This R package/Shiny app is a handy interface to
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:
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.
While we are on this tab let us map Color By:, Column Split:, Linetype By: and Shape By: to Gender
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))
Now launch the application on your own data that is already in R and start exploring it:
Alternatively launch the application without any data and navigate to your csv file: