At a recent Saint Louis R users meeting I had the pleasure of giving a basic introduction to the awesome dplyr R package. For me, data analysis ubiquitously involves splitting the data based on grouping variable and then applying some function to the subsets or what is termed split-apply-combine. Having personally recently incorporated dplyr into my data wrangling workflows; I’ve found this package’s syntax and performance a joy to work with. My feeling about dplyr are as follows.

Data wrangling without dplyr.

Data wrangling with dplyr.

This tutorial features an introduction to common dplyr verbs and an overview of implementing split-apply-combine in dplyr.

Some of my conclusions were; not only does dplyr make writing data wrangling code clearer and far faster, the packages calculation speed is also very high (non-sophisticated comparison to base).

The plot above shows the calculation time for 10 replications in seconds (y-axis) for calculating the median of varying number of groups (x-axis), rows (y-facet) and columns (x-facet) with (green line) and without (red line) dplyr.

May 3, 2015 | Categories: Uncategorized | Tags: data analysis, data wrangling, dplyr, r-bloggers, TeachingDemos | 2 Comments

I recently had the pleasure in participating in the 2014 WCMC Statistics for Metabolomics Short Course. The course was hosted by the NIH West Coast Metabolomics Center and focused on statistical and multivariate strategies for metabolomic data analysis. A variety of topics were covered using 8 hands on tutorials which focused on:

- data quality overview
- statistical and power analysis
- clustering
- principal components analysis (PCA)
- partial least squares (O-/PLS/-DA)
- metabolite enrichment analysis
- biochemical and structural similarity network construction
- network mapping

I am happy to have taught the course using all open source software, including: R, and Cytoscape. The data analysis and visualization were done using Shiny-based apps: DeviumWeb and MetaMapR. Check out some of the slides below or download all the class material and try it out for yourself.

2014 WCMC LC-MS Data Processing and Statistics for Metabolomics by Dmitry Grapov is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Special thanks to the developers of Shiny and Radiant by Vincent Nijs.

February 17, 2014 | Categories: Uncategorized | Tags: biochemical network, chemical similarity network, Cytoscape, data analysis, data visualization, Devium, ggplot2, hierarchical clustering, mass spectral similarity, metabolomics, MetaMapR, network, O-PLS, O-PLS-DA, PCA, R, r-bloggers, shiny, TeachingDemos, tutorial | 13 Comments

Network mapping is a high-dimensional data visualization technique which can be applied to virtually any type of data. I recently gave a tutorial on the basics of network mapping where each participants generated a mapped network for their name.

Download the full tutorial at TeachingDemos, and then follow along with the tutorial at your own pace.

Happy network mapping!

January 31, 2014 | Categories: Uncategorized | Tags: biochemical network, chemical similarity network, data visualization, MetaMapR, network mapping, networks, TeachingDemos, tutorial | Leave a comment

I’ve posted two new tutorials focused on intermediate and advanced strategies for biological, and specifically metabolomic data analysis (click titles for pdfs).

May 29, 2013 | Categories: Uncategorized | Tags: ANCOVA, chemical similarity network, classification, climate, correlation network, covariate adjustment, data analysis, data visualization, Gaussian graphical Markov metabolic network, imDEV, metabolomics, network, PCA, PLS, PLS-DA, R, research, science, TeachingDemos, tutorial | Leave a comment