I’d like to announce the release of version 0.7 of my R package for data visualization and give a small overview of this package (download and installation instructions can be found on the package page, detailed examples on RPubs).
What does this package do?
In short, the functions in this package mostly do two things:
- compute basic or advanced statistical analyses
- either plot the results as ggplot-diagram or print them as html-table
However, meanwhile the amount of functions has increased, hence you’ll also find some utility functions beside the plotting functions.
How does this package help me?
Basically, this package either helps those users…
- who have difficulties using and/or understanding all possibilities that ggplot offers to create plots, simply by providing intuitive function parameters, which allow for manipulating the appearance of plots; or
- who don’t want to set up complex ggplot-object each time from the scratch; or
- like quick inspections of (basic) statistics via (html-)tables that are shown in the GUI viewer pane or default browser; or
- want easily create beautiful table outputs that can be imported in office applications.
Furthermore, for advanced users, each functions returns either the prepared ggplot-object (in case of
sjp-plotting functions) or the HTML-tables (in case of
sjt-table-output functions), which than can be manipulated even further (for instance, for ggplot-objects, you can specify certain parameters that cannot be modified via the sjPlot package or html-tables could be integrated into knitr-documents).
What are all these functions about?
There’s a certain naming convention for the functions:
- sjc – collection of functions useful for carrying out cluster analyses
- sji – collection of functions for data import and manipulation
- sjp – collection plotting functions, the “core” of this package
- sjt – collection of function that create (HTML) table outputs (instead of ggplot-graphics
- sju – collection of statistical utility functions
- You can plot results of Anova, correlations, histograms, box plots, bar plots, (generalized) linear models, likert scales, PCA, proportional tables as bar chart etc.
- You can create plots to analyse model assumptions (lm, glm), predictor interactions, multiple contigency tables etc.
- You can create table outputs instead of graphs for most plotting functions
- With the import and utility functions, you can, for instance, extract beta coefficients of linear models, convert numeric scales into grouped factors, perform statistical tests, import SPSS data sets (and retrieve variable and value labels from the importet data), convert factors to numeric variables (and vice versa)…
At the bottom of my package page you’ll find some examples of selected functions that have been published on this blog before I created the package. Furthermore, the package includes a sample dataset from one of my research projects. Once the package is installed, you can test each function by running the examples. All news and recent changes can be found in the NEWS section of the package help (type
?sjPlot to access the help file inside R).
I tried to write a very comprehensive documentation for each function and their parameters, hopefully this will help you using my package…
Any comments, suggestions etc. are very welcome!