1Geography & Earth Science, Aberystwyth University (United Kingdom)
2IRAMAT-CRP2A, UMR 5060, CNRS - Université Bordeaux Montaigne (France)
3Department of Geography and Geology, University of Salzburg, Salzburg (Austria)"
Getting started with a new R package can be a very tedious business (if not to say annoying). This document was written with the intention to make your first steps as painless as possible.
If you have no idea what a function does and how it works, it is always a good idea to have a
closer look into the example sections of the package functions. The package
one central function named
model_DoseRate(). The example given in the example section in
the manual will be used in the following to illustrate the central package functionality in
##load example data data("Example_Data", envir = environment())
To get a first impression on how the example dataset looks like, you call the function
print the first five rows of a
data.frame on the terminal.
Unfortunately, the naming of the table columns is not straightforward to understand. The good news is that each column carries additional information that can be seen in the R terminal by typing, e.g., for the column 'K' (which is the 2nd column):
It reveals that the numbers in the column correspond to the potassium concentration and are given in '%'. Similar all other columns can be inspected.
And here the full overview
##extract attributes attributes <- lapply(1:ncol(Example_Data), function(x) attributes(Example_Data[[x]])) attributes <- as.data.frame(data.table::rbindlist(attributes), stringAsFactors = FALSE) df <- cbind(COLUM = colnames(Example_Data), attributes) knitr::kable(df)
Now we want to start the modelling using the data given for the first sample only.
##extract only the first row data <- Example_Data[1,] ##run model results <- model_DoseRate( data = data, DR_conv_factors = "Carb2007", n.MC = 10, txtProgressBar = FALSE)
The function returns a terminal output along with two plots, which are mostly similar to the original graphical output provided by the 'MATLAB' program 'Carb'.
In the example above the function
model_DoseRate() was called with three additional
DR_conv_factors = "Carb2007",
n.MC = 10,
txtProgressBar = FALSE. The first
argument selects the dose rate conversion factors used by 'RCarb'. The second argument limits the number of
Monte Carlo runs for the error estimation to 10 and the second argument prevents the plotting of the progress bar, indicating the progression of the calculation. Both arguments were solely set to reduce calculation time and output in this vignette.
Obviously, you do not want to run each row in the input table separately to model all dose rates, so to run all the modelling for all samples in the example dataset you can call the model without subsetting the dataset first. Be careful, the calculation may take some time.
results <- model_DoseRate( data = Example_Data)
A note on the used dose rate conversion factors: For historical reasons 'Carb' has its own set of dose rate
conversion factors, which differ slightly from values in the literature (e.g., Adamiec \& Atiken, 1998)
and are used in
'RCarb' as default values. However, with
'RCarb' >= 0.1.3 you can select other dose rate conversion factors. Please type
?RCarb::Reference_Data in your R terminal for further details.
Running only the example dataset is somewhat dissatisfactory, and the usual case will be that you provide your own dataset as input. While you can enter all data directly using R, the package offers another way, using external spreadsheet software such as 'Libre Office' (or, of course, MS Excel). The procedure is sketched in the following.
write_InputTemplate() was written to create a template table (a CSV-file) that
can be subsequently opened and filled. Using the function ensures that your input data have the
correct structure, e.g., the correct number for columns and column names.
write_InputTemplate(file = "files/RCarb_Input.csv")
The path given with the argument
file can be modified as needed.
Own data are added using an external spreadsheet program and then save again as CSV-file.
knitr::include_graphics(path = "files/Screenshot.png")
For re-importing, the data standard R functionality can be used.
data <- read.csv(file = "files/RCarb_Input.csv")
The final modelling does not differ from the call already show above (here without a plot output):
##run model results <- model_DoseRate( data = data, n.MC = 10, txtProgressBar = FALSE, plot = FALSE)
Well, then you are wrong here. However, if you are just tired of using the R terminal and you want to have a graphical user interface to interact with 'RCarb'? Surprise: We also spent countless hours to develop a shiny application called 'RCarb app', and we ship it as part of the R package 'RLumShiny'.
Adamiec, G., Aitken, M.J., 1998. Dose-rate conversion factors: update. Ancient TL 16, 37–50. http://ancienttl.org/ATL_16-2_1998/ATL_16-2_Adamiec_p37-50.pdf
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