pXRF

The goal of pXRF is to help with the evaluation of pxrf data, especially at the moment from the Niton™ XL3t XRF Analyzer.

This package is not in a production state yet!!!

Meanwhile, it is used to present a evaluation strategy to a pxrf course at Bern University. You can access the rendered Rmarkdown-Documents that will result in the package functionality, but for the time being just show the workflow using other packages, here:

• basic_functionality.md is actually how you can load pXRF data and preprocess them.
• analysis.md represents analytical procedures from simple scatterplots to pca and lda
• fine_calibration.md explains how you can come up with fine calibration for our specific device

Work in Progress!!!

Installation

You can install the development version of pXRF from GitHub with:

# install.packages("devtools")
devtools::install_github("MartinHinz/pXRF")

Example

This is a basic example which shows you how to solve a common problem:

library(pXRF)
## basic example code

What is special about using README.Rmd instead of just README.md? You can include R chunks like so:

summary(cars)
#>      speed           dist       
#>  Min.   : 4.0   Min.   :  2.00  
#>  1st Qu.:12.0   1st Qu.: 26.00  
#>  Median :15.0   Median : 36.00  
#>  Mean   :15.4   Mean   : 42.98  
#>  3rd Qu.:19.0   3rd Qu.: 56.00  
#>  Max.   :25.0   Max.   :120.00

You’ll still need to render README.Rmd regularly, to keep README.md up-to-date. devtools::build_readme() is handy for this. You could also use GitHub Actions to re-render README.Rmd every time you push. An example workflow can be found here: https://github.com/r-lib/actions/tree/v1/examples.

You can also embed plots, for example:

In that case, don’t forget to commit and push the resulting figure files, so they display on GitHub and CRAN.