plot_pc_variance: Plot contribution of principal component to variance
In lisaschneider0509/lipidomeComparisonR:

View source: R/lipidome_comparison_pca.R

'plot_pc_variance' prints a barplot with a line of the contribution of principal components to the data variance

plot_pc_variance(
  data_frame,
  x,
  y,
  xlab = "dimensions",
  ylab = "variance [%]",
  title = "Scree plot",
  fill = "#35608DFF",
  hjust = -0.2
)

`data_frame`	data frame. Contains eigenvalues, contribution to data variance and cummulative contribution to data variance for a PCA model.
`x`	column of data_frame. Column with principal component names.
`y`	column of data_frame. Column with eigenvalues/contribution to variance/ cummulative contribution to variance.
`xlab`	string. X-axis label. Default: "dimensions"
`ylab`	string. X-axis label. Default: "variance [%]"
`title`	string. Main title. Default: "Scree plot"
`fill`	string. Color of the barplot. Default: "#35608DFF"
`hjust`	numeric. Position of labels relative to the points in the graph. Default: -0.2

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{iris_pca <- FactoMineR::PCA(dplyr::select_if(iris, is.numeric))
iris_eigen <- as.data.frame(factoextra::get_eigenvalue(iris_pca))
plot_pc_variance(iris_eigen, x = rownames(iris_eigen), y = iris_eigen$cumulative.variance.percent)}