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Introduction to ggquickeda
Samer Mouksassi
2025-02-17
Source:vignettes/ggquickeda.Rmd
ggquickeda.Rmd
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:
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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)
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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:
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While we are on this tab let us map Color By:, Column Split:, Linetype By: and Shape By: to Gender
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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))
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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()