From time to time we all need some fun, so why not make some funny graphs since we are R users, yeah! This post is based on the tutorials on R CHARTS, do not forget to pick a look.
Bar dogs First I want to show you how to add some good doggos to an old-fashioned bar chart. If you have not installed ggdogs, run the next code (you need devtools):...
Design of Experiments with Mixtures and their Analysis with R
Using R for design and analysis of results for experiments with mixtures.
“Mixtures are absolutely everywhere you look. Anything you can combine is a mixture.”
All the code and data in this post are available in the repository: Design of Experiments with Mixtures and their Analysis with R
What are experimental designs with mixtures? These are designs aimed at determining the effect of the proportion of different components of a mixture on one or more response variables....
Response Surface Designs and their Analysis with R
A basic R tutorial for carrying out the analysis of results of response surface designs. It also discusses how to generate Box-Behnken and Central Composite designs.
Any model is only an approximation
All code and data used in this post are available on GitHub: Response Surface Designs and their Analysis with R
What is a response surface design? Response surface experimental designs and the analysis of their results are oriented to determine the optimal combination of factors that allow us to obtain the best response within the experimental region....
Fractional factorial designs 2^k-p
Some basic concepts about fractional experimental designs, as well as their generation and analysis in R.
All other things being equal, the simplest explanation is usually the most likely.
All code and data on this post are available on GitHub: Fractional Designs with R.
What are fractional designs? These are designs in which a part or fraction of the treatments of a full factorial design are appropriately chosen, with the objective of determining which of the factors are significant using fewer experimental runs....
Data Analysis Reproducibility with R and RStudio
Some ideas related to the reproducibility of data analysis in the life science/chemistry laboratory, as well as a tutorial on how to approach reproducibility analysis with R and RStudio.
Data Analysis Reproducibility This post contains several of my opinions on the topic, and most of the content is of a self-study and of reminder nature. These are things I wish I had known at the beginning of my master’s degree, and later in my PhD, and I have been learning and applying ever so imperfectly....
Principal Component Analysis (PCA) from Scratch
How to perform PCA step by step using R and basic linear algebra functions and operations.
What is PCA? PCA is an exploratory data analysis based in dimensions reduction. The general idea is to reduce the dataset to have fewer dimensions and at the same time preserve as much information as possible.
PCA allows us to make visual representations in two dimensions and check for groups or differences in the data related to different states, treatments, etc....