1e-5),
    var2nd.series = "Radical"
  )
#
## preview
head(spectra.data.tidy.df)
#>    Radical       B_G  dIepr_over_dB      B_mT
#>     <char>     <num>          <num>     <num>
#> 1:    TMPD 3439.1699 -1.1209472e-02 343.91699
#> 2:    TMPD 3439.2200 -6.1013013e-03 343.92200
#> 3:    TMPD 3439.2700 -7.7044437e-03 343.92700
#> 4:    TMPD 3439.3201  7.8416853e-06 343.93201
#> 5:    TMPD 3439.3701 -1.8524045e-02 343.93701
#> 6:    TMPD 3439.4199 -1.4383015e-02 343.94199
#
## plotting both EPR spectra together
plot_EPR_Specs(
  data.spectra = spectra.data.tidy.df,
  x = "B_G",
  x.unit = "G",
  var2nd.series = "Radical",
  var2nd.series.slct.by = 1,
  xlim = c(3400,3600),
  line.colors = c("darkorange","blue2"),
  legend.title = "Radical"
)

#
## ...and the interactive preview (B in mT)
plot_EPR_Specs2D_interact(
  data.spectra = spectra.data.tidy.df,
  line.colors = c("darkorange","blue2"),
  var2nd.series = "Radical",
  legend.title = "Radical"
)