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

Source: R/transform_dfs_2tidyDF.R
transform_dfs_2tidyDF.Rd

Taking several data frame objects 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 object 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 vobjects 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 of labels (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 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, carrying all the individual inputs, defined by the ... argument and ready for the overlay/offset plot to compare the data in desired series.

Note

In order to compare \(g_{\text{iso}}\)-values of different EPR spectra, prior to own transformation check that all data frame objects contain g_Val(ue) variable/column. If this is not case, the user may create those columns by the eval_gFactor function.

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
#> 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
#> 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
#> 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"
)