A Collection of Database, Data Structure, Visualization, and Utility Functions for R
Add new levels to the Factors in a DataFrame.
Find the "best" record within subgroups of a dataframe.
Data Import Wrapper for dbWriteTable.
Distribution plot of points
Modify defaults of possible optional ellipsis parameter values passed ...
Report a model fit in a single line of text output
Calculate the Geometric Mean
Group a datafame by a factor and perform aggreate functions.
Simple Heatmap Plot
Annotate Outliers in a Scatterplot via an HTML Image-Map
Install the next oldest package
Buffered Segments for Point Labels
automatically find an optimal position a plot legend based on point co...
Generate a Color Coded Legend dataframe via head and sum.
Regexp Match Operator
Make Elipse Coordinates
Move Text Labels Interactively
Named Merge
Create a named vector from a dataframe, table or vector
Pad a vector of numerical string with zeros.
Process Command Line Arguments
Add Percentage Columns to a Dataframe
Pie chart scatterplot
Plot a grid of x y plots split by a confounder z
Visually compare all points from different univariate distributions
An x/y scatterplot with a linear regression line and p-value
Plot a simple clock.
Add Arms to a RA plot.
Add axis labels to an RA plot.
Add Significance Lines to an RA plot.
Generate a Ratio Average [RAy] Plot.
Read in a Tab Delimited File.
Regroup a dataframe.
Rename select rows of a dataframe
Spie charts
Sum Sorted Tabulation
Table to Data Frame
A Text-Only Plot
Grab and adjust the current plot dimensions
Create a Venn Ready Matrix out of a List of Factors
Weighted Jitter
Write a (tab) delimited text file.
The caroline R library contains dozens of functions useful for: database migration (dbWriteTable2), database style joins & aggregation (nerge, groupBy, & bestBy), data structure conversion (nv, tab2df), legend table making (sstable & leghead), automatic legend positioning for scatter and box plots (), plot annotation (labsegs & mvlabs), data visualization (pies, sparge, confound.grid & raPlot), character string manipulation (m & pad), file I/O (write.delim), batch scripting, data exploration, and more. The package's greatest contributions lie in the database style merge, aggregation and interface functions as well as in it's extensive use and propagation of row, column and vector names in most functions.