Perform principal component analysis (PCA) and return the results.

Usage

pca_in_one(
  data,
  sample,
  pca.num = 5,
  plot = TRUE,
  x = "pc1",
  y = "pc2",
  color = "species",
  shape = "species"
)

Arguments

data: A data frame where rows are samples and columns are indicators.
sample: A data frame containing sample information, where one column is the sample names that match those in @param data description, without the need for row names.
pca.num: The returned results include the number of principal components, with a default value of 10.
plot: Whether to plot, with the default being to draw a scatter plot. The plotted graph is a ggplot2 object, which can be customized and enhanced using ggplot2.
x: Which principal component to use as the X-axis, default is pc1.
y: Which principal component to use as the Y-axis, default is pc2.
color: Which grouping information to use as the color of the points, default is species.
shape: Which grouping information to use as the shape of the points, default is species.

Value

A list containing the calculation results, scree plot, scatter plot, and COS2 plot.

Examples

library(dplyr)
library(tibble)
library(magrittr)

data <- iris[, 1:4]
sample <- iris$Species %>%
  as.data.frame() %>%
  rownames_to_column(var = "sample") %>%
  set_names(c("sample", "species"))

pca_in_one(data, sample) -> result.pca