plater makes it easy to work with data from experiments performed in plates. It is aimed at scientists and analysts who deal with microtiter plate-based instruments.
plater is available through CRAN. Just run:
Getting your data in
Many scientific instruments (such as plate readers and qPCR machines) produce data in tabular form that mimics a microtiter plate: each cell corresponds to a well as physically laid out on the plate. For experiments like this, it's often easiest to keep records of what was what (control vs. treatment, concentration, sample type, etc.) in a similar plate layout form.
But data in those dimensions aren't ideal for analysis. That's where
add_plate() come in.
read_plate()takes data in plate layout form and converts it to a data frame, with one well per row, identified by well name.
add_plate()does the same thing, but merges the new columns into an existing data frame you provide.
In other words, these functions seamlessly convert plate-shaped data (easy to think about) into tidy data (easy to analyze).
To make it even easier, if you have multiple plates in an experiment, use
read_plates() to read them all in and combine them into a single data frame.
Seeing your data
Sometimes it's useful to map your data back onto a plate (are the weird outliers all from the same corner of the plate?). For that, there's
view_plate(), which takes a data frame with one well per row, and lays it out like it's on a plate.
For a detailed example of how to use
plater, check out the vignette.
Code of conduct
plater is developed under a Contributor Code of Conduct. To contribute to its development, you must agree to abide by its terms.