Density plots display the distribution of meta-data values
corresponding to peaks observed in one or more samples, a group, or pair
of groups. The densityPlot function can display histograms
or smoothed density, and it can display both together if plotting a
single distribution (i.e. one sample or one aggregated group). The
distribution of any numeric column of the e_meta element
may be plotted.
To construct a density plot for a single sample, first construct a
subset containing only the sample of interest. By default,
densityPlot plots the distribution (without a histogram) of
the chosen variable.
library(ftmsRanalysis)
data("exampleProcessedPeakData")
one_sample <- subset(exampleProcessedPeakData, samples="EM0011_sample")
densityPlot(one_sample, variable="NOSC")Setting the plot_hist parameter to TRUE
will display both the curve and histogram.
densityPlot(one_sample, variable="NOSC", plot_hist=TRUE)Alternatively, using plot_curve=FALSE gives just a
histogram. The yaxis parameter controls the scale of the
y-axis: it can be either “density” (default) or “count”
densityPlot(one_sample, variable="NOSC", plot_hist=TRUE, plot_curve=FALSE, yaxis="count")The densityPlot function can also choose a single sample
from a larger dataset, using the samples parameter:
densityPlot(exampleProcessedPeakData, variable="NOSC", samples="EM0069_sample")The samples parameter may also be used to specify more
than one sample, although only curves may be plotted, not
histograms.
densityPlot(exampleProcessedPeakData, variable="NOSC", samples=c("EM0011_sample", "EM0013_sample", "EM0015_sample"),
curve_colors=c("blue", "red", "green"))Since the exampleProcessedPeakData object has a group
definition (see group_designation for how to construct
this), the groups parameter can be used to select multiple
groups to plot (these curves are the distribution combining all peaks
observed by samples in the group).
getGroupDF(exampleProcessedPeakData)
#> SampleID Group Location Crop.Flora
#> 1 EM0011_sample M_S M S
#> 2 EM0013_sample M_S M S
#> 3 EM0015_sample M_S M S
#> 4 EM0017_sample M_S M S
#> 5 EM0019_sample M_S M S
#> 6 EM0061_sample M_C M C
#> 7 EM0063_sample M_C M C
#> 8 EM0065_sample M_C M C
#> 9 EM0067_sample M_C M C
#> 10 EM0069_sample M_C M C
#> 11 EW0111_sample W_C W C
#> 12 EW0113_sample W_C W C
#> 13 EW0115_sample W_C W C
#> 14 EW0117_sample W_C W C
#> 15 EW0119_sample W_C W C
#> 16 EW0161_sample W_S W S
#> 17 EW0163_sample W_S W S
#> 18 EW0165_sample W_S W S
#> 19 EW0167_sample W_S W S
#> 20 EW0169_sample W_S W S
densityPlot(exampleProcessedPeakData, variable="NOSC", samples=FALSE, groups=c("M_C", "M_S"))The samples and groups parameters work in
similar ways to select multiple distributions to plot. An
NA value indicates that all samples or groups in the data
object should be plotted, a FALSE value suppresses curves,
and a character vector may be used to select specific samples and/or
groups. The samples parameter is NA by default
(so it will plot all samples found) and groups is
FALSE by default (do not plot groups).
The easiest way to plot all samples in a group, and the group
aggregate is to subset down by group, then call
densityPlot. (Recall that all samples are plotted by
default so only the groups parameter must be
specified.)
MSgroup <- subset(exampleProcessedPeakData, groups="M_S")
densityPlot(MSgroup, variable="NOSC", groups=NA)