by John D. Gagnon
University of California, San Francisco
Overview
Installation
Usage
Session info
License
A shiny app-based GUI wrapper for ggplot2 with built-in statistical
analysis. Import data from file and use dropdown menus and checkboxes to
specify the plotting variables, graph type, and look of your plots. Once
created, plots can be saved independently or stored in a report that can
be saved as a pdf. If new data are added to the file, the report can be
refreshed to include new data. Statistical tests can be selected and
added to the graphs.
Analysis of flow cytometry data is especially integrated with
plotGrouper. Count data can be transformed to return the absolute number
of cells in a sample (this feature requires inclusion of the number of
beads per sample and information about any dilution performed).
Examples of some of the types of plots you can create:
If you do not already have R installed, or your version is out of date, download and install the latest version.
Download the package from Bioconductor.
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("plotGrouper")
'Bioconductor'
BiocManager::install("plotGrouper", version = "devel")
BiocManager::install("jdgagnon/plotGrouper")
Load the package into the R session.
library(plotGrouper)
To initialize the shiny app, paste the following code in your R console and run it.
plotGrouper()
Once the web app opens, you can access the iris dataset by clicking
the iris button to learn how to use the app. After the iris data
loads, the selection windows will be automatically populated and a graph
should be displayed.
The Raw Data tab displays the structure of the data loaded. Your file
should be organized in the following way:
| Unique identifier | Comparisons | Variables |
|---|---|---|
| Sample | Species | Sepal.Length |
| setosa_1 | setosa | 5.1 |
| setosa_2 | setosa | 4.9 |
| versicolor_1 | versicolor | 7 |
| versicolor_2 | versicolor | 6.4 |
| virginica_1 | virginica | 6.3 |
| virginica_2 | virginica | 5.8 |
| etc… | etc… | etc… |
These columns can be titled anything you want but values in the columns are important.
The Unique identifier column should contain only unique values that
identify each individual sample (e.g., Sample within iris
Raw Data).
The Comparisons column should contain replicated values that
identify each individual as belonging to a group (e.g., Species
within iris Raw Data).
The Variables column(s) should created for each variable you wish to
plot. The values in these columns must be numeric (e.g.,
Sepal.Length, Sepal.Width, Petal.Length, Petal.Width within
iris Raw Data)
After importing a data file, a Sheet column will be created and
populated with the sheet name(s) from the file if it came from an excel
spreadsheet or the file name if it came from a csv or tsv file.
The Variables to plot selection window is used to choose which
variable(s) to plot (e.g., Sepal.Width from the iris data). If
multiple are selected, they will be grouped according to the
Independent variable selected.
The Comparisons selection window is used to choose which column
contains the information that identifies which condition each sample
belongs to (e.g., the Species column within the iris data).
The Independent variable selection window is used to select how the
plots should be grouped. If variable is selected (the default), the
plots will be grouped by the values in Variables to plot.
Use the Shapes selector to change the shape of the points for each
comparison variable.
Use the Colors selector to change the point colors for each
comparison variable.
Use the Fills selector to change the fill color for the other geoms
being plotted for each comparison variable.
To prevent the Shapes, Colors, or Fills from reverting to their
defaults, click the Lock checkboxes.
Individual plots can be saved by clicking Save on the Plot tab or
multiple plots may be arranged on a single page by clicking
Add plot to report. Clicking this button will send the current plot to
the Report tab and assign it a number in the Report plot # dropdown
menu. To revisit a plot stored in the Report tab, select the plot you
wish to restore and click Load plot from report. Changes can be made
to this plot and then updated in the Report by clicking
Update plot in report.
The statistics calculated for the current plot being displayed in the
Plot tab are stored in the Statistics tab. These can be saved by
clicking the Download button on the Statistics tab.
The Plot Data tab contains the reorganized subset of data being
plotted.
The Raw Data tab displays the dataframe that was created upon import
of the file along with the automatically created Sheet column.
Here is the output of sessionInfo() on the system on which this
package was developed:
sessionInfo()
#> R version 4.5.1 (2025-06-13 ucrt)
#> Platform: x86_64-w64-mingw32/x64
#> Running under: Windows 11 x64 (build 26200)
#>
#> Matrix products: default
#> LAPACK version 3.12.1
#>
#> locale:
#> [1] LC_COLLATE=English_United States.utf8
#> [2] LC_CTYPE=English_United States.utf8
#> [3] LC_MONETARY=English_United States.utf8
#> [4] LC_NUMERIC=C
#> [5] LC_TIME=English_United States.utf8
#>
#> time zone: America/New_York
#> tzcode source: internal
#>
#> attached base packages:
#> [1] stats graphics grDevices utils
#> [5] datasets methods base
#>
#> other attached packages:
#> [1] rmarkdown_2.30
#>
#> loaded via a namespace (and not attached):
#> [1] digest_0.6.39 R6_2.6.1
#> [3] fastmap_1.2.0 xfun_0.56
#> [5] tidyselect_1.2.1 magrittr_2.0.4
#> [7] glue_1.8.0 tibble_3.3.0
#> [9] knitr_1.51 htmltools_0.5.9
#> [11] pkgconfig_2.0.3 dplyr_1.1.4
#> [13] generics_0.1.4 lifecycle_1.0.5
#> [15] cli_3.6.5 vctrs_0.6.5
#> [17] rsconnect_1.7.0 compiler_4.5.1
#> [19] rstudioapi_0.18.0 tools_4.5.1
#> [21] evaluate_1.0.5 pillar_1.11.1
#> [23] yaml_2.3.12 otel_0.2.0
#> [25] rlang_1.1.6