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Data Visualization in R: ggvis continued Cheat Sheet by

ggvis & Group_by

When these 2 are used in conjun­ction, we can create powerful visual­iza­tions.

Code:
train_tbl %>%
group­_by­(se­ason) %>%
ggvis­(~t­emp­_f,­~count, stroke = ~facto­r(s­eason)) %>%
layer­_sm­oot­hs()
Here, season is a catego­rical variable. And we have grouped it and then used stroke to highlight the different seasons.

Output

In-Built plot types

1. layer_points()
2. layer_lines()
3. layer_bars()
4. layer_smooths()
5. layer_histograms()
Most popular ones cited

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 visual­ization can be adjusted with scale().
ggvis provides several different functions for creating scales:
scale_­dat­eti­me(), scale­_lo­gic­al(), scale_­nom­inal(), scale_­num­eric(), scale_­sin­gul­ar()

Code
faithful %>%
ggivs­(~e­rup­tio­ns,­~wa­iting, fill = ~erupt­ions) %>%
layer­_po­ints() %>%
scale­_nu­mer­ic(­"­fil­l", range = c("r­ed",­"­ora­nge­"))

Output

 

ggvis & intera­ction ()

We can also group data based on intera­ction of two or more variables. group­_by() creates unique groups for each distinct combin­ation of values within the grouping variables.

ungro­up() can remove the grouping inform­ation.
inter­act­ion() can map the properties to unique combin­ations of the variables

Code:

train_tbl %>%
group­_by­(se­aso­n,h­oliday) %>%
ggvis­(~c­ount, fill = ~inter­act­ion­(se­aso­n,h­oli­day)) %>%
layer­_de­nsi­ties()

Output

Model Prediction

layer_­mod­el_­pre­dic­tions() plots the prediction line of a model fitted to the data.
layer­_mo­del­_pr­edi­cti­ons­(model = "­lm")

Code:

faithful %>%
ggvis(­~er­upt­ion­s,~­wai­ting) %>%
layer_­poi­nts­(fill := "­gre­en", fillOp­acity := 0.5) %>%
layer_­mod­el_­pre­dic­tio­ns(­model = "­lm", stroke := "­red­") %>%
layer_­smo­oth­s(s­troke := "­sky­blu­e")

Output

 

Intera­ctive Plots

ggivs comes several widgets such as
input_­che­ckb­ox(),
input­_ch­eck­box­gro­up(),
input­_nu­mer­ic(),
input­_ra­dio­but­ton­s(),
input_­sel­ect(),
input­_sl­ider(), and input_­tex­t().

label = "ABCD " , choices = c("r­ed",­"­bla­ck") -
value = "­bla­ck" - Used with input_­text()
map = as.name used when we want to return variable names

Are the common arguments inside these functions.

Output

Legends & Axis

Axis
You can add axes with add_a­xis()

Syntax:
faithful %>%
ggvis­(~e­rup­tio­ns,­~wa­iting) %>%
add_a­xis­("x", label = "­Eru­pti­ons­", values = c(1,2,­3,4), subdivide = 9, orient = top") %>%
layer­_po­ints()

Lege­nds
ggvis adds a legend for each property that is specified. To combine multiple legends into a single legend with common values, use a vector of property names.
add_l­ege­nd()
hide_­leg­end()

Syntax
faithful %>%
ggvis(­~wa­iting, ~erupt­ions, opacity := 0.6,
fill = ~facto­r(r­oun­d(e­rup­tio­ns)), shape = ~facto­r(r­oun­d(e­rup­tio­ns)),
size = ~round­(er­upt­ions)) %>%
layer_­poi­nts() %>%
add_le­gen­d(c­("fi­ll", "­sha­pe", "­siz­e"),
title = "~ duration (m)", values = c(2, 3, 4, 5))
   

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