# Introduction to ggquickeda

#### Samer Mouksassi

#### 2026-02-01

This R package/Shiny app is 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 tables. For a quick overview using an older version of the app head to this Youtube Tutorial . This intro will walk you through making a plot and a summary table.

    # Install from CRAN:
    install.packages("ggquickeda")
    library(ggquickeda)
    run_ggquickeda()

After launching the app with `run_ggquickeda()` and clicking on use sample_data: The app will load the built-in example dataset and map the first column to y variable(s) and the second column to x variable and a simple scatter plot with points will be generated:

select sample_df.csv

We want to look at the Column Conc (concentration of drug in blood) versus Time joining each Subject data with a line:

- Change the mapped y variable(s) from ID to Conc (remove the default selection of ID by clicking on the small x and then select Conc)
- Switch to the Points, Lines tab and select Lines (you can also choose another symbol for points and play with point sizes and transparency)

select sample_df.csv

Wait something is wrong! We forgot to tell the app that we want to group by ID.

- Go Back to Color/Group/Split/Size/Fill Mappings tab and select ID for the Group By:

select sample_df.csv

While we are on this tab let us map Color By:, Column Split:, Linetype By: and Shape By: to Gender

select sample_df.csv

Now we want to add a loess trend line: * Go to Smooth/Linear/Logistic Regressions and click on the Smooth radio button:

After we made the plot we wanted, now we are interested to do a summary statistics of Weight and Age columns by Gender this will require the following steps: * Change the mapped y variable(s) to Weight, Age and Race * Change the mapped x variable to Gender * Go to One Row by ID(s) and select ID so we keep one row by ID
* Go to Descriptive Stats tab (notice how you can use html codes for line breaks, superscript and subscript in the Quick HTML Labels. e.g. Weight(kg))

select sample_df.csv

Now launch the application on your own data that is already in R and start exploring it: `run_ggquickeda(yourdataname)`

Alternatively launch the application without any data and navigate to your csv file: `run_ggquickeda()`