Figures • g2r

g2r comes with a number of figures that can be used to create visualisations. However, these can be used in many different ways than what is presented here in order to produce completely different looking charts; see examples, and methods.

See the graph vignette for network-related figures.

Line

The line can be controlled with shape:

smooth
dot
vh
hv
hvh
vhv
dash
line (default).

df <- data.frame(
  x = 1:99,
  y = rnorm(99, mean = rep(c(65,70,75),each=33), sd = 2) 
)

g2(df, asp(x, y)) %>% 
  fig_line()

Step

A step chart is a line chart with a shape, one of: vh, hv, hvh, or vhv.

g2(df, asp(x, y)) %>% 
  fig_line(asp(shape = "hv"))

Point

The shape of the point can also be set to:

circle
bowtie
cross
diamond
hexagon
hollow-bowtie
hollow-circle (default)
hollow-diamond
hollow-hexagon
hollow-triangle
hollow-triangle-down
line
hyphen
plus
tick
square

g2(iris, asp(Sepal.Width, Sepal.Length, color = Species)) %>% 
  fig_point()

Area

The shape of the area can also be set to:

area (default)
line
smooth
smooth-line

blockchain <- jsonlite::fromJSON(
  "https://gw.alipayobjects.com/os/antvdemo/assets/data/blockchain.json"
)

g2(blockchain, asp(date, blockchain)) %>% 
  fig_area()

Path

df <- data.frame(
  x = runif(50),
  y = runif(50) 
)

g2(df, asp(x, y)) %>% 
  fig_path()

Interval

The shape of the interval can also be set to:

rect (default)
funnel
hollow-rect
line
pyramid
tick
triangle

df <- data.frame(
  x = seq.Date(Sys.Date() - 29, Sys.Date(), by = "days"),
  y = rnorm(30, mean = rep(c(70,60),each=15), sd = 3)
)

g2(df, asp(x, y)) %>% 
  fig_interval()

Polygon

df <- expand.grid(x = letters, y = letters)
df$value <- rnorm(nrow(df))

g2(df, asp(x, y, color = value)) %>% 
  fig_polygon() %>% 
  gauge_color_blue()

Heatmap

g2(iris, asp(Sepal.Length, Sepal.Width, color = Petal.Length)) %>% 
  fig_heatmap() %>% 
  gauge_color(c("blue", "cyan", "lime", "yellow", "red"))

Bin

Use count (internally computed) to define the color (optional).

g2(cars, asp(speed, dist, color = count)) %>% 
  fig_bin() %>% 
  gauge_color_blue()

Use size_count to size the bins by count.

data(diamonds, package = "ggplot2")

g2(diamonds, asp(table, price, color = count)) %>% 
  fig_bin(
    type = "hex", 
    size_count = FALSE,
    stroke = "#fff"
  ) %>% 
  gauge_color_blue()

Ribbon

df <- data.frame(
 x = 1:50,
 y = rnorm(50, mean = rep(c(67,73),each=25), sd = 2)
)

df$ymin <- df$y - runif(50, 2, 3)
df$ymax <- df$y + runif(50, 2, 4)

g2(df, asp(x, ymin = ymin, ymax = ymax)) %>% 
  fig_line(asp(y = y)) %>% 
  fig_ribbon()

Histogram

df <- data.frame(
 grp = rep(c("A", "B"), each = 200),
  val = c(
    rnorm(200, mean = 57, sd = 5), 
    rnorm(200, mean = 53, sd = 5)
  )
)

g2(df, asp(val, color = grp)) %>% 
 fig_histogram(bin_width = 1, fillOpacity = .5)

Density

g2(iris, asp(Sepal.Length, color = Species)) %>% 
  fig_density()

Boxplot

df <- tidyr::pivot_longer(iris, -Species)

g2(df, asp(name, value, color = Species)) %>% 
 fig_boxplot(adjust("dodge"))

Smooth

g2(cars, asp(speed, dist)) %>% 
  fig_point() %>% 
  fig_smooth(asp(shape = "smooth"), method = "polynomial")

Range

Two types: interval and area.

df <- data.frame(
 x = 1:10,
 ymin = runif(10, 1, 5),
 ymax = runif(10, 6, 13)
)

g2(df, asp(x, ymin = ymin, ymax = ymax)) %>% 
 fig_range()

g2(df, asp(x, ymin = ymin, ymax = ymax)) %>% 
 fig_range(type = "area")

Pie

df <- data.frame(
 name = letters[1:5],
 value = runif(5)
)

g2(df, asp(y = value, color = name, label = name)) %>% 
 fig_pie()

Voronoi

df <- dplyr::tibble(
  x = runif(50, 1, 500),
  y = runif(50, 1, 500),
  value = runif(50, 1, 500)
)

g2(df, asp(x, y, color = value)) %>% 
  fig_voronoi() %>% 
  gauge_x_linear(nice = FALSE) %>% 
  gauge_y_linear(nice = FALSE) %>% 
  gauge_color_blue()

Waffle

fruits <- dplyr::tibble(
  fruit = c("Apples", "Bananas", "Pears", "Oranges"),
  value = c(.45, .15, .35, .05) * 100
)

g2(fruits, asp(value, color = fruit)) %>% 
  fig_waffle() %>% 
  motif(padding = c(10, 10, 30, 10)) %>% 
  axis_hide()

Rug

g2(mtcars, asp(wt, mpg)) %>% 
  fig_point() %>% 
  fig_smooth(asp(shape = "smooth"), method = "polynomial") %>% 
  fig_rug(asp(size = 10)) %>% 
  fig_rug(asp(size = 10), axis = "y")

Candle

wallgreens  <- tidyquant::tq_get("WBA", from = Sys.Date() - 90)
#> Registered S3 method overwritten by 'quantmod':
#>   method            from
#>   as.zoo.data.frame zoo
#> Registered S3 method overwritten by 'ggplot2':
#>   method        from
#>   print.element g2r

cb <- htmlwidgets::JS(
  "(trend) => {
    if(trend == 'Up')
      return 'green';
    
    return 'red';
  }"
)

g2(wallgreens, asp(date, open = open, close = close, high = high, low = low)) %>% 
  fig_candle() %>% 
  gauge_x_time_cat() %>% 
  gauge_color(cb)

Error bars

df <- data.frame(
  x = as.factor(c(1:10, 1:10)),
  y = runif(20, 15, 25),
  grp = rep(c("A", "B"), each = 2)
)

df$ymin <- df$y - runif(20, 1, 3)
df$ymax <- df$y + runif(20, 1, 3)

g2(df, asp(x = x, color = grp)) %>% 
  fig_error(
    asp(ymin = ymin, ymax = ymax, size = 10), 
    adjust("dodge")
  ) %>% 
  fig_interval(
    asp(y = y), 
    adjust("dodge"),
    fillOpacity = .4
  )

Contour

data(faithfuld, package = "ggplot2")

g2(faithfuld, asp(waiting, eruptions, z = density)) %>% 
  fig_contour(
    binwidth = 0.001,
    colors = c("#440154FF", "#21908CFF", "#FDE725FF")
  )

g2(faithfuld, asp(eruptions, waiting, z = density)) %>% 
  fig_contour(
    type = "filled", 
    colors = c("#000004FF", "#B63679FF", "#FCFDBFFF")
  ) %>% 
  gauge_x_linear(nice = FALSE) %>% 
  gauge_y_linear(nice = FALSE)

Segments

seg <- df <- data.frame(
  x = c(24, 23),
  y = c(70, 54),
  xend = c(17, 24),
  yend = c(50, 93)
)

g2(cars, asp(speed, dist)) %>% 
  fig_point() %>% 
  fig_segment(
    asp(x = x, y = y, xend = xend, yend = yend),
    data = seg
  )

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