Grid Graphics Data Visualization with ggplot2 ggplot2 internals 35 - - PowerPoint PPT Presentation

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Grid Graphics Data Visualization with ggplot2 ggplot2 internals 35 - - PowerPoint PPT Presentation

Aug 14, 2023 198 likes •986 views

DATA VISUALIZATION WITH GGPLOT2 Grid Graphics Data Visualization with ggplot2 ggplot2 internals 35 Explore grid graphics 30 Elements of ggplot2 plot 25 mpg How do graphics work in R? 20 2 plo ing systems 15 10

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SLIDE 1

DATA VISUALIZATION WITH GGPLOT2

Grid Graphics

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SLIDE 2

Data Visualization with ggplot2

ggplot2 internals

  • Explore grid graphics
  • Elements of ggplot2 plot
  • How do graphics work in R?
  • 2 ploing systems
  • base package
  • grid graphics

10 15 20 25 30 35 2 3 4 5

wt mpg

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SLIDE 3

Data Visualization with ggplot2

base package

> plot(mtcars$wt, mtcars$mpg, pch = 16, col = "#00000080")

2 3 4 5 10 15 20 25 30 mtcars$wt mtcars$mpg

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SLIDE 4

Data Visualization with ggplot2

base package

> plot(mtcars$wt, mtcars$mpg, pch = 16, col = "#00000080") > abline(lm(mpg ~ wt, data = mtcars), col = "red")

2 3 4 5 10 15 20 25 30 mtcars$wt mtcars$mpg

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SLIDE 5

Data Visualization with ggplot2

base package - change labels

2 3 4 5 10 15 20 25 30 mtcars$wt mtcars$mpg Weight mpg (US gallons)

> plot(mtcars$wt, mtcars$mpg, pch = 16, col = "#00000080") > abline(lm(mpg ~ wt, data = mtcars), col = "red") > mtext("Weight", 1, 3) > mtext("mpg (US gallons)", 2, 3)

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SLIDE 6

Data Visualization with ggplot2

base package - change dots

2 3 4 5 10 15 20 25 30 mtcars$wt mtcars$mpg

> plot(mtcars$wt, mtcars$mpg, pch = 16, col = "#00000080") > abline(lm(mpg ~ wt, data = mtcars), col = "red") > points(mtcars$wt, mtcars$mpg, pch = 16, 
 col = "#C3212766", cex = 2)

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SLIDE 7

Data Visualization with ggplot2

base package - restart

2 3 4 5 10 15 20 25 30 Weight mpg (US gallons)

> plot(mtcars$wt, mtcars$mpg, pch = 16, col = "#C3212766", cex = 2, xlab = "Weight", ylab = "mpg (US gallons)") > abline(lm(mpg ~ wt, data = mtcars), col = "red")

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SLIDE 8

Data Visualization with ggplot2

grid package

  • Paul Murell
  • Low-level graphic functions
  • Assemble yourself
  • ggplot2 built on top of grid
  • Two components
  • Create graphic outputs
  • Layer and position outputs with viewports
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SLIDE 9

Data Visualization with ggplot2

Graphic output

> # Rectangle > grid.rect()

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SLIDE 10

Data Visualization with ggplot2

Graphic output

> # Rectangle > grid.rect() > # Line > grid.lines()

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SLIDE 11

Data Visualization with ggplot2

Graphic output

> # Rectangle > grid.rect() > # Line > grid.lines() > # Circle > grid.circle()

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SLIDE 12

Data Visualization with ggplot2

Graphic output

> # Rectangle > grid.rect() > # Line > grid.lines() > # Circle > grid.circle() > # Grid polygon > grid.polygon()

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SLIDE 13

Data Visualization with ggplot2

Graphic output

> # Rectangle > grid.rect() > # Line > grid.lines() > # Circle > grid.circle() > # Grid polygon > grid.polygon() > # Text > grid.text("hello")

hello

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SLIDE 14

Data Visualization with ggplot2

Graphic output - adjust

> # Rectangle > grid.rect() > # Modified rectangle > grid.rect(x = 0.5, y = 0.5, width = 0.5, height = 0.5, just = "center")

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SLIDE 15

Data Visualization with ggplot2

Graphic output - gpar()

> # Rectangle > grid.rect(x = 0.5, y = 0.5, width = 0.5, height = 0.5, just = "center", gp = gpar(fill = "darkgreen"))

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SLIDE 16

Data Visualization with ggplot2

Graphic output - gpar()

> # Rectangle > grid.rect(x = 0.5, y = 0.5, width = 0.5, height = 0.5, just = "center", gp = gpar(fill = "darkgreen")) > # Line > grid.lines(x = c(0, 0.5), y = c(0.25, 1), gp = gpar(lty = 3, 
 col = "darkblue"))

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SLIDE 17

Data Visualization with ggplot2

Graphic output - gpar()

> # Rectangle > grid.rect(x = 0.5, y = 0.5, width = 0.5, height = 0.5, just = "center", gp = gpar(fill = "darkgreen")) > # Line > grid.lines(x = c(0, 0.5), y = c(0.25, 1), gp = gpar(lty = 3, 
 col = "darkblue")) > # Circle > grid.circle(x = 0.5, y = 0.5, r = 0.25, gp = gpar(fill = "darkred", col = NA))

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SLIDE 18

Data Visualization with ggplot2

Naming graphic output

> # Circle > grid.circle(x = 0.5, y = 0.5, r = 0.25, gp = gpar(fill = "darkred", col = NA), name = "myCircle")

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SLIDE 19

Data Visualization with ggplot2

Naming graphic output

> # Circle > grid.circle(x = 0.5, y = 0.5, r = 0.25, gp = gpar(fill = "darkred", col = NA), name = "myCircle") > grid.edit("myCircle", gp = gpar(fill = "pink"))

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SLIDE 20

Data Visualization with ggplot2

Naming graphic output

> # Circle > grid.circle(x = 0.5, y = 0.5, r = 0.25, gp = gpar(fill = "darkred", col = NA), name = "myCircle") > grid.edit("myCircle", gp = gpar(fill = "pink")) > grid.edit("myCircle", x = unit(0.6, "npc"), r = unit(0.6, "npc"))

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SLIDE 21

Data Visualization with ggplot2

Viewports

Windows onto which we draw graphic outputs

> grid.rect(gp = gpar(fill = "grey80")) > vp <- viewport(x = 0.5, y = 0.5, w = 0.5, h = 0.5, 
 just = "center") > pushViewport(vp)

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SLIDE 22

Data Visualization with ggplot2

Viewports

Windows onto which we draw graphic outputs

> grid.rect(gp = gpar(fill = "grey80")) > vp <- viewport(x = 0.5, y = 0.5, w = 0.5, h = 0.5, 
 just = "center") > pushViewport(vp) > grid.rect(gp = gpar(fill = "#2685A2"))

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SLIDE 23

Data Visualization with ggplot2

Viewports

Windows onto which we draw graphic outputs

> grid.rect(gp = gpar(fill = "grey80")) > vp <- viewport(x = 0.5, y = 0.5, w = 0.5, h = 0.5, 
 just = "center" ) > pushViewport(vp) > grid.rect(gp = gpar(fill = "#2685A2")) , name = "vp1"

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SLIDE 24

Data Visualization with ggplot2

plotViewport

> grid.rect(gp = gpar(fill = "grey80")) > mar <- c(5, 4, 2, 2) > vp_plot <- plotViewport(margins = mar, name = "vp2") > pushViewport(vp_plot) > grid.rect(gp = gpar(fill = "#2685A2"))

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SLIDE 25

Data Visualization with ggplot2

dataViewport

> grid.rect(gp = gpar(fill = "grey80")) > mar <- c(5, 4, 2, 2) > vp_plot <- plotViewport(margins = mar, name = "vp2") > pushViewport(vp_plot) > vp_data <- dataViewport(mtcars$wt, mtcars$mpg) > pushViewport(vp_data) > grid.rect(gp = gpar(fill = "grey70"))

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SLIDE 26

Data Visualization with ggplot2

dataViewport

> grid.rect(gp = gpar(fill = "grey80")) > mar <- c(5, 4, 2, 2) > vp_plot <- plotViewport(margins = mar, name = "vp2") > pushViewport(vp_plot) > vp_data <- dataViewport(mtcars$wt, mtcars$mpg) > pushViewport(vp_data) > grid.rect(gp = gpar(fill = "grey70")) > grid.xaxis() > grid.yaxis()

2 3 4 5 10 15 20 25 30 35

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SLIDE 27

Data Visualization with ggplot2

dataViewport

> grid.rect(gp = gpar(fill = "grey80")) > mar <- c(5, 4, 2, 2) > vp_plot <- plotViewport(margins = mar, name = "vp2") > pushViewport(vp_plot) > vp_data <- dataViewport(mtcars$wt, mtcars$mpg) > pushViewport(vp_data) > grid.rect(gp = gpar(fill = "grey70")) > grid.xaxis() > grid.yaxis() > grid.text("Weight", y = unit(-3, "lines")) > grid.text("MPG", x = unit(-3, "lines"), rot = 90)

2 3 4 5 10 15 20 25 30 35 Weight MPG

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SLIDE 28

Data Visualization with ggplot2

dataViewport

> grid.rect(gp = gpar(fill = "grey80")) > mar <- c(5, 4, 2, 2) > vp_plot <- plotViewport(margins = mar, name = "vp2") > pushViewport(vp_plot) > vp_data <- dataViewport(mtcars$wt, mtcars$mpg) > pushViewport(vp_data) > grid.rect(gp = gpar(fill = "grey70")) > grid.xaxis() > grid.yaxis() > grid.text("Weight", y = unit(-3, "lines")) > grid.text("MPG", x = unit(-3, "lines"), rot = 90) > grid.points(mtcars$wt, mtcars$mpg, pch = 16, gp = gpar(col = "#00000080"), name = "data")

2 3 4 5 10 15 20 25 30 35 Weight MPG

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SLIDE 29

Data Visualization with ggplot2

grid.edit

> grid.rect(gp = gpar(fill = "grey80")) ... > grid.points(mtcars$wt,mtcars$mpg, pch = 16, gp = gpar(col = "#00000080"), name = "datapoints") > grid.edit("datapoints", gp = gpar(col = "#C3212766", cex = 4))

2 3 4 5 10 15 20 25 30 35 Weight MPG

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SLIDE 30

DATA VISUALIZATION WITH GGPLOT2

Let’s practice!

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SLIDE 31

DATA VISUALIZATION WITH GGPLOT2

Grid graphics in ggplot2

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SLIDE 32

Data Visualization with ggplot2

Grobs

  • Graphical objects = grobs
  • ggplot2 object = collection of grobs

Graphic Output Graphics Object grid.rect() rectGrob() grid.lines() linesGrob() grid.circle() circleGrob() grid.polygon() polygonGrob() grid.text() textGrob()

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SLIDE 33

Data Visualization with ggplot2

ggplot2 example

> p <- ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, col = Species)) + geom_point(alpha = 0.3, size = 5, shape = 16) + geom_smooth(method = "lm", se = FALSE) + scale_y_continuous("Width", limits = c(2, 4.5), expand = c(0,0)) + scale_x_continuous("Length", limits = c(4, 8), expand = c(0,0)) + coord_equal() + ggtitle("Iris Sepals") + theme(rect = element_blank())

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SLIDE 34

Data Visualization with ggplot2

ggplot2 example

> p

2.0 2.5 3.0 3.5 4.0 4.5 4 5 6 7 8

Length Width Species

setosa versicolor virginica

Iris Sepals

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SLIDE 35

Data Visualization with ggplot2

Accessing grobs

> library(grid) > g <- ggplotGrob(p) > g TableGrob (6 x 6) "layout": 9 grobs z cells name grob 1 0 (1-6,1-6) background zeroGrob[plot.background..zeroGrob.23938] 2 3 (3-3,3-3) axis-l absoluteGrob[GRID.absoluteGrob.23907] 3 1 (4-4,3-3) spacer zeroGrob[NULL] 4 2 (3-3,4-4) panel gTree[GRID.gTree.23893] 5 4 (4-4,4-4) axis-b absoluteGrob[GRID.absoluteGrob.23900] 6 5 (5-5,4-4) xlab titleGrob[axis.title.x..titleGrob.23910] 7 6 (3-3,2-2) ylab titleGrob[axis.title.y..titleGrob.23913] 8 7 (3-3,5-5) guide-box gtable[guide-box] 9 8 (2-2,4-4) title titleGrob[plot.title..titleGrob.23937]

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SLIDE 36

Data Visualization with ggplot2

List of grobs

> g$grob [[1]] zeroGrob[plot.background..zeroGrob.24133] ... [[6]] titleGrob[axis.title.x..titleGrob.24105] [[7]] titleGrob[axis.title.y..titleGrob.24108] [[8]] TableGrob (3 x 3) "guide-box": 1 grobs z cells name grob 99_cf2b20daa6ef538a0def731fa7c3e7db 1 (2-2,2-2) guides gtable[layout] ...

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SLIDE 37

Data Visualization with ggplot2

Legend grob

> g$grob[[8]] TableGrob (3 x 3) "guide-box": 1 grobs z cells name grob 99_cf2b20daa6ef538a0def731fa7c3e7db 1 (2-2,2-2) guides gtable[layout]

Species

setosa versicolor virginica

> grid.draw(g$grob[[8]])

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SLIDE 38

Data Visualization with ggplot2

Structure of legend

> library(gtable) > gtable_show_layout(g$grob[[8]])

(1, 1) 0.2cm 0.2cm (1, 2) max(sum(0.15cm, 0.6096cm, 0.0762cm, 1.32833232985602cm, 0cm, 0.15cm)) (1, 3) 0.2cm 0.2cm (2, 1) sum(0.15cm, 0.277778049045139cm, 0.1524cm, 0.6096cm, 0.6096cm, 0.6096cm, 0.15cm) (2, 2) (2, 3) sum(0.15cm, 0.277778049045139cm, 0.1524cm, 0.6096cm, 0.6096cm, 0.6096cm, 0.15cm) (3, 1) 0.2cm 0.2cm (3, 2) max(sum(0.15cm, 0.6096cm, 0.0762cm, 1.32833232985602cm, 0cm, 0.15cm)) (3, 3) 0.2cm 0.2cm

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SLIDE 39

Data Visualization with ggplot2

Update legend

> my_text <- textGrob(label = "Anderson, 1936", gp = gpar(fontsize = 7, col = "gray25"))

(1, 1) 0.2cm 0.2cm (1, 2) max(sum(0.15cm, 0.6096cm, 0.0762cm, 1.32833232985602cm, 0cm, 0.15cm)) (1, 3) 0.2cm 0.2cm (2, 1) sum(0.15cm, 0.277778049045139cm, 0.1524cm, 0.6096cm, 0.6096cm, 0.6096cm, 0.15cm) (2, 2) (2, 3) sum(0.15cm, 0.277778049045139cm, 0.1524cm, 0.6096cm, 0.6096cm, 0.6096cm, 0.15cm) (3, 1) 0.2cm 0.2cm (3, 2) max(sum(0.15cm, 0.6096cm, 0.0762cm, 1.32833232985602cm, 0cm, 0.15cm)) (3, 3) 0.2cm 0.2cm

> leg2 <- gtable_add_grob(g$grob[[8]], my_text, 3, 2) > leg2 TableGrob (3 x 3) "guide-box": 2 grobs z cells name grob 99_cf2.... 1 (2-2,2-2) guides gtable[layout] 2 (3-3,2-2) guide-box text[GRID.text.24881]

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SLIDE 40

Data Visualization with ggplot2

Update legend

> my_text <- textGrob(label = "Anderson, 1936", gp = gpar(fontsize = 7, col = "gray25")) > leg2 <- gtable_add_grob(g$grob[[8]], my_text, 3, 2) > leg2 TableGrob (3 x 3) "guide-box": 2 grobs z cells name grob 99_cf2.... 1 (2-2,2-2) guides gtable[layout] 2 (3-3,2-2) guide-box text[GRID.text.24881]

Species

setosa versicolor virginica

Anderson, 1936

> grid.draw(leg2)

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SLIDE 41

Data Visualization with ggplot2

Update legend (2)

> my_text <- textGrob(label = "Anderson, 1936", gp = gpar(fontsize = 7, col = "gray25")) > <- gtable_add_grob(g$grob[[8]], my_text, 3, 2) g$grobs[[8]] > grid.draw(g)

2.0 2.5 3.0 3.5 4.0 4.5 4 5 6 7 8

Length Width Species

setosa versicolor virginica

Anderson, 1936

Iris Sepals

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SLIDE 42

DATA VISUALIZATION WITH GGPLOT2

Let’s practice!

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SLIDE 43

DATA VISUALIZATION WITH GGPLOT2

ggplot2 Objects

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SLIDE 44

Data Visualization with ggplot2

ggplot2 example

> p

2.0 2.5 3.0 3.5 4.0 4.5 4 5 6 7 8

Length Width Species

setosa versicolor virginica

Iris Sepals

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SLIDE 45

Data Visualization with ggplot2

Accessing grobs

> library(grid) > g <- ggplotGrob(p) > g TableGrob (6 x 6) "layout": 9 grobs z cells name grob 1 0 (1-6,1-6) background zeroGrob[plot.background..zeroGrob.23938] 2 3 (3-3,3-3) axis-l absoluteGrob[GRID.absoluteGrob.23907] 3 1 (4-4,3-3) spacer zeroGrob[NULL] 4 2 (3-3,4-4) panel gTree[GRID.gTree.23893] 5 4 (4-4,4-4) axis-b absoluteGrob[GRID.absoluteGrob.23900] 6 5 (5-5,4-4) xlab titleGrob[axis.title.x..titleGrob.23910] 7 6 (3-3,2-2) ylab titleGrob[axis.title.y..titleGrob.23913] 8 7 (3-3,5-5) guide-box gtable[guide-box] 9 8 (2-2,4-4) title titleGrob[plot.title..titleGrob.23937]

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SLIDE 46

Data Visualization with ggplot2

ggplot object

> e <- ggplot() > class(e) [1] "gg" "ggplot" > names(e) [1] "data" "layers" "scales" "mapping" "theme" [6] "coordinates" "facet" "plot_env" "labels"

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SLIDE 47

Data Visualization with ggplot2

p

> p <- ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, col = Species)) + geom_point(alpha = 0.3, size = 5, shape = 16) + geom_smooth(method = "lm", se = FALSE) + scale_y_continuous("Width", limits = c(2, 4.5), expand = c(0,0)) + scale_x_continuous("Length", limits = c(4, 8), expand = c(0,0)) + coord_equal() + ggtitle("Iris Sepals") + theme(rect = element_blank()) > names(p) [1] "data" "layers" "scales" "mapping" "theme" [6] "coordinates" "facet" "plot_env" "labels"

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SLIDE 48

Data Visualization with ggplot2

> p$data Sepal.Length Sepal.Width Petal.Length Petal.Width Species 1 5.1 3.5 1.4 0.2 setosa 2 4.9 3.0 1.4 0.2 setosa 3 4.7 3.2 1.3 0.2 setosa 4 4.6 3.1 1.5 0.2 setosa ... > p$layers > p$scales > p$mapping > p$theme > p$coordinates > p$facet > p$plot_env > p$labels

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SLIDE 49

Data Visualization with ggplot2

> p$data > p$layers [[1]] geom_point: na.rm = FALSE stat_identity: na.rm = FALSE position_identity [[2]] geom_smooth: na.rm = FALSE stat_smooth: na.rm = FALSE, method = lm, formula = y ~ x, se = FALSE position_identity > p$scales > p$mapping > p$theme > p$coordinates > p$facet > p$plot_env > p$labels

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SLIDE 50

Data Visualization with ggplot2

> p$data > p$layers > p$scales <ggproto object: Class ScalesList> add: function clone: function find: function get_scales: function has_scale: function input: function n: function non_position_scales: function scales: list super: <ggproto object: Class ScalesList> > p$mapping > p$theme > p$coordinates > p$facet > p$plot_env > p$labels

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SLIDE 51

Data Visualization with ggplot2

> p$data > p$layers > p$scales > p$mapping * x -> Sepal.Length * y -> Sepal.Width * colour -> Species > p$theme > p$coordinates > p$facet > p$plot_env > p$labels

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SLIDE 52

Data Visualization with ggplot2

> p$data > p$layers > p$scales > p$mapping > p$theme List of 1 $ rect: list() ..- attr(*, "class")= chr [1:2] "element_blank" "element"

  • attr(*, "class")= chr [1:2] "theme" "gg"
  • attr(*, "complete")= logi FALSE
  • attr(*, "validate")= logi FALSE

> p$coordinates > p$facet > p$plot_env > p$labels

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SLIDE 53

Data Visualization with ggplot2

> p$data > p$layers > p$scales > p$mapping > p$theme > p$coordinates <ggproto object: Class CoordFixed, CoordCartesian, Coord> aspect: function distance: function expand: TRUE is_linear: function labels: function limits: list range: function ratio: 1 render_axis_h: function ... > p$facet > p$plot_env > p$labels

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SLIDE 54

Data Visualization with ggplot2

> p$data > p$layers > p$scales > p$mapping > p$theme > p$coordinates > p$facet facet_null() > p$plot_env > p$labels

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SLIDE 55

Data Visualization with ggplot2

> p$data > p$layers > p$scales > p$mapping > p$theme > p$coordinates > p$facet > p$plot_env <environment: R_GlobalEnv> > p$labels

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SLIDE 56

Data Visualization with ggplot2

> p$data > p$layers > p$scales > p$mapping > p$theme > p$coordinates > p$facet > p$plot_env > p$labels $title [1] "Iris Sepals" $x [1] "Sepal.Length" $y [1] "Sepal.Width" $colour [1] "Species"

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SLIDE 57

Data Visualization with ggplot2

ggplot_build

> p_build <- ggplot_build(p) > names(p_build) [1] "data" "panel" "plot"

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SLIDE 58

Data Visualization with ggplot2

data

> p_build$data [[1]] colour y x PANEL group shape size fill alpha stroke 1 #F8766D 3.5 5.1 1 1 16 5 NA 0.3 0.5 2 #F8766D 3.0 4.9 1 1 16 5 NA 0.3 0.5 3 #F8766D 3.2 4.7 1 1 16 5 NA 0.3 0.5 4 #F8766D 3.1 4.6 1 1 16 5 NA 0.3 0.5 ... [[2]] colour x y PANEL group fill size linetype weight alpha 1 #F8766D 4.300000 2.864239 1 1 grey60 1 1 1 0.4 2 #F8766D 4.318987 2.879401 1 1 grey60 1 1 1 0.4 3 #F8766D 4.337975 2.894563 1 1 grey60 1 1 1 0.4 4 #F8766D 4.356962 2.909725 1 1 grey60 1 1 1 0.4 ...

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SLIDE 59

Data Visualization with ggplot2

panel

> p_build$panel $layout PANEL ROW COL SCALE_X SCALE_Y 1 1 1 1 1 1 $shrink [1] TRUE $x_scales $x_scales[[1]] <ScaleContinuousPosition> Range: 4.3 -- 7.9 Limits: 4.3 -- 8 $y_scales $y_scales[[1]] <ScaleContinuousPosition> ...

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SLIDE 60

Data Visualization with ggplot2

plot

> p_build$plot

2.0 2.5 3.0 3.5 4.0 4.5 4 5 6 7 8

Length Width Species

setosa versicolor virginica

Iris Sepals

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SLIDE 61

Data Visualization with ggplot2

gtable

> p_build <- ggplot_build(p) > gtab <- ggplot_gtable(p_build) > gtab TableGrob (6 x 6) "layout": 9 grobs z cells name grob 1 0 (1-6,1-6) background zeroGrob[plot.background..zeroGrob.25361] 2 3 (3-3,3-3) axis-l absoluteGrob[GRID.absoluteGrob.25330] 3 1 (4-4,3-3) spacer zeroGrob[NULL] 4 2 (3-3,4-4) panel gTree[GRID.gTree.25316] 5 4 (4-4,4-4) axis-b absoluteGrob[GRID.absoluteGrob.25323] 6 5 (5-5,4-4) xlab titleGrob[axis.title.x..titleGrob.25333] 7 6 (3-3,2-2) ylab titleGrob[axis.title.y..titleGrob.25336] 8 7 (3-3,5-5) guide-box gtable[guide-box] 9 8 (2-2,4-4) title titleGrob[plot.title..titleGrob.25360] > gtab2 <- ggplotGrob(p) # same thing

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SLIDE 62

Data Visualization with ggplot2

grid.draw

> library(grid) > grid.draw(gtab)

2.0 2.5 3.0 3.5 4.0 4.5 4 5 6 7 8

Length Width Species

setosa versicolor virginica

Iris Sepals

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SLIDE 63

Data Visualization with ggplot2

gtab

> names(gtab) [1] "grobs" "layout" "widths" "heights" [5] "respect" "rownames" "colnames" "name" [9] "gp" "vp" "children" "childrenOrder"

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SLIDE 64

Data Visualization with ggplot2

layout

> gtable_show_layout(gtab)

(1, 1) 5.5pt 5.5pt (1, 2) 1grobwidth (1, 3) sum(1grobwidth, 2.75pt) (1, 4) 1null (1, 5) sum(0.2cm, max(sum(0.15cm, 0.6096cm, 0.0762cm, 1.32833232985602cm, 0cm, 0.15cm)), 0.2cm)+0.2cm (1, 6) 5.5pt 5.5pt (2, 1) 1grobheight (2, 2) (2, 3) (2, 4) (2, 5) (2, 6) 1grobheight (3, 1) 0.675675675675676null (3, 2) (3, 3) (3, 4) (3, 5) (3, 6) 0.675675675675676n (4, 1) sum(2.75pt, 1grobheight) (4, 2) (4, 3) (4, 4) (4, 5) (4, 6) sum(2.75pt, 1grobheight) (5, 1) 1grobheight (5, 2) (5, 3) (5, 4) (5, 5) (5, 6) 1grobheight (6, 1) 5.5pt 5.5pt (6, 2) 1grobwidth (6, 3) sum(1grobwidth, 2.75pt) (6, 4) 1null (6, 5) sum(0.2cm, max(sum(0.15cm, 0.6096cm, 0.0762cm, 1.32833232985602cm, 0cm, 0.15cm)), 0.2cm)+0.2cm (6, 6) 5.5pt 5.5pt

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SLIDE 65

Data Visualization with ggplot2

layout (2)

> gtab$layout t l b r z clip name 9 1 1 6 6 0 on background 1 3 3 3 3 3 off axis-l 2 4 3 4 3 1 off spacer 3 3 4 3 4 2 on panel 4 4 4 4 4 4 off axis-b 5 5 4 5 4 5 off xlab 6 3 2 3 2 6 off ylab 7 3 5 3 5 7 off guide-box 8 2 4 2 4 8 off title

(1, 1) 5.5pt 5.5pt (1, 2) 1grobwidth (1, 3) sum(1grobwidth, 2.75pt) (1, 4) 1null (1, 5) sum(0.2cm, max(sum(0.15cm, 0.6096cm, 0.0762cm, 1.32833232985602cm, 0cm, 0.15cm)), 0.2cm)+0.2cm (1, 6) 5.5pt 5.5pt (2, 1) 1grobheight (2, 2) (2, 3) (2, 4) (2, 5) (2, 6) 1grobheight (3, 1) 0.675675675675676null (3, 2) (3, 3) (3, 4) (3, 5) (3, 6) 0.675675675675676n (4, 1) sum(2.75pt, 1grobheight) (4, 2) (4, 3) (4, 4) (4, 5) (4, 6) sum(2.75pt, 1grobheight) (5, 1) 1grobheight (5, 2) (5, 3) (5, 4) (5, 5) (5, 6) 1grobheight (6, 1) 5.5pt 5.5pt (6, 2) 1grobwidth (6, 3) sum(1grobwidth, 2.75pt) (6, 4) 1null (6, 5) sum(0.2cm, max(sum(0.15cm, 0.6096cm, 0.0762cm, 1.32833232985602cm, 0cm, 0.15cm)), 0.2cm)+0.2cm (6, 6) 5.5pt 5.5pt

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SLIDE 66

Data Visualization with ggplot2

Update clipping

> gtab$layout$clip [1] "on" "off" "off" "on" "off" "off" "off" "off" "off" > gtab$layout$clip[gtab$layout$name == "panel"] <- "off"

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SLIDE 67

Data Visualization with ggplot2

Redraw

> library(grid) > grid.draw(gtab)

2.0 2.5 3.0 3.5 4.0 4.5 5 6 7 8

Length Width Species

setosa versicolor virginica

Iris Sepals

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SLIDE 68

DATA VISUALIZATION WITH GGPLOT2

Let’s practice!

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SLIDE 69

DATA VISUALIZATION WITH GGPLOT2

gridExtra

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SLIDE 70

Data Visualization with ggplot2

gridExtra

  • Manage multiple ploing objects
  • Reasons
  • Avoid giant faceed plot
  • Defer ploing
  • Arrange multiple plots in layout
  • Make a multiple page pdf of plots
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SLIDE 71

Data Visualization with ggplot2

> library(plyr) > my_plots <- dlply(mtcars, .(cyl), function(df) { ggplot(df, aes(mpg, wt)) + geom_point() + xlim(range(mtcars$mpg)) + ylim(range(mtcars$wt)) + ggtitle(paste(df$cyl[1], "cylinders"))})

Build multiple plots

> length(my_plots) [1] 3 > names(my_plots) [1] "4" "6" "8"

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SLIDE 72

Data Visualization with ggplot2

Plot by position

> my_plots[[2]]

  • 2

3 4 5 10 15 20 25 30 35

mpg wt

6 cylinders

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SLIDE 73

Data Visualization with ggplot2

Plot by name

> my_plots[["6"]]

  • 2

3 4 5 10 15 20 25 30 35

mpg wt

6 cylinders

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SLIDE 74

Data Visualization with ggplot2

Combine plots (1)

> library(gridExtra) > grid.arrange(my_plots[[2]], my_plots[[1]], ncol = 2)

  • 2

3 4 5 10 15 20 25 30 35

mpg wt

6 cylinders

  • 2

3 4 5 10 15 20 25 30 35

mpg wt

4 cylinders

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SLIDE 75

Data Visualization with ggplot2

Combine plots (2)

> do.call(grid.arrange, my_plots)

  • 2

3 4 5 10 15 20 25 30 35

mpg wt

4 cylinders

  • 2

3 4 5 10 15 20 25 30 35

mpg wt

6 cylinders

  • 2

3 4 5 10 15 20 25 30 35

mpg wt

8 cylinders

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SLIDE 76

Data Visualization with ggplot2

Combine plots (3)

> grid.arrange(p, my_plots[[1]])

2.0 2.5 3.0 3.5 4.0 4.5 4 5 6 7 8

Length Width Species

setosa versicolor virginica

Iris Sepals

  • 2

3 4 5 10 15 20 25 30 35

mpg wt

4 cylinders

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SLIDE 77

Data Visualization with ggplot2

Why grid.arrange()?

  • You are not able to make manual adjustments
  • Creating many of the same composite plots
  • Slight variations (different dataset or variables)
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SLIDE 78

DATA VISUALIZATION WITH GGPLOT2

Let’s practice!