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#' PCA Biplot with `ggplot2`
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#'
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#' Produces a `ggplot2` variant of a so-called [biplot](https://en.wikipedia.org/wiki/Biplot) for PCA (principal component analysis), but is more flexible and more appealing than the base \R [biplot()] function.
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#' @inheritSection lifecycle Stable Lifecycle
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#' @param x an object returned by [pca()], [prcomp()] or [princomp()]
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#' @inheritParams stats::biplot.prcomp
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#' @param 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()].
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@ -64,23 +63,28 @@
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#' # `example_isolates` is a data set available in the AMR package.
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#' # See ?example_isolates.
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#'
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#' # See ?pca for more info about Principal Component Analysis (PCA).
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#' \donttest{
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#' if (require("dplyr")) {
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#' pca_model <- example_isolates %>%
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#' filter(mo_genus(mo) == "Staphylococcus") %>%
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#' group_by(species = mo_shortname(mo)) %>%
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#' summarise_if (is.rsi, resistance) %>%
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#' pca(FLC, AMC, CXM, GEN, TOB, TMP, SXT, CIP, TEC, TCY, ERY)
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#' # calculate the resistance per group first
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#' resistance_data <- example_isolates %>%
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#' group_by(order = mo_order(mo), # group on anything, like order
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#' genus = mo_genus(mo)) %>% # and genus as we do here;
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#' filter(n() >= 30) %>% # filter on only 30 results per group
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#' summarise_if(is.rsi, resistance) # then get resistance of all drugs
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#'
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#' # old (base R)
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#' biplot(pca_model)
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#' # now conduct PCA for certain antimicrobial agents
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#' pca_result <- resistance_data %>%
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#' pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT)
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#'
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#' summary(pca_result)
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#'
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#' # new
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#' ggplot_pca(pca_model)
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#' # old base R plotting method:
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#' biplot(pca_result)
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#' # new ggplot2 plotting method using this package:
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#' ggplot_pca(pca_result)
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#'
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#' if (require("ggplot2")) {
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#' ggplot_pca(pca_model) +
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#' ggplot_pca(pca_result) +
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#' scale_colour_viridis_d() +
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#' labs(title = "Title here")
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#' }
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