Principal component analysis is the first multivariate analysis most researchers need, and the one most often abandoned halfway through an R tutorial. Magic Stat runs it from the spreadsheet you already have and gives you the figure, the loadings and the report.
PCA takes a table of samples and continuous variables and finds new axes — the principal components — along which the data varies most. The first components compress the main structure of the table into a few dimensions you can draw. It is used to see whether samples group together, which variables drive that grouping, and how many dimensions the data really needs.
PCA is linear. It is the right answer when your variables are continuous and their relationships are roughly linear. When they are not — abundance data with many zeros, strong non-linear gradients — the ordination that is actually appropriate is usually PCoA or NMDS, and Magic Stat runs those too.
The practical trap is not the arithmetic, it is the workflow: getting the loadings out of a script, turning them into a readable table, and getting a figure that does not embarrass you in a figure legend.
Transformations: None · Z-score (standardise) · ln(1+x) · ln(x) · Hellinger · Chord · Chi-square · Square root · Range 0–1.
Input: PCA works on the sample × variable matrix directly (unconstrained ordination) — no distance matrix required.
If they are measured on different scales — concentrations in mg/L next to temperature in °C — yes, otherwise the variable with the largest numbers dominates the components. Z-score is in the transformation list for exactly this.
PCA if the variables are continuous and roughly linear. NMDS or PCoA if you are working from a dissimilarity (ecological abundance data, many zeros, non-linear gradients). Using PCA on abundance data is common and usually a mistake.
No. And if you already use R comfortably, keep it — the point here is the output and the manuscript workflow, not replacing your scripts.
Yes — the figure is exported at publication quality, including TIFF, with legends placed outside the plot.
No. Everything runs on your machine. The only automatic signal is an anonymous installation counter.