BIN521L_Summer2026

STEP ONE: Read in files

A3 <- read.csv('A3_Pierce_Standard_0001_A3_26-07-17_13-30_0002.csv')
A4 <- read.csv('A4_2minTrypLC0c_0001_A4_26-07-17_13-36_0001.csv')

STEP TWO: Column slice to keep only m_z and intensity

A3new <- A3[,1:2]
A4new <- A4[,c(1,2)]
names(A3new) <- c('m_z','int')
names(A4new) <- c('m_z','int')

STEP THREE: Filtering by m_z and round

A3filtered <- A3new[A3new$int>1000,]
A4filtered <- A4new[A4new$int>100,]
A3filtered$m_z <- round(A3filtered$m_z)
A4filtered$m_z <- round(A4filtered$m_z)

STEP FOUR:Use merge to keep perfect matches

matchDF <- merge(A3filtered,
                 A4filtered,by="m_z")

STEP FIVE: Tidy data

A3tidy <- cbind(sample='Standard',A3filtered)
A4tidy <- cbind(sample='Digest',A4filtered)
Matchtidy <- cbind(sample='Match',
                   matchDF[,c(1,3)])
names(Matchtidy) <- c('sample','m_z','int')
tidyDF <- rbind(A3tidy,
                A4tidy,
                Matchtidy)

STEP SIX: Plot using ggplot & facet wrap

p<-ggplot(tidyDF, aes(x = m_z,y=int))+
  geom_col(aes(colour = sample ,fill=sample))+
  xlab("m/z ratio") + ylab("Millivolts")+
  facet_wrap(~sample,nrow=3)
p