In RtutoR: Shiny Apps for Plotting and Exploratory Analysis

You can view a demo of this app here

App Usage

The app can be used as follows:

The app is launched by invoking the gen_exploratory_report_app(df) function where dfis the dataframe object to be analyzed
Choose the dependent variable from the Select Target Variable dropdown. The dropdown would contain all the column names from the dataframe object
You can change the plot theme by selecting an option from the Select Theme dropdown
The slide format can be changed as well. There are 2 possible options - 1 plot per slide or 2 plots per slide. Default value is 2. This can be changed by selecting a value from the No. of plots per slide dropdown
By default, the app would consider all the features available in the dataset for analysis. If an initial feature screening is required, set the Select Top N features? dropdown to Yes. This launches 2 additional controls - No. of features to select This takes a numeric input indicating the no. of features to select & Select feature selector method to select a filtering approach. 4 different filtering aproaches are provided (from the FSelector package)
Specify the Output report file name in the Enter the Output file name input field. If only the file name is specified, the report is saved in the current working directory.
You can also specify a grouping variable in the "Select Grouping Variable" input field. Currently, only categorical features can be selected as a Grouping variable. Selecting a Grouping variable (or multiple grouping variables) introduces an additional section to the Exploratory Analysis report - Bivariate Analysis with Grouping Variable.
Finally, to generate the report click on the Generate Report button - This will generate a presentation with the plots and summary tables.
The plots can be visualized interactively (using the plotly package). Once the report is generated, the 1st plot is displayed on the "Plot Output" box.
You can navigate to a specific plot by selecting a "Plot Category" and a specific plot within that Category.The plots are named as follows:
For Univariate analysis, plots as named as "Histogram - feature_name" for numeric features and "Bar Plot - feature_name" for categorical features
Bivaraite analysis plots are named as "Feature Name vs Target Variable"
Bivaraite analysis (with Grouping variable, if grouping variable(s) is provided) is named as "Feature Name vs Target Variable group by Group Variable name"