Rnmr1D

packages <- c("impute", "MassSpecWavelet","pcaMethods")
if (length(setdiff(packages, rownames(installed.packages()))) > 0) {
   BiocManager::install(setdiff(packages, rownames(installed.packages())));
}

packages <- c('doParallel', 'ptw', 'signal', 'speaq', 'base64enc', 'XML', 'igraph', 'ggplot2', 'plotly', 'plyr')
if (length(setdiff(packages, rownames(installed.packages()))) > 0) {
  install.packages(setdiff(packages, rownames(installed.packages())), repos='http://cran.rstudio.com')
}

require(devtools)
install_github("INRA/Rnmr1D", dependencies = TRUE)

library(Rnmr1D)

# Test with the provided example data
data_dir <- system.file("extra", package = "Rnmr1D")
RAWDIR <- file.path(data_dir, "CD_BBI_16P02")
CMDFILE <- file.path(data_dir, "NP_macro_cmd.txt")
SAMPLEFILE <- file.path(data_dir, "Samples.txt")

# Detect the number of Cores
detectCores()

# Launch the pre-processing then the processing defined in the macro-command file
out <- Rnmr1D::doProcessing(RAWDIR, cmdfile=CMDFILE, samplefile=SAMPLEFILE, ncpu=detectCores())

# Have a look on returned data structure
ls(out)
ls(out$specMat)

### Stacked Plot with a perspective effect
dev.new()
plotSpecMat(out$specMat, ppm_lim=c(0.5,5), K=0.33)

### Overlaid Plot
dev.new()
plotSpecMat(out$specMat, ppm_lim=c(0.5,5), K=0, pY=0.1)

# Get the data matrix 
outMat <- getBucketsDataset(out, norm_meth='CSN')

# Get the Signal/Noise Ratio (SNR) matrix 
outSNR <- getSnrDataset(out, c(10.2,10.5), ratio=TRUE)

# Get the bucket table
outBucket <- getBucketsTable(out)

# Get the spectra data
spectra <- getSpectraData(out)

vignette("Rnmr1D")