Chapter 6 GGvis cheatsheet | Spring 2021 EDAV Community Contributions

6.1 ggvis

Official website: http://ggvis.rstudio.com/

ggvis is a visualization package that can: - plot the graph in the same way as ggplot2 - create interaction strategies where you can interact with the graph locally or in the website

6.1.1 Getting start

First thing first, we shall import some necessary packages

#install.packages("dplyr")
#install.packages("ggvis")

library(ggvis)
library(dplyr)

This is a simple demonstration on how to use ggvis. As can be seen, ggvis can create graph in the same way ggplot does!

p <- ggvis(mtcars, x = ~wt, y = ~ mpg)
layer_points(p)

Similar way to write basic ggvis

layer_points(ggvis(mtcars, x = ~wt, y = ~ mpg))

Apply pipeline to write ggvis

mtcars %>%
  ggvis(x = ~wt, y = ~mpg) %>%
  layer_points

We can add a theme on it

mtcars %>% 
ggvis(x = ~wt, y = ~mpg) %>% 
layer_points() %>%
  add_axis("x", title = "Weight", ticks = 40,
    properties = axis_props(
      ticks = list(stroke = "red"),
      majorTicks = list(strokeWidth = 2),
      grid = list(stroke = "red"),
      labels = list(
        fill = "steelblue",
        angle = 50,
        fontSize = 14,
        align = "left",
        baseline = "middle",
        dx = 3
      ),
      title = list(fontSize = 16),
      axis = list(stroke = "#333", strokeWidth = 1.5)
    )
  )

6.2 aesthetics

Try to adjust the parameters

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

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

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

mtcars %>% 
  ggvis(~mpg, ~disp, shape = ~factor(cyl)) %>% 
  layer_points()

mtcars %>% 
  ggvis(~wt, ~mpg, fill := "red",  stroke := "black") %>% 
  layer_points()

mtcars %>% 
  ggvis(~wt, ~mpg, size := 300, opacity := 0.4) %>% 
  layer_points()

6.3 Interaction

6.3.1 basic interaction

mtcars %>% ggvis(x = ~wt, y = ~mpg) %>%
  layer_points() %>%
  layer_smooths(
    span = input_slider(0.2, 1, value = 0.5, step = 0.05, label = "span")
  )

Next, we add some interaction strategies into it. We can add the following interaction into the graph: - Change the size of the dot - Change the opacity of the dot

mtcars %>% 
   ggvis(~wt, ~mpg, 
         size := input_slider(10, 100),
         opacity := input_slider(0, 1)
   ) %>% 
   layer_points()

Add interaction strategies to a histograms - Change the width of the histograms - Change the center of the histograms

mtcars %>% 
   ggvis(~wt) %>% 
   layer_histograms(
                    width =  input_slider(0, 2, step = 0.10, label = "width"),
                    center = input_slider(0, 2, step = 0.05, label = "center"))

Add tooltip

mtcars %>%
   ggvis(~wt, ~mpg) %>% 
   layer_points()%>%
   add_tooltip(function(df) df$wt)

6.3.2 Input option

6.3.2.1 input_select()

mtcars %>% ggvis(x = ~wt) %>%
  layer_densities(
    adjust = input_slider(.1, 2, value = 1, step = .1, label = "Bandwidth adjustment"),
    kernel = input_select(
      c("Gaussian" = "gaussian", "Epanechnikov" = "epanechnikov",
        "Rectangular" = "rectangular", "Triangular" = "triangular",
        "Biweight" = "biweight", "Cosine" = "cosine", "Optcosine" = "optcosine"),
      label = "Kernel")
  )

6.3.2.2 Checkbox input

mtcars %>% ggvis(x = ~wt, y = ~mpg) %>%
  layer_points(
    opacity := input_checkbox(label = "Semi-transparent",
                                    map = function(val) ifelse(val, .3, 1))) %>%
  layer_model_predictions(
    model = input_checkbox(label = "LOESS (curve) model fit",
                            map = function(val) ifelse(val, "loess", "lm")))

6.3.2.3 Text input

mtcars %>% ggvis(x = ~wt, y = ~mpg) %>%
  layer_points(fill := input_text(label = "Point color", value = "red")) %>%
  layer_model_predictions(model = input_text(label = "Model type", value = "loess"))

6.3.2.4 Numeric input

mtcars %>% ggvis(x = ~wt, y = ~mpg) %>%
  layer_points(size := input_numeric(value = 25, label = "Point size")) %>%
  layer_smooths(span = input_numeric(value = 0.5, label = "Interpolation points"))

6.3.2.5 Radio buttons

mtcars %>% ggvis(x = ~wt, y = ~mpg) %>%
  layer_points() %>%
  layer_model_predictions(
    model = input_radiobuttons(c("LOESS" = "loess", "Linear" = "lm"),
      label = "Model type"),
    stroke := input_radiobuttons(c("Red" = "red", "Black" = "black"),
      label = "Line color")
  )

6.3.2.6 Checkbox group

mtcars %>% ggvis(x = ~wt, y = ~mpg) %>%
  layer_points(
    fill := input_checkboxgroup(
      choices = c("Red" = "r", "Green" = "g", "Blue" = "b"),
      label = "Point color components",
      map = function(val) {
        rgb(0.8 * "r" %in% val, 0.8 * "g" %in% val, 0.8 * "b" %in% val)
      }
    )
  )

6.3.3 Map

6.3.3.1 Example with value map function

mtcars %>% ggvis(x = ~wt, y = ~mpg) %>%
  layer_points() %>%
  layer_model_predictions(model = "loess",
    model_args = list(n = input_select(
      choices = c("Two", "Six", "Eighty"),
      map = function(value) switch(value, Two = 2, Six = 6, Eighty = 80),
      label = "Number of points"
    ))
  )

new_vals <- input_select(c("Set A" = "A", "Set B" = "B"),
  label = "Dynamically-generated column",
  map = function(value) {
    vals <- switch(value,
      "A" = rep(c("One", "Two")),
      "B" = c("First", "Second", "Third", "Fourth"))
    rep(vals, length = nrow(mtcars))
  })

mtcars %>% ggvis(x = ~wt, y = ~mpg, fill = new_vals) %>%
  layer_points()

mtcars %>% ggvis(
    x = ~wt,
    y = ~mpg,
    fill = input_select(c("mpg", "wt"), map = as.name)
  ) %>%
  layer_points()

6.4 Layers

6.4.1 Simple layers

include primitives like points, lines and rectangles.

mtcars %>% 
  ggvis(~wt, ~mpg) %>% 
  layer_points()

Paths and polygons

df <- data.frame(x = 1:10, y = runif(10))
df %>% ggvis(~x, ~y) %>% layer_paths()

If you add fill, you will get a polygon

t <- seq(0, 2 * pi, length = 100)
df <- data.frame(x = sin(t), y = cos(t))
df %>% ggvis(~x, ~y) %>% layer_paths(fill := "red")

df <- data.frame(x = 1:10, y = runif(10))
df %>% ggvis(~x, ~y) %>% layer_ribbons()

Rectangle

set.seed(1014)
df <- data.frame(x1 = runif(5), x2 = runif(5), y1 = runif(5), y2 = runif(5))
df %>% ggvis(~x1, ~y1, x2 = ~x2, y2 = ~y2, fillOpacity := 0.1) %>% layer_rects()

Text

df <- data.frame(x = 3:1, y = c(1, 3, 2), label = c("a", "b", "c"))

df %>% ggvis(~x, ~y, text := ~label) %>% layer_text(fontSize := 50)

6.4.2 Compound layers

df <- data.frame(x = sin(t), y = cos(t))

df %>% ggvis(~x, ~y) %>% layer_paths()

df %>% ggvis(~x, ~y) %>% layer_lines()

mtcars %>% ggvis(~mpg) %>% layer_histograms()

mtcars %>% ggvis(~wt, ~mpg) %>% layer_smooths()