granovaGG

Create what we call Elemental Graphics for display of anova results. The term elemental derives from the fact that each function is aimed at construction of graphical displays that afford direct visualizations of data with respect to the fundamental questions that drive the particular anova methods. This package represents a modification of the original granova package; the key change is to use 'ggplot2', Hadley Wickham's package based on Grammar of Graphics concepts (due to Wilkinson). The main function is granovagg.1w() (a graphic for one way ANOVA); two other functions (granovagg.ds() and granovagg.contr()) are to construct graphics for dependent sample analyses and contrast-based analyses respectively. (The function granova.2w(), which entails dynamic displays of data, is not currently part of 'granovaGG'.) The 'granovaGG' functions are to display data for any number of groups, regardless of their sizes (however, very large data sets or numbers of groups can be problematic). For granovagg.1w() a specialized approach is used to construct data-based contrast vectors for which anova data are displayed. The result is that the graphics use a straight line to facilitate clear interpretations while being faithful to the standard effect test in anova. The graphic results are complementary to standard summary tables; indeed, numerical summary statistics are provided as side effects of the graphic constructions. granovagg.ds() and granovagg.contr() provide graphic displays and numerical outputs for a dependent sample and contrast-based analyses. The graphics based on these functions can be especially helpful for learning how the respective methods work to answer the basic question(s) that drive the analyses. This means they can be particularly helpful for students and non-statistician analysts. But these methods can be of assistance for work-a-day applications of many kinds, as they can help to identify outliers, clusters or patterns, as well as highlight the role of non-linear transformations of data. In the case of granovagg.1w() and granovagg.ds() several arguments are provided to facilitate flexibility in the construction of graphics that accommodate diverse features of data, according to their corresponding display requirements. See the help files for individual functions.

Help pages

12includes a package overview page

Examples that run

100%3 run in a plain check

Exported functions with examples

100%

Documented parameters

100%

Return-value docs

100%

Help pages citing references

83%

Also includes 3 demo scripts (run with demo())

Installing granovaGG adds 29 packages (15 with compiled code, 28 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, isoband 1.5 MB.

Pick two releases to compare their code metrics.

First published

2011-09-03

Total releases

9 / 15 yrs

License

MIT + file LICENSE OSI · relicensed from GPL (>= 2) in 1.4.1

Minimum R

≥ 2.14.0

Bundled data

3.0 KB / 8 files

Download size

42 KB

Cite this package

Run in R for the authors' entry:

The package provides a citation file, so citation() returns the authors' own entry.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

From data release v2026-10-21, which the citation names so these numbers can be found later. More on citing and the projects behind them.