CRAN Travis-CI Build Status DOWNLOADS

Installation and Running information

# Install from CRAN:

# Or the development version from GitHub:
# install.packages("devtools")


To launch the application, use run_ggquickeda() then navigate to your csv file (or run_ggquickeda(data) to launch the app with a specific dataset already loaded).

R Shiny app/package as a handy interface to ggplot2/table1. It enables you to quickly explore your data to detect trends on the fly. You can do scatter plots, dotplots, boxplots, barplots, histograms, densities and summary statistics of multiple variable(s) by column(s) splits. For a quick overview using an older version of the app head to this Youtube Tutorial .

Export Plots and Plot Code tabs contributed by Dean Attali. Once a plot is saved in the X/Y Plot tab by providing a name and hitting the Save plot star button it will become available for exporting. You can export in portrait, landscape and multiple plots per page.
Plot Code will let you look at the source code that generated the plot with the various options. This is helpful to get you to know ggplot2 code.

Quick summary statistics tables using Benjamin Rich table1 package.

The best way to learn is to load a data your are familiar with and start experimenting. Try to reproduce the steps below using the included sample_df.csv. This will give you an idea on the kind of ouputs that can be generated.

The package has also two vignettes.

Example 1

Example use case 1 with the included sample_df.csv.

Example 2

Example use case 2 with the included sample_df.csv.

Example 3

Example use case 3 with the included sample_df.csv.

Example 4

Example use case 4 with the included sample_df.csv.

Here is an overview of some of the things that can be done with the various menus:



Choose csv file to upload or use sample data This execute the code to load your csv file or the internal sample_data.csv:

read.csv("youruploadeddata.csv",na.strings = c("NA","."))

Once your data is uploaded the first column will be selected for the y variable(s): and the second column for the x variable:, respectively. A simple scatter plot of y versus x variables is shown. ggquickeda can handle one or more y variable(s) selections but only one x variable. Note that the x variable should be different from those selected for y variable(s). Whether the user selects one or more y variable(s), the y variable(s) data will be automatically stacked (gathered) into two columns named yvalues (values) and yvars (identifier from which variable the value is coming from) and a scatter plot of yvalues versus x, faceted plot by yvars will be shown. Mixing categorical and continuous variables will render all yvalues to be treated as character. The order of the selected y variables(s) matters and can be changed via drag and drop. Selections can be removed by clicking on the small x. When no y variable(s) is selected a histogram (if x variable is continuous) or a barplot (if x variable is categorical) is shown.

Data Manipulations

After selecting your y variable(s) if any and x variable you can directly proceed into data manipulation within the Inputs tab using the following subtabs. Note that the subtabs execution is sequential i.e. each subtab actions are executed in the order they appear. If the user changes an upstream action this will reset the subsequent ones.

Graph Options

Various options to tweak the plot: * Controlling y and x axis labels, legends and other commonly used theme options. * Adding a title, subtitle and a caption

How To

A shorter version of this walk-through within the app.

X/Y Plot

Main plot is output here with the various options to generate the plot below the possibilities include:

Summary/Regression functions

Additional Information

Installing the package should handle the installation of all dependencies. There are listed here in case you are curious:


The app can also be directly launched using this command

shiny::runGitHub('ggquickeda', 'smouksassi', subdir = 'inst/shinyapp')