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< h1 > PCA biplot with < code > ggplot2< / code > < / h1 >
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< small class = "dont-index" > Source: < a href = 'https://github.com/msberends/AMR/blob/master/R/ggplot_pca.R' > < code > R/ggplot_pca.R< / code > < / a > < / small >
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< div class = "hidden name" > < code > ggplot_pca.Rd< / code > < / div >
< / div >
< div class = "ref-description" >
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< p > Produces a < code > ggplot2< / code > variant of a so-called < a href = 'https://en.wikipedia.org/wiki/Biplot' > biplot< / a > for PCA (principal component analysis), but is more flexible and more appealing than the base < span style = "R" > R< / span > < code > < a href = 'https://rdrr.io/r/stats/biplot.html' > biplot()< / a > < / code > function.< / p >
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< / div >
< pre class = "usage" > < span class = 'fu' > ggplot_pca< / span > (
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< span class = 'kw' > x< / span > ,
choices = < span class = 'fl' > 1< / span > < span class = 'op' > :< / span > < span class = 'fl' > 2< / span > ,
scale = < span class = 'fl' > TRUE< / span > ,
pc.biplot = < span class = 'fl' > TRUE< / span > ,
labels = < span class = 'kw' > NULL< / span > ,
labels_textsize = < span class = 'fl' > 3< / span > ,
labels_text_placement = < span class = 'fl' > 1.5< / span > ,
groups = < span class = 'kw' > NULL< / span > ,
ellipse = < span class = 'fl' > TRUE< / span > ,
ellipse_prob = < span class = 'fl' > 0.68< / span > ,
ellipse_size = < span class = 'fl' > 0.5< / span > ,
ellipse_alpha = < span class = 'fl' > 0.5< / span > ,
points_size = < span class = 'fl' > 2< / span > ,
points_alpha = < span class = 'fl' > 0.25< / span > ,
arrows = < span class = 'fl' > TRUE< / span > ,
arrows_colour = < span class = 'st' > "darkblue"< / span > ,
arrows_size = < span class = 'fl' > 0.5< / span > ,
arrows_textsize = < span class = 'fl' > 3< / span > ,
arrows_textangled = < span class = 'fl' > TRUE< / span > ,
arrows_alpha = < span class = 'fl' > 0.75< / span > ,
base_textsize = < span class = 'fl' > 10< / span > ,
< span class = 'kw' > ...< / span >
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)< / pre >
< h2 class = "hasAnchor" id = "arguments" > < a class = "anchor" href = "#arguments" > < / a > Arguments< / h2 >
< table class = "ref-arguments" >
< colgroup > < col class = "name" / > < col class = "desc" / > < / colgroup >
< tr >
< th > x< / th >
< td > < p > an object returned by < code > < a href = 'pca.html' > pca()< / a > < / code > , < code > < a href = 'https://rdrr.io/r/stats/prcomp.html' > prcomp()< / a > < / code > or < code > < a href = 'https://rdrr.io/r/stats/princomp.html' > princomp()< / a > < / code > < / p > < / td >
< / tr >
< tr >
< th > choices< / th >
< td > < p > length 2 vector specifying the components to plot. Only the default
is a biplot in the strict sense.< / p > < / td >
< / tr >
< tr >
< th > scale< / th >
< td > < p > The variables are scaled by < code > lambda ^ scale< / code > and the
observations are scaled by < code > lambda ^ (1-scale)< / code > where
< code > lambda< / code > are the singular values as computed by
< code > < a href = 'https://rdrr.io/r/stats/princomp.html' > princomp< / a > < / code > . Normally < code > 0 < = scale < = 1< / code > , and a warning
will be issued if the specified < code > scale< / code > is outside this range.< / p > < / td >
< / tr >
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< tr >
< th > pc.biplot< / th >
< td > < p > If true, use what Gabriel (1971) refers to as a "principal component
biplot", with < code > lambda = 1< / code > 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.< / p > < / td >
< / tr >
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< tr >
< th > labels< / th >
< td > < p > an optional vector of labels for the observations. If set, the labels will be placed below their respective points. When using the < code > < a href = 'pca.html' > pca()< / a > < / code > function as input for < code > x< / code > , this will be determined automatically based on the attribute < code > non_numeric_cols< / code > , see < code > < a href = 'pca.html' > pca()< / a > < / code > .< / p > < / td >
< / tr >
< tr >
< th > labels_textsize< / th >
< td > < p > the size of the text used for the labels< / p > < / td >
< / tr >
< tr >
< th > labels_text_placement< / th >
< td > < p > adjustment factor the placement of the variable names (< code > > =1< / code > means further away from the arrow head)< / p > < / td >
< / tr >
< tr >
< th > groups< / th >
< td > < p > an optional vector of groups for the labels, with the same length as < code > labels< / code > . If set, the points and labels will be coloured according to these groups. When using the < code > < a href = 'pca.html' > pca()< / a > < / code > function as input for < code > x< / code > , this will be determined automatically based on the attribute < code > non_numeric_cols< / code > , see < code > < a href = 'pca.html' > pca()< / a > < / code > .< / p > < / td >
< / tr >
< tr >
< th > ellipse< / th >
< td > < p > a logical to indicate whether a normal data ellipse should be drawn for each group (set with < code > groups< / code > )< / p > < / td >
< / tr >
< tr >
< th > ellipse_prob< / th >
< td > < p > statistical size of the ellipse in normal probability< / p > < / td >
< / tr >
< tr >
< th > ellipse_size< / th >
< td > < p > the size of the ellipse line< / p > < / td >
< / tr >
< tr >
< th > ellipse_alpha< / th >
< td > < p > the alpha (transparency) of the ellipse line< / p > < / td >
< / tr >
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< tr >
< th > points_size< / th >
< td > < p > the size of the points< / p > < / td >
< / tr >
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< tr >
< th > points_alpha< / th >
< td > < p > the alpha (transparency) of the points< / p > < / td >
< / tr >
< tr >
< th > arrows< / th >
< td > < p > a logical to indicate whether arrows should be drawn< / p > < / td >
< / tr >
< tr >
< th > arrows_colour< / th >
< td > < p > the colour of the arrow and their text< / p > < / td >
< / tr >
< tr >
< th > arrows_size< / th >
< td > < p > the size (thickness) of the arrow lines< / p > < / td >
< / tr >
< tr >
< th > arrows_textsize< / th >
< td > < p > the size of the text at the end of the arrows< / p > < / td >
< / tr >
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< tr >
< th > arrows_textangled< / th >
< td > < p > a logical whether the text at the end of the arrows should be angled< / p > < / td >
< / tr >
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< tr >
< th > arrows_alpha< / th >
< td > < p > the alpha (transparency) of the arrows and their text< / p > < / td >
< / tr >
< tr >
< th > base_textsize< / th >
< td > < p > the text size for all plot elements except the labels and arrows< / p > < / td >
< / tr >
< tr >
< th > ...< / th >
< td > < p > Parameters passed on to functions< / p > < / td >
< / tr >
< / table >
< h2 class = "hasAnchor" id = "source" > < a class = "anchor" href = "#source" > < / a > Source< / h2 >
< p > The < code > ggplot_pca()< / code > function is based on the < code > ggbiplot()< / code > function from the < code > ggbiplot< / code > package by Vince Vu, as found on GitHub: < a href = 'https://github.com/vqv/ggbiplot' > https://github.com/vqv/ggbiplot< / a > (retrieved: 2 March 2020, their latest commit: < a href = 'https://github.com/vqv/ggbiplot/commit/7325e880485bea4c07465a0304c470608fffb5d9' > < code > 7325e88< / code > < / a > ; 12 February 2015).< / p >
< p > As per their GPL-2 licence that demands documentation of code changes, the changes made based on the source code were:< / p > < ol >
< li > < p > Rewritten code to remove the dependency on packages < code > plyr< / code > , < code > scales< / code > and < code > grid< / code > < / p > < / li >
< li > < p > Parametrised more options, like arrow and ellipse settings< / p > < / li >
< li > < p > Added total amount of explained variance as a caption in the plot< / p > < / li >
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< li > < p > Cleaned all syntax based on the < code > lintr< / code > package and added integrity checks< / p > < / li >
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< li > < p > Updated documentation< / p > < / li >
< / ol >
< h2 class = "hasAnchor" id = "details" > < a class = "anchor" href = "#details" > < / a > Details< / h2 >
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< p > The colours for labels and points can be changed by adding another scale layer for colour, like < code > < a href = 'https://ggplot2.tidyverse.org/reference/scale_viridis.html' > scale_colour_viridis_d()< / a > < / code > or < code > < a href = 'https://ggplot2.tidyverse.org/reference/scale_brewer.html' > scale_colour_brewer()< / a > < / code > .< / p >
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< h2 class = "hasAnchor" id = "maturing-lifecycle" > < a class = "anchor" href = "#maturing-lifecycle" > < / a > Maturing lifecycle< / h2 >
< p > < img src = 'figures/lifecycle_maturing.svg' style = margin-bottom:5px / > < br / >
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The < a href = 'lifecycle.html' > lifecycle< / a > of this function is < strong > maturing< / strong > . 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 < a href = 'https://github.com/msberends/AMR/issues' > to suggest changes at our repository< / a > or < a href = 'AMR.html' > write us an email (see section 'Contact Us')< / a > .< / p >
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< h2 class = "hasAnchor" id = "examples" > < a class = "anchor" href = "#examples" > < / a > Examples< / h2 >
< pre class = "examples" > < span class = 'co' > # `example_isolates` is a dataset available in the AMR package.< / span >
< span class = 'co' > # See ?example_isolates.< / span >
< span class = 'co' > # See ?pca for more info about Principal Component Analysis (PCA).< / span >
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< span class = 'co' > if< / span > (< span class = 'fl' > FALSE< / span > ) {
< span class = 'fu' > < a href = 'https://rdrr.io/r/base/library.html' > library< / a > < / span > (< span class = 'kw' > < a href = 'https://dplyr.tidyverse.org' > dplyr< / a > < / span > )
< span class = 'kw' > pca_model< / span > < span class = 'op' > < -< / span > < span class = 'kw' > example_isolates< / span > < span class = 'op' > %> %< / span >
< span class = 'fu' > < a href = 'https://dplyr.tidyverse.org/reference/filter.html' > filter< / a > < / span > (< span class = 'fu' > < a href = 'mo_property.html' > mo_genus< / a > < / span > (< span class = 'kw' > mo< / span > ) < span class = 'op' > ==< / span > < span class = 'st' > "Staphylococcus"< / span > ) < span class = 'op' > %> %< / span >
< span class = 'fu' > < a href = 'https://dplyr.tidyverse.org/reference/group_by.html' > group_by< / a > < / span > (species = < span class = 'fu' > < a href = 'mo_property.html' > mo_shortname< / a > < / span > (< span class = 'kw' > mo< / span > )) < span class = 'op' > %> %< / span >
< span class = 'fu' > < a href = 'https://dplyr.tidyverse.org/reference/summarise_all.html' > summarise_if< / a > < / span > (< span class = 'kw' > is.rsi< / span > , < span class = 'kw' > resistance< / span > ) < span class = 'op' > %> %< / span >
< span class = 'fu' > < a href = 'pca.html' > pca< / a > < / span > (< span class = 'kw' > FLC< / span > , < span class = 'kw' > AMC< / span > , < span class = 'kw' > CXM< / span > , < span class = 'kw' > GEN< / span > , < span class = 'kw' > TOB< / span > , < span class = 'kw' > TMP< / span > , < span class = 'kw' > SXT< / span > , < span class = 'kw' > CIP< / span > , < span class = 'kw' > TEC< / span > , < span class = 'kw' > TCY< / span > , < span class = 'kw' > ERY< / span > )
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< span class = 'co' > # old (base R)< / span >
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< span class = 'fu' > < a href = 'https://rdrr.io/r/stats/biplot.html' > biplot< / a > < / span > (< span class = 'kw' > pca_model< / span > )
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< span class = 'co' > # new < / span >
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< span class = 'fu' > ggplot_pca< / span > (< span class = 'kw' > pca_model< / span > )
< span class = 'co' > if< / span > (< span class = 'fu' > < a href = 'https://rdrr.io/r/base/library.html' > require< / a > < / span > (< span class = 'st' > < a href = 'http://ggplot2.tidyverse.org' > "ggplot2"< / a > < / span > )) {
< span class = 'fu' > ggplot_pca< / span > (< span class = 'kw' > pca_model< / span > ) < span class = 'op' > +< / span >
< span class = 'fu' > < a href = 'https://ggplot2.tidyverse.org/reference/scale_viridis.html' > scale_colour_viridis_d< / a > < / span > () < span class = 'op' > +< / span >
< span class = 'fu' > < a href = 'https://ggplot2.tidyverse.org/reference/labs.html' > labs< / a > < / span > (title = < span class = 'st' > "Title here"< / span > )
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}
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}< / pre >
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