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A tool for generating figure-ready graphs from file. It borrows heavily from packages developed by others, including ggplot2 and dplyr from the tidyverse and batch statistical calculations from ggpubr.
Plots can be made using combinations of geoms including bar, violin, box, crossbar, density, point, line, and errorbar.
If you do not already have R installed, or your version is out of date, download the latest version Here.
Optionally, install the latest version of RStudio Desktop.
Download the package from Bioconductor.
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("plotGrouper")
Load the package into the R session.
To initialize the shiny app, paste the following code in your R console and run it.
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.
Raw Data tab displays the structure of the data loaded. Your file should
be organized in the following way:
knitr::kable( matrix(c("***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..."), ncol = 3, byrow = T), col.names = c("Unique identifier", "Comparisons", "Variables"), align = "c")
These columns can be titled anything you want but values in the columns are important.
Unique identifier column should contain only unique values that
identify each individual sample (e.g.,
Comparisons column should contain replicated values that identify each
individual as belonging to a group (e.g.,
Variables column(s) should created for each variable you wish
to plot. The values in these columns must be numeric (e.g.,
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.
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.
Comparisons selection window is used to choose which column contains
theinformation that identifies which condition each sample belongs to (e.g.,
Species column within 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.
Shapes selector to change the shape of the points for each
Colors selector to change the point colors for each
Fills selector to change the fill color for the other geoms being
plotted for each comparison variable.
To prevent the
Fills from reverting to their defaults,
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
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
Report by clicking
Update plot in report.
The statistics calculated for the current plot being displayed in the
tab are stored in the
Statistics tab. These can be saved by clicking the
Download button on the
Plot Data tab contains the reorganized subset of data being plotted.
Raw Data tab displays the dataframe that was created upon import of the
file along with the automatically created
Here is the output of
sessionInfo() on the system on which this package was
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