Introduction to ggquickeda

Samer Mouksassi

2019-10-07

This package/R 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 . To get started:

devtools::install_github("smouksassi/ggquickeda")
library(ggquickeda)

Let us start by launching the app 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:

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

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:

select sample_df.csv 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
* 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) select sample_df.csv

Now launch the application on your own data and start exploring: run_ggquickeda(yourdataname)