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# PCA Biplot with `ggplot2`

Source: `R/ggplot_pca.R`
`ggplot_pca.Rd`

Produces a `ggplot2` variant of a so-called biplot for PCA (principal component analysis), but is more flexible and more appealing than the base R `biplot()` function.

## Usage

    ggplot_pca(
      x,
      choices = 1:2,
      scale = 1,
      pc.biplot = TRUE,
      labels = NULL,
      labels_textsize = 3,
      labels_text_placement = 1.5,
      groups = NULL,
      ellipse = TRUE,
      ellipse_prob = 0.68,
      ellipse_size = 0.5,
      ellipse_alpha = 0.5,
      points_size = 2,
      points_alpha = 0.25,
      arrows = TRUE,
      arrows_colour = "darkblue",
      arrows_size = 0.5,
      arrows_textsize = 3,
      arrows_textangled = TRUE,
      arrows_alpha = 0.75,
      base_textsize = 10,
      ...
    )

## Source

The `ggplot_pca()` function is based on the `ggbiplot()` function from the `ggbiplot` package by Vince Vu, as found on GitHub: https://github.com/vqv/ggbiplot (retrieved: 2 March 2020, their latest commit: `7325e88`; 12 February 2015).

As per their GPL-2 licence that demands documentation of code changes, the changes made based on the source code were:

1. Rewritten code to remove the dependency on packages `plyr`, `scales` and `grid`

2. Parametrised more options, like arrow and ellipse settings

3. Hardened all input possibilities by defining the exact type of user input for every argument

4. Added total amount of explained variance as a caption in the plot

5. Cleaned all syntax based on the `lintr` package, fixed grammatical errors and added integrity checks

6. Updated documentation

## Arguments

x

  An object returned by `pca()`, `prcomp()` or `princomp()`.

choices

  length 2 vector specifying the components to plot. Only the default is a biplot in the strict sense.

scale

  The variables are scaled by `lambda ^ scale` and the observations are scaled by `lambda ^ (1-scale)` where `lambda` are the singular values as computed by `princomp`. Normally `0 <= scale <= 1`, and a warning will be issued if the specified `scale` is outside this range.

pc.biplot

  If true, use what Gabriel (1971) refers to as a "principal component biplot", with `lambda = 1` and observations scaled up by sqrt(n) and variables scaled down by sqrt(n). Then inner products between variables approximate covariances and distances between observations approximate Mahalanobis distance.

labels

  An optional vector of labels for the observations. If set, the labels will be placed below their respective points. When using the `pca()` function as input for `x`, this will be determined automatically based on the attribute `non_numeric_cols`, see `pca()`.

labels_textsize

  The size of the text used for the labels.

labels_text_placement

  Adjustment factor the placement of the variable names (`>=1` means further away from the arrow head).

groups

  An optional vector of groups for the labels, with the same length as `labels`. If set, the points and labels will be coloured according to these groups. When using the `pca()` function as input for `x`, this will be determined automatically based on the attribute `non_numeric_cols`, see `pca()`.

ellipse

  A logical to indicate whether a normal data ellipse should be drawn for each group (set with `groups`).

ellipse_prob

  Statistical size of the ellipse in normal probability.

ellipse_size

  The size of the ellipse line.

ellipse_alpha

  The alpha (transparency) of the ellipse line.

points_size

  The size of the points.

points_alpha

  The alpha (transparency) of the points.

arrows

  A logical to indicate whether arrows should be drawn.

arrows_colour

  The colour of the arrow and their text.

arrows_size

  The size (thickness) of the arrow lines.

arrows_textsize

  The size of the text at the end of the arrows.

arrows_textangled

  A logical whether the text at the end of the arrows should be angled.

arrows_alpha

  The alpha (transparency) of the arrows and their text.

base_textsize

  The text size for all plot elements except the labels and arrows.

...

  Arguments passed on to functions.

## Details

The colours for labels and points can be changed by adding another scale layer for colour, such as `scale_colour_viridis_d()` and `scale_colour_brewer()`.

## Examples

    # `example_isolates` is a data set available in the AMR package.
    # See ?example_isolates.

    # \donttest{
    if (require("dplyr")) {
      # calculate the resistance per group first
      resistance_data <- example_isolates %>%
        group_by(
          order = mo_order(mo), # group on anything, like order
          genus = mo_genus(mo)
        ) %>% #   and genus as we do here;
        filter(n() >= 30) %>% # filter on only 30 results per group
        summarise_if(is.sir, resistance) # then get resistance of all drugs

      # now conduct PCA for certain antimicrobial drugs
      pca_result <- resistance_data %>%
        pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT)

      summary(pca_result)

      # old base R plotting method:
      biplot(pca_result, main = "Base R biplot")

      # new ggplot2 plotting method using this package:
      if (require("ggplot2")) {
        ggplot_pca(pca_result) +
          labs(title = "ggplot2 biplot")
      }
      if (require("ggplot2")) {
        # still extendible with any ggplot2 function
        ggplot_pca(pca_result) +
          scale_colour_viridis_d() +
          labs(title = "ggplot2 biplot")
      }
    }
    #> Warning: There were 73 warnings in `summarise()`.
    #> The first warning was:
    #> ℹ In argument: `PEN = (function (..., minimum = 30, as_percent = FALSE,
    #>   only_all_tested = FALSE, ...`.
    #> ℹ In group 5: `order = "Lactobacillales"`, `genus = "Enterococcus"`.
    #> Caused by warning:
    #> ! Introducing NA: only 14 results available for PEN in group: order =
    #> "Lactobacillales", genus = "Enterococcus" (whilst `minimum = 30`).
    #> ℹ Run `dplyr::last_dplyr_warnings()` to see the 72 remaining warnings.
    #> ℹ Columns selected for PCA: "\033[1mAMC\033[22m", "\033[1mCAZ\033[22m",
    #>   "\033[1mCTX\033[22m", "\033[1mCXM\033[22m", "\033[1mGEN\033[22m",
    #>   "\033[1mSXT\033[22m", "\033[1mTMP\033[22m", and "\033[1mTOB\033[22m". Total
    #>   observations available: 7.
    #> Groups (n=4, named as 'order'):
    #> [1] "Caryophanales"    "Enterobacterales" "Lactobacillales"  "Pseudomonadales"
    #>

    # }