1) Availability of original data;
2) Existence of a code to process and analyze this data;
3) Documentation of data and code, enabling reproducibility;
4) Distribution and accessibility of the code.Introduction
R is free software developed for statistical computing and graphics. R runs on several user interfaces (GUI), among which RStudio stands out, an integrated development environment (IDE) that is also free. Rstudio also has a free online version, accessible through the Posit Cloud website.
The R & RStudio platform can be used in a virtually unlimited way for various fields of knowledge. Among these are the Natural Sciences and, in particular, Biochemistry, Molecular Biology, Biophysics, and related areas.
Biochemistry
To access the content developed with R and RStudio for Biochemistry and related topics, you can either download the ebook Quantitative Biochemistry & R, or access the individual topics below.
This allows you to reproduce the activities in any topic covered by simply clicking on the upper right corner of the code snippet containing a copy icon 
, and pasting it into an installed session of RStudio, or in its online version RStudio Cloud. This quick tutorial in video illustrates the tip.
, and pasting it into an installed session of RStudio, or in its online version RStudio Cloud. This quick tutorial in video illustrates the tip.There are many features that enable the use of R & RStudio for teaching and learning in Biochemistry. Some of these features reside in Reproducible Research (Gandrud, 2018), which is briefly guided by:
In this sense, the production of texts, tables, graphs, data analysis, and simulations can be perfectly adjusted to teaching-learning through the use of such principles and the simultaneity of text and code, touching on Reproducible Teaching. This approach allows the reader to study the various topics covered through reading and interpretation, as well as to gain a more convergent understanding through the execution, modification, and creation of codes relevant to each topic. In this work, these concepts are applied to the quantitative content and mathematical relationships of the topics covered in Biochemistry.
These relationships are present in Biochemistry textbooks and cover, for example, titration curves of weak acids and amino acids, the study of effective charges in biomolecules, physical-chemical characteristics of proteins and nucleic acids predicted by sequence analysis, enzyme kinetics and their inhibition, thermodynamic quantities and bioenergetics, ligand-biopolymer interaction, stoichiometry of biochemical reactions, biochemical pathways and metabolic networks, among others.
In general, the above topics are covered in this material with the help of R & RStudio. However, its content does not intend to go beyond a superficial treatment of the use of R, RStudio, or even the proposed topics in Biochemistry. For these, traditional sources of tutorials, textbooks, and the internet are recommended. Nor does it venture into the universe of Bioinformatics, traditional or structural, such as Systems Biology, sequence alignment, structural prediction, molecular modeling, dynamics, and docking, or the various facets of omic studies.
In summary, the objective is only to address the quantitative content and mathematical relationships present in part of Biochemistry, as described above, using R & Rstudio. This approach touches on problem solving and simulations using linear systems of equations, linear algebra, linear, nonlinear, polynomial, and multiple linear regression, optimization, minimization, simple differential equations, and sequence analysis, among others.
Secondarily, the objective is to allow the reader to reproduce calculations, graphs, and/or tables relevant to the listed content by repeating or modifying simple code snippets and scripts.
In general, the above topics are covered in this material with the help of R & RStudio. However, its content does not intend to go beyond a superficial treatment of the use of R, RStudio, or even the proposed topics in Biochemistry. For these, traditional sources of tutorials, textbooks, and the internet are recommended. Nor does it venture into the universe of Bioinformatics, traditional or structural, such as Systems Biology, sequence alignment, structural prediction, molecular modeling, dynamics, and docking, or the various facets of omic studies.
In summary, the objective is only to address the quantitative content and mathematical relationships present in part of Biochemistry, as described above, using R & Rstudio. This approach touches on problem solving and simulations using linear systems of equations, linear algebra, linear, nonlinear, polynomial, and multiple linear regression, optimization, minimization, simple differential equations, and sequence analysis, among others.
Secondarily, the objective is to allow the reader to reproduce calculations, graphs, and/or tables relevant to the listed content by repeating or modifying simple code snippets and scripts.
Quantitative Biochemistry with R & RStudio
For the study of Biochemistry and related topics with the aid of the R & RStudio platform, you can access the individual topics below, which are also mirrored in the book Quantitative Biochemistry & R.
I. INTRODUCTION
II. STRUCTURE AND FUNCTION
III. METABOLISM
IV. OTHERS
References
GANDRUD, C. Reproducible research with r and RStudio. [S. l.]: Chapman; Hall/CRC, 2018.