AMR/man/ggplot_pca.Rd

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% Please edit documentation in R/ggplot_pca.R
\name{ggplot_pca}
\alias{ggplot_pca}
\title{PCA biplot with \code{ggplot2}}
\source{
The \code{\link[=ggplot_pca]{ggplot_pca()}} function is based on the \code{ggbiplot()} function from the \code{ggbiplot} package by Vince Vu, as found on GitHub: \url{https://github.com/vqv/ggbiplot} (retrieved: 2 March 2020, their latest commit: \href{https://github.com/vqv/ggbiplot/commit/7325e880485bea4c07465a0304c470608fffb5d9}{\code{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:
\enumerate{
\item Rewritten code to remove the dependency on packages \code{plyr}, \code{scales} and \code{grid}
\item Parametrised more options, like arrow and ellipse settings
\item Added total amount of explained variance as a caption in the plot
\item Cleaned all syntax based on the \code{lintr} package and added integrity checks
\item Updated documentation
}
}
\usage{
ggplot_pca(
  x,
  choices = 1:2,
  scale = TRUE,
  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_alpha = 0.75,
  base_textsize = 10,
  ...
)
}
\arguments{
\item{x}{an object returned by \code{\link[=pca]{pca()}}, \code{\link[=prcomp]{prcomp()}} or \code{\link[=princomp]{princomp()}}}

\item{choices}{
    length 2 vector specifying the components to plot. Only the default
    is a biplot in the strict sense.
  }

\item{scale}{
    The variables are scaled by \code{lambda ^ scale} and the
    observations are scaled by \code{lambda ^ (1-scale)} where
    \code{lambda} are the singular values as computed by
    \code{\link[stats]{princomp}}. Normally \code{0 <= scale <= 1}, and a warning
    will be issued if the specified \code{scale} is outside this range.
  }

\item{pc.biplot}{
    If true, use what Gabriel (1971) refers to as a "principal component
    biplot", with \code{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.
  }

\item{labels}{an optional vector of labels for the observations. If set, the labels will be placed below their respective points. When using the \code{\link[=pca]{pca()}} function as input for \code{x}, this will be determined automatically based on the attribute \code{non_numeric_cols}, see \code{\link[=pca]{pca()}}.}

\item{labels_textsize}{the size of the text used for the labels}

\item{labels_text_placement}{adjustment factor the placement of the variable names (\verb{>=1} means further away from the arrow head)}

\item{groups}{an optional vector of groups for the labels, with the same length as \code{labels}. If set, the points and labels will be coloured according to these groups. When using the \code{\link[=pca]{pca()}} function as input for \code{x}, this will be determined automatically based on the attribute \code{non_numeric_cols}, see \code{\link[=pca]{pca()}}.}

\item{ellipse}{a logical to indicate whether a normal data ellipse should be drawn for each group (set with \code{groups})}

\item{ellipse_prob}{statistical size of the ellipse in normal probability}

\item{ellipse_size}{the size of the ellipse line}

\item{ellipse_alpha}{the alpha (transparency) of the ellipse line}

\item{points_size}{the size of the points}

\item{points_alpha}{the alpha (transparency) of the points}

\item{arrows}{a logical to indicate whether arrows should be drawn}

\item{arrows_colour}{the colour of the arrow and their text}

\item{arrows_size}{the size (thickness) of the arrow lines}

\item{arrows_textsize}{the size of the text at the end of the arrows}

\item{arrows_alpha}{the alpha (transparency) of the arrows and their text}

\item{base_textsize}{the text size for all plot elements except the labels and arrows}

\item{...}{Parameters passed on to functions}
}
\description{
Produces a \code{ggplot2} variant of a so-called \href{https://en.wikipedia.org/wiki/Biplot}{biplot} for PCA (principal component analysis), but is more flexible and more appealing than the base \R \code{\link[=biplot]{biplot()}} function.
}
\details{
The colours for labels and points can be changed by adding another scale layer for colour, like \code{scale_colour_viridis_d()} or \code{scale_colour_brewer()}.
}
\section{Maturing lifecycle}{

\if{html}{\figure{lifecycle_maturing.svg}{options: style=margin-bottom:5px} \cr}
The \link[AMR:lifecycle]{lifecycle} of this function is \strong{maturing}. The unlying code of a maturing function has been roughed out, but finer details might still change. Since this function needs wider usage and more extensive testing, you are very welcome \href{https://gitlab.com/msberends/AMR/-/issues}{to suggest changes at our repository} or \link[AMR:AMR]{write us an email (see section 'Contact Us')}.
}

\examples{
# `example_isolates` is a dataset available in the AMR package.
# See ?example_isolates.

\dontrun{
# See ?pca for more info about Principal Component Analysis (PCA).
library(dplyr)
pca_model <- example_isolates \%>\% 
  filter(mo_genus(mo) == "Staphylococcus") \%>\% 
  group_by(species = mo_shortname(mo)) \%>\%
  summarise_if (is.rsi, resistance) \%>\%
  pca(FLC, AMC, CXM, GEN, TOB, TMP, SXT, CIP, TEC, TCY, ERY)
  
# old
biplot(pca_model)

# new 
ggplot_pca(pca_model)
}
}