---
title: "Getting Started with libscanR"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Getting Started with libscanR}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
resource_files:
- figures/logo.svg
- figures/logo-dark.svg
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.width = 7,
fig.height = 4.5,
dpi = 96
)
```
```{r load}
library(libscanR)
```
## What is libscanR?
`libscanR` provides a vendor-agnostic pipeline for Laser-Induced Breakdown
Spectroscopy (LIBS) data, including import, preprocessing, peak detection,
calibration, quantification, chemometrics, spatial mapping, and
visualization. It includes explicitly fictional teaching datasets. Atomic-line identification
requires a separately downloaded, user-configured reference; no NIST compilation
is bundled. See the README for local download/import configuration.
## Core data structures
`libscanR` defines three S3 classes:
* `libs_spectrum` — a single spectrum (possibly multi-shot).
* `libs_dataset` — a collection of spectra sharing a wavelength axis.
* `libs_calibration` — a fitted calibration model.
## A 5-minute tour
### Simulate a spectrum
```{r simulate}
spec <- ls_simulate_spectrum(line_data = ls_demo_peaks(),
elements = c(Ca = 5000, Na = 1000, Fe = 200),
n_channels = 1024,
seed = 1
)
spec
```
### Plot it
```{r plot-raw, fig.alt="Fictional teaching spectrum: mean intensity across wavelengths from 200 to 900 nm, including simulated peaks and a continuum background."}
ls_plot_spectrum(spec)
```
### Preprocess: baseline, smooth, normalize
```{r preprocess, fig.alt="The same fictional spectrum after SNIP baseline correction, five-channel moving-average smoothing and total normalization; the vertical scale is normalized intensity."}
spec_proc <- spec |>
ls_baseline(method = "snip", iterations = 40) |>
ls_smooth(method = "moving_avg", window = 5) |>
ls_normalize(method = "total")
ls_plot_spectrum(spec_proc)
```
### Detect peaks without an atomic reference
```{r peaks}
peaks <- ls_find_peaks(spec_proc, snr_threshold = 3)
head(peaks)
```
Atomic identification is a separate, optional step. First configure a reference
using the README instructions. These candidates are wavelength matches, not
proof of an element's presence:
```{r identify, eval=FALSE}
options(libscanR.element_db = "local-data/atomic-lines.csv")
id <- ls_identify_peaks(peaks, elements = c("Ca", "Na", "Fe"))
ls_plot_spectrum(spec_proc, show_elements = c("Ca", "Na"))
```
### Read from a file
```{r read}
tmp <- tempfile(fileext = ".csv")
utils::write.csv(
data.frame(wavelength = spec$wavelength,
intensity = colMeans(spec$intensity)),
tmp, row.names = FALSE
)
spec_in <- ls_read_spectrum(tmp, verbose = FALSE)
unlink(tmp)
spec_in
```
### Use example datasets
```{r example}
ds <- ls_example_data("tissue")
summary(ds)
```
## Next steps
* `vignette("preprocessing-workflow", package = "libscanR")`
* `vignette("calibration-quantification", package = "libscanR")`
* `vignette("tissue-classification", package = "libscanR")`
* `vignette("spatial-mapping", package = "libscanR")`
