## ---- include = FALSE--------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) # Libraries library(tidyr) library(dplyr) library(ggplot2) library(cowplot) ## ----setup-------------------------------------------------------------------- library(andurinha) ## ----echo=FALSE, fig.retina=5, fig.width=7.1, fig.align='center'-------------- ggplot() + geom_blank() + geom_segment(aes(x = c(4.1, 8.1, 12.1), xend = c(4.9, 8.9, 12.9), y = .25, yend = .25), arrow = arrow(length = unit(.3, "cm"))) + annotate("rect", xmin = c(1, 5, 9, 13), xmax = c(4, 8, 12, 16), ymin = 0, ymax = 0.5, color = c("white", "white", "white", "cyan3"), fill = c("grey", "orange", "grey", "grey88"), alpha = .5) + annotate("text", x = c(2.5, 6.5, 10.5, 14.5), y = 0.25, label = c("Import", "Find peaks", "Visualisation", "Peaks \n selection")) + scale_x_discrete() + scale_y_discrete() + theme_void() ## ----eval=FALSE--------------------------------------------------------------- # spectra <- importSpectra(path = tempdir(), sep = ";") # head(spectra) # # #> WN A B C # #> 1 399 0.011 0.008 0.009 # #> 2 401 0.008 0.006 0.006 # #> 3 403 0.006 0.005 0.006 # #> 4 405 0.005 0.005 0.005 # #> 5 407 0.005 0.005 0.003 # #> 6 409 0.003 0.004 0.002 ## ----------------------------------------------------------------------------- # Search peaks based on absorbance sum spectrum # with standarised absorbance data fp.abs <- findPeaks(andurinhaData, ndd = FALSE) summary(fp.abs) dim(fp.abs$sumSpectrum_peaksTable) # Search peaks based on second derivative sum spectrum # with standarised absorbance data fp.ndd <- findPeaks(andurinhaData) summary(fp.ndd) dim(fp.ndd$sumSpectrum_peaksTable) # Search peaks based on second derivative sum spectrum # with no standarised absorbance data fp.nZs <- findPeaks(andurinhaData, scale = FALSE) summary(fp.nZs) dim(fp.nZs$sumSpectrum_peaksTable) ## ----fig.retina=5, fig.width=7.1, fig.height=5.5, fig.align='center'---------- # Graphic overview of the raw data gOverview(andurinhaData) ## ----fig.retina=5, fig.width=7.1, fig.height=7.5, fig.align='center'---------- # Graphic overview of the processed data # Peaks searched based on the second derivative sum spectrum # with standarised absorbance data gOverview(fp.ndd$dataZ, fp.ndd$secondDerivative) ## ----fig.retina=5, fig.width=7.1, fig.height=3, fig.align='center'------------ # Peaks searched based on absorbance sum spectrum plotPeaks(fp.abs[[3]]$WN, data_abs = fp.abs$dataZ) ## ----fig.retina=5, fig.width=7.1, fig.height=6, fig.align='center'------------ # Peaks searched based on the second derivative sum spectrum plotPeaks(fp.ndd[[4]]$WN, data_abs = fp.ndd$dataZ, data_ndd = fp.ndd$secondDerivative) ## ----------------------------------------------------------------------------- # Select cutOff based on absorbance sum spectrum # to clean your peaks table round(fp.abs$sumSpectrum_peaksTable, 2) %>% arrange(desc(sumSpectrum)) # In that case cleaning may not be necesary # Select cutOff based on second derivative sum spectrum # to clean your peaks table round(fp.ndd$sumSpectrum_peaksTable, 2) %>% arrange(desc(sumSpectrum)) %>% filter(sumSpectrum > 0.18) # In that case a cut off of 0.25 my be selected ## ----------------------------------------------------------------------------- # Run finPeaks() with the new cutOff # based on the second derivative sum spectrum fp.ndd2 <- findPeaks(andurinhaData, cutOff = 0.25) ## ----fig.retina=5, fig.width=7.1, fig.height=3, fig.align='center'------------ # plotPeaks # based on absorbance sum spectrum # no cleaning needed plotPeaks(fp.ndd2[[3]]$WN, data_abs = fp.ndd2$dataZ) ## ----fig.retina=5, fig.width=7.1, fig.height=6, fig.align='center'------------ # plotPeaks # based on the second derivative sum spectrum plotPeaks(fp.ndd2[[4]]$WN, data_abs = fp.ndd2$dataZ, data_ndd = fp.ndd2$secondDerivative)