Specifications
| Publisher | Chapman and Hall/CRC; 1st edition |
| ISBN 13 | 9781032415475 |
| ISBN 10 | 1032415479 |
| Author | David K. Gosser |
| Book Format | Paperback |
| Language | English |
| Book Description | R for Quantitative Chemistry is an exploration of how the R language can be applied to a wide variety of problems in what is typically termed "Quantitative Chemistry" or sometimes "Analytical Chemistry". Topics include: basic statistics, spectroscopic data, acid base equilibria and titrations, binding curves (of great current interest for biomedical applications), Fourier Transforms, and chemical kinetics and enzyme kinetics. An innovative feature is the discussion (as an alternative to the less stable nls packages) of the simplex adaptation subplex (R package) coupled with Monte Carlo analysis to determine confidence intervals for estimated parameters resulting from least squares optimization. Chemists who are interested in learning R as a research tool as well as Chemists who are teaching Quantitative Chemistry, as well as their students will be interested. This book is useful as most R books approach data analysis from an economic, social, medical, or biological context. Analysis of chemical data draws upon specific numerical models and a different set R programming and packages than is typically discussed in other disciplines. This book will be based upon, in large part, actual experimental data and will include end of chapter questions and projects. Readers are encouraged to email the author at gosserch@gmail.com and to follow the accompanying blog on Medium "R Programming for Quantitative Chemistry". Key Features:Elements of R programming for ChemistsLiterature Based ExamplesIncludes Binding Assay AnalysisIntegrates theory, experiment, and R programming |
| About the Author | Dr. David Gosser is a Professor of Chemistry at City College of New York, CUNY. Dr. Gosser received his Ph.D. in Physical Inorganic Chemistry from Brown University. |
| Publication Date | 31 August 2023 |
| Number of Pages | 110 pages |
R for Quantitative Chemistry
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