Full Pattern Summation of X-Ray Powder Diffraction Data

Full pattern summation of X-ray powder diffraction data as described in Chipera and Bish (2002) . Derives quantitative estimates of crystalline and amorphous phase concentrations in complex mixtures.

powdR: full pattern summation of XRPD data

Build Status


powdR is an implementation of the full pattern summation approach to quantitative mineralogy from X-ray powder diffraction data (Chipera and Bish 2002; Chipera and Bish 2013; Eberl 2003). Version 0.1.0 contains functionality for a standardless approach, which assumes all phases within a sample can be identified, and thus they sum to 100 %. Future versions of powdR seek to add functionality for samples prepared with an internal standard.

powdR has several advantages over the excel based implementations of full pattern summation such as FULLPAT (Chipera and Bish 2002) and ROCKJOCK (Eberl 2003). First, computation is faster and, when quantifying multiple samples, can be used in combination with other packages (e.g foreach) for parralel processing. Secondly, powdR can be run via a shiny web application, which offers a user friendly interface for fast and iterative mineral quantification. Lastly, R represents a powerful tool for data manipulation, allowing users to creatively adapt, pre-treat and visualise their XRPD data.

Installation via Github

#alternatively, an installation that builds the vignette
devtools::install_github('benmbutler/powdR', build_vignettes = TRUE)


#> powdR: full pattern summation of X-ray powder diffraction data
#Load some soils to quantify
#Load a reference library of pure patterns
#Quantify a sample
q <-  fps(lib = minerals,
          smpl = soils$sandstone,
          refs = minerals$phases$phase_id,
          std = "QUA.1")
#Inspect the phase concentrations (summarised by name)
#>       phase_name phase_percent
#> 1         Illite      1.237556
#> 2     K-feldspar      1.250870
#> 3      Kaolinite      1.390756
#> 4 Organic-Matter     39.805850
#> 5    Plagioclase      1.159294
#> 6         Quartz     55.155674
#Inspect the quantification

Alternatively, plot(q, interactive = TRUE) provides an interative plot for better inspection of the fit. More detailed usage instructions are provided in the package vignette.

Shiny app

To run powdR via the shiny app, use run_powdR(). This loads the application in your default web browser. The application has four tabs:

  1. Reference library builder: Allows you to create a reference library from two .csv files: one for the XRPD measurements, and the other for the ID, name and reference intensity ratio of each pattern.
  2. Reference library Viewer: For quick inspection of the phases within a reference library.
  3. Full pattern summation: A user friendly interface for iterative full pattern summation of single samples.
  4. Results viewer: Allows for quick inspection of results derived from full pattern summation.


Chipera, Steve J., and David L. Bish. 2002. “FULLPAT: A full-pattern quantitative analysis program for X-ray powder diffraction using measured and calculated patterns.” Journal of Applied Crystallography 35 (6): 744–49. doi:10.1107/S0021889802017405.

———. 2013. “Fitting Full X-Ray Diffraction Patterns for Quantitative Analysis: A Method for Readily Quantifying Crystalline and Disordered Phases.” Advances in Materials Physics and Chemistry 03 (01): 47–53. doi:10.4236/ampc.2013.31A007.

Eberl, D. D. 2003. “User’s guide to ROCKJOCK - A program for determining quantitative mineralogy from powder X-ray diffraction data.” Boulder, CA: USGS.


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


0.2.0 by Benjamin Butler, 2 months ago


Report a bug at http://github.com/benmbutler/powdR/issues

Browse source code at https://github.com/cran/powdR

Authors: Benjamin Butler [aut, cre] , Stephen Hillier [aut] , Dylan Beaudette [ctb]

Documentation:   PDF Manual  

GPL-2 | file LICENSE license

Imports reshape, plotly, ggplot2, stats, ggpubr, shiny, nnls, shinyWidgets, baseline

Suggests knitr, rmarkdown

See at CRAN