ggvis is currently dormant. We fundamentally believe in the ideas that underlie ggvis: reactive programming is the right foundation for interactive visualisation. However, we are not currently working on ggvis because we do not see it as the most pressing issue for the R community as you can only use interactive graphics once you've successfuly tackled the rest of the data analysis process.
The goal of ggvis is to make it easy to describe interactive web graphics in R. It combines:
a grammar of graphics from ggplot2,
reactive programming from shiny, and
data transformation pipelines from dplyr.
ggvis graphics are rendered with vega, so you can generate both raster graphics with HTML5 canvas and vector graphics with svg. ggvis is less flexible than raw d3 or vega, but is much more succinct and is tailored to the needs of exploratory data analysis.
If you find a bug, please file a minimal reproducible example at https://github.com/rstudio/ggvis/issues. If you're not sure if something is a bug, you'd like to discuss new features or have any other questions about ggvis, please join us on the mailing list: https://groups.google.com/group/ggvis.
Install the latest release version from CRAN with:
Install the latest development version with:
# install.packages("devtools") devtools::install_github("hadley/lazyeval", build_vignettes = FALSE) devtools::install_github("hadley/dplyr", build_vignettes = FALSE) devtools::install_github("rstudio/ggvis", build_vignettes = FALSE)
You construct a visualisation by piping pieces together with
%>%. The pipeline starts with a data set, flows into
ggvis() to specify default visual properties, then layers on some visual elements:
mtcars %>% ggvis(~mpg, ~wt) %>% layer_points()
The vignettes, available from https://ggvis.rstudio.com/, provide many more details. Start with the introduction, then work your way through the more advanced topics. Also check out the
various demos in the
demo/ directory. See the basics in
then check out the the coolest demos,