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ggvis_18BCE2193 Cheat Sheet by

R is a language used popularly for Data Visualization. ggvis is a data visualization package for R which lets you: 1.Declaratively describe data graphics with a syntax similar in spirit to ggplot2. 2.Create rich interactive graphics that you can play with locally in Rstudio or in your browser. 3.Leverage shiny’s infrastructure to publish interactive graphics usable from any browser (either within your company or to the world).


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


Simple Layer
Multiple Layer
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.

Scale Types

Any visual property in the visual‐ ization can be adjusted with scale(). ggvis provides several different functions for creating scales:

Model Prediction

faithful %>%
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).

Simple Layer

mtcars %>% ggvis(mpg, disp,fill = ~vs) %>% layer_­poi­nts()

Scale Types (cont)

scale_lo‐ gical(),
scale_sin‐ gular()
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

1. layer_­poi­nts()
2. layer_­lines()
3. layer_­bars()
4. layer_­smo­oths()
5. layer_­his­tog­rams()

Multiple Layer

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)) %>%

Intera­ctive Plots

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 = used to return variable nam

Are the common argu these functions.


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