elementR_project: Object elementR_project
In elementR: An Framework for Reducing Elemental LAICPMS Data from Solid Structures

Description Usage Format Details Fields Methods See Also Examples

The R6Class object elementR_project contains all the information needed for running an elementR session

1	elementR_project

An R6Class generator object

The elementR_project structure allows to organized data in a session framework, faciliting therefore numerous major functionalities: handling as many standard replicates as wanted, machine drift verification and correction, sample replicate realignment and averaging. Moreover, this object can be easily exported, allowing user to re-open it later for finishing or editing final results.

name: A character string corresponding to the name of the project

folderPath: A character string corresponding to the path of the project

standardsPath: A character string corresponding to the path of the standard folder

standardsFiles: A vector containing the names of each standard file

standards: A list containing the elementR_repStandard of each type of standard

samplesPath: A character string corresponding to the path of the sample folder

samplesFiles: A vector containing the names of each sample file

samples: A list containing the elementR_repSample of each sample

EtalonPath: A character string corresponding to the path of the calibration file

EtalonData: A matrix corresponding to the calibration data

listeElem: A vector containing the names of the chemical elements included in the project

flag_stand: A vector indicating which standards have been filtered

flag_Sample: A vector indicating which samples have been filtered

flagRealign: A list vectors indicating which samples have been realigned or averaged

standardRank: A vector corresponding to the standard rank in ICPMS analysis

sampleRank: A vector corresponding to the sample rank in ICPMS analysis

elementChecking: A list indicating the number and the location of the error(s) of structure within data included in the project

errorSession: A numerical value indicating the non numeric error(s) within data included in the project

regressionModel: A matrix summarizing, for each chemical element, the parameters of the linear regression corresponding to the machine drift

machineCorrection: A vector summarizing the chemical element(s) to correct from machine drift

flagMachineCorrection: A numerical value indicating the validation of the machine correction step

nbCalib: A vector corresponding to the number of standard values available for each chemical element to proceed the linear regression

elemStand: A character string indicating the chemical element considered as internal standard (by default = Ca)

summarySettings: A matrix summarizing all the parameters set by user for each replicate (sample and standard)

ChoiceUserCorr: A logical value corresponding to the choice of the user to correct or no the session based on the first step of configuration

R2Threshold: the threshold to switch the machine drift correction from a linear to a neighbor correction

set_summarySettings(name, rank, bins, plat1, plat2, average, LOD): Aim: set summarySettings; Arguments: name = a character string corresponding to the name of the replicate to set, rank= its rank in ICPMS analysis, bins = a numerical value corresponding to the time at which end the blank values, plat1 = a numerical value corresponding to the time at which begin the plateau values, plat2 = a numerical value corresponding to the time at which end the plateau values, average = a vector corresponding to the blank averaged value (here, BlankAverarge) for each chemical element of the considered replicate, LOD = a vector corresponding to the limit of detection (here, LOD) for each chemical element of the considered replicate

is.integer0(x): Aim: test the integer(0); Arguments: x = a vector to test; Outputs: TRUE or FALSE

closest(x,y): Aim: find the nearest value among a vector of numerical data; Arguments: x = a vector of numerical values, y = the investigated value; Output: val = a list of two values: the nearest value and its place within the vector

PlotIC(name, Mean,SD, coord, lengthSeg, xlim, ylim, type = "p", xlab, ylab): Aim: plot mean +/- SD; Arguments: name = a vector of the names to display on xaxis, Mean = a vector of mean, SD = a vector of SD, coord = a vector of coordonnates to place xticks, lengthSeg = a numeric value cooresponding to the length of the top segment of the SD bar, xlim & ylim = the limits of plots, xlab & ylab = the labels of axis

setEtalon(x, sep, dec): Aim: define EtalonPath and EtalonData and check the validity of their data structure; Arguments: x = a character string corresponding to the path of the calibration file, dec = the decimal system of the data, sep = the separator character of the data

setflagMachineCorrection(x): Aim: set flagMachineCorrection; Arguments: x = the numerical value to set

NonNumericCheck(data, col): Aim: check non numeric characters of data; Arguments: data = a dataframe or a matrix, col = a vector of numerical values corresponding to the column(s) to investigate; Output: errB = a numerical value corresponding to the number of cells containing non numeric characters

setflagStand(place, value): Aim: set flag_stand; Arguments: place = a numerical value corresponding to the considered replicate, value = the numerical value to set

setflagSample(sample, replicate, value): Aim: set flag_Sample; Arguments: sample = a numerical value corresponding to the considered sample, replicate = a numerical value corresponding to the considered replicate, value = the numerical value to set

setCorrection(x): Aim: set machineCorrection; Arguments: x = a vector indicating the chemical elements to correct from machine drift

correction(): Aim: proceed to the linear regression on standards replicates and set nbCalib & regressionModel

setRank(type, value): Aim: set the order in which ICPMS runs each standard (standardRank) and sample (sampleRank) replicates; Arguments: type = a character string indicating the type of replicate standard ("standard") or sample ("sample"), value = a numerical value corresponding to the rank of the considered replicate

set_flagRealign(replicate, type, value): Aim: set flagRealign; Arguments: replicate = a numerical value corresponding to the number of the considered replicate, type = a character string indicating the raster or spot mode, value = the numerical value to set

setElemStand(elem): Aim: define elemStand and transmit this value to all elementR_rep and elementR_data objects inlcuded in the project; Arguments: elem = a character string corresponding to the element considered as intern standard

initialize(folderPath, sep, dec): Aim: create the project; Arguments: filePath = the path of the considered project, dec = the decimal system of the data, sep = the separator character of the data; Outputs: R6Class elementR_project

set_ChoiceUserCorr(x): Aim: information about the will of user to check or not the machine drift; Arguments: x = T (for checking machine drift), F (for not checking machine drift)

setR2Threshold(x): Aim: set R2Threshold; Arguments: x = a value between 0 and 1

insert.at(a, pos, toInsert): Aim: insert values in vectors; Arguments: a = a vector, pos = the position to insert, toInsert = a vector to insert

detectPlateau(dat, col): Aim: detection of the plateau limits of a matrix based on clustering methods and on the internal standard element; Arguments: dat = the data to proceed, col = the column used for the detection

detectBlank(dat, col): Aim: detection of the blank limits of a matrix based on the derivative value and on the internal standard element; Arguments: dat = the data to proceed, col = the column used for the detection

elementR_rep. elementR_data.

## create a new elementR_repStandard object based on the "filePath" 
## from a folder containing sample replicate

# filePath <- system.file("Example_Session", package="elementR")

# exampleProject <- elementR_project$new(filePath)

## Display the raw data 

# exampleProject$samplesFiles