Tidy Thermo-Phenom SEM data

Tidy-Thermo-Phenom-SEM.r

# Tidy Thermo-Phenom SEM data
# Mae Saslaw, 2025

# Before using this script, rename .emsa file to .csv. 
# All caps indicates placeholder text 

library(dplyr)
library(stringr) 
library(tinyplot)

csv.to.spectrum <- function(emsa) {
  
  # Create dataframe from SEM output
  xperchan <- as.numeric(str_remove(emsa$V1[12], "#XPERCHAN    : "))
  counts <- emsa |> filter(!row_number() %in% 1:33)
  eV <- seq(from = 0, by = xperchan, length.out = as.numeric(nrow(counts)))
  spectrum <- data.frame(eV, counts = unlist(counts))
  
  # Plot spectral data
  plt(
    counts ~ eV,
    data = spectrum,
    type = "l",
    xlim = c(0, 10000)
    )
  
  return(spectrum)
}

# Sample 1
emsa <- read.csv("DATA/SAMPLE1.csv", header = FALSE)
SAMPLE1 <- csv.to.spectrum(emsa) 

# Suggested fields to add 
SAMPLE1 <- SAMPLE1 |> 
  mutate(SampleID = "SAMPLE1") |>
  mutate(Analysis = "Area 1 Spot 1") |>
  mutate(Mineral = "QUARTZ") 

# Repeat for all samples and join sample(n) tables to create a frame for plotting 
samples <- rbind(SAMPLE1, SAMPLE2)

# Plot spectra together
plt(
  counts ~ eV | SampleID,
  data = samples,
  type = "l",
  palette = "Dark 2",
  main = "title"
)
# Dataframe can also be used with ggplot