install.packages("ggvis") will install all the required packages you need for visualization through ggvis
-library(ggvis) will call the ggvis package
to be used in your visualization
Here I am using the dataset mtcars and visualising it through layer points.
I have taken the mtcars dataset and visualized the multiple layers using different strokes
Global Vs Local properties
A property that is set inside ggvis() is applied globally. While a property set inside layer_<marks>() is applied locally. Local properties can override global properties when applicable.
Any visual property in the visual‐ ization can be adjusted with scale(). ggvis provides several different functions for creating scales:
layer_points(fill := "gree‐ n", fillOpacity := 0.5) %>% layer_model_predictions(‐ model = "lm", stroke := "re‐ d") %>%
layer_smooths(stroke := )
The goal is to combine the best of R (e.g. every modelling function you can imagine) and the best of the web (everyone has a web browser). Data manipulation and transformation are done in R, and the graphics are rendered in a web browser, using Vega. For RStudio users, ggvis graphics display in a viewer panel, which is possible because RStudio is a web browser.
The graphics produced by ggvis are fundamentally web graphics and work very differ‐ ently from tradit‐ ional R graphics. This allows us to implement exciting new features like interactivity
The goal of ggvis is to make it easy to build interactive graphics for explor‐ atory data analysis. ggvis has a similar underlying theory to ggplot2 (the grammar of graphics).
mtcars %>% ggvis(~mpg, ~disp,fill = ~vs) %>% layer_points()
Scale Types (cont)
Code faithful %>%ggivs(~eruptions,~waiting, fill = ~eruptions) %>% layer_points() %>%scale_numeric("fill", range)
More about ggvis
1.Differences and similarities to ggplot2.
2.The relationship between ggvis and Vega
Popular In-Built plot types
mtcars %>% ggvis(~wt,~mpg) %>% layer_smooths(span= 1) %>%layer_smooths(span
= 0.3, stroke := "‐ red")
ggvis & interaction ()
train_tbl %>% group_by(season,holiday) %>% ggvis(~count, fill = ~inter‐ action(season,holiday)) %>%
ggivs comes several
input_checkbox(), input_checkboxgroup input_numeric(), input_radiobuttons(), input_select(), input_slider(), and inp
label = "ABCD " , cho black") -
value = "black" - Use text()
map = as.name used to return variable nam
Are the common argu these functions.