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ggplot2 Version of Figures in “Lattice: Multivariate Data Visualization with R” (Part 3)

July 2, 2009

This is the third post in a series attempting to recreate the figures in Lattice: Multivariate Data Visualization with R (R code) with ggplot2.


Chapter 3 – Visualizing Univariate Distributions

Topics covered:

  • Kernel Density Plot, Histogram
  • Theoretical Q-Q plot, Empirical CDF plot
  • Two-sample Q-Q plot
  • Comparative Box and Whisker plots, Violin plots
  • Comparative Strip charts
  • Discrete distributions


Figure 3.1

> library(lattice)
> library(ggplot2)
> data(Oats, package = "MEMSS")

lattice

> pl <- densityplot(~eruptions, data = faithful)
> print(pl)

ggplot2

> p <- ggplot(faithful, aes(eruptions))
> pg <- p + stat_density(geom = "path", position = "identity") +
+     geom_point(aes(y = 0.05), position = position_jitter(height = 0.005),
+         alpha = 0.25)
> print(pg)

Note

y = 0.05 specifies the position of jitter on y-axis.

chapter03-03_01_l_small.png chapter03-03_01_r_small.png

Figure 3.2

lattice

> pl <- densityplot(~eruptions, data = faithful, kernel = "rect",
+     bw = 0.2, plot.points = "rug", n = 200)
> print(pl)

ggplot2

> pg <- p + stat_density(geom = "path", kernel = "rect",
+     position = "identity", bw = 0.2) + geom_rug()
> print(pg)

chapter03-03_02_l_small.png chapter03-03_02_r_small.png

Figure 3.3

> library("latticeExtra")
> data(gvhd10)

lattice

> pl <- densityplot(~log(FSC.H) | Days, data = gvhd10,
+     plot.points = FALSE, ref = TRUE, layout = c(2, 4))
> print(pl)

ggplot2

> p <- ggplot(gvhd10, aes(log(FSC.H)))
> pg <- p + stat_density(geom = "path", position = "identity") +
+     facet_wrap(~Days, ncol = 2, as.table = FALSE)
> print(pg)

Note

as.table = FALSE changes the default orders of the facets.

chapter03-03_03_l_small.png chapter03-03_03_r_small.png

Figure 3.4

lattice

> pl <- histogram(~log2(FSC.H) | Days, gvhd10, xlab = "log Forward Scatter",
+     type = "density", nint = 50, layout = c(2, 4))
> print(pl)

ggplot2

> pg <- p + geom_histogram(aes(y = ..density..), binwidth = diff(range(log2(gvhd10$FSC.H)))/50) +
+     facet_wrap(~Days, ncol = 2, as.table = FALSE) + xlab("log Forward Scatter")
> print(pg)

Note

ggplot2 uses binwidth by default, therefore the number of bins needs to be presented in terms of binwidth.

chapter03-03_04_l_small.png chapter03-03_04_r_small.png

Figure 3.5

> data(Chem97, package = "mlmRev")

lattice

> pl <- qqmath(~gcsescore | factor(score), data = Chem97,
+     f.value = ppoints(100))
> print(pl)

ggplot2

> p <- ggplot(Chem97)
> pg <- p + geom_point(aes(sample = gcsescore), stat = "qq",
+     quantiles = ppoints(100)) + facet_wrap(~score)
> print(pg)

chapter03-03_05_l_small.png chapter03-03_05_r_small.png

Figure 3.6

lattice

> pl <- qqmath(~gcsescore | gender, Chem97, groups = score,
+     aspect = "xy", f.value = ppoints(100), auto.key = list(space = "right"),
+     xlab = "Standard Normal Quantiles", ylab = "Average GCSE Score")
> print(pl)

ggplot2

> pg <- p + geom_point(aes(sample = gcsescore, colour = factor(score)),
+     stat = "qq", quantiles = ppoints(100)) + facet_grid(~gender) +
+     opts(aspect.ratio = 1) + scale_x_continuous("Standard Normal Quantiles") +
+     scale_y_continuous("Average GCSE Score")
> print(pg)

chapter03-03_06_l_small.png chapter03-03_06_r_small.png

Figure 3.7

lattice

> Chem97.mod <- transform(Chem97, gcsescore.trans = gcsescore^2.34)
> pl <- qqmath(~gcsescore.trans | gender, Chem97.mod, groups = score,
+     f.value = ppoints(100), aspect = "xy", auto.key = list(space = "right",
+         title = "score"), xlab = "Standard Normal Quantiles",
+     ylab = "Transformed GCSE Score")
> print(pl)

ggplot2

> pg <- p + geom_point(aes(sample = gcsescore^2.34, colour = factor(score)),
+     stat = "qq", quantiles = ppoints(100)) + facet_grid(~gender) +
+     opts(aspect.ratio = 1) + scale_x_continuous("Standard Normal Quantiles") +
+     scale_y_continuous("Transformed GCSE Score")
> print(pg)

chapter03-03_07_l_small.png chapter03-03_07_r_small.png

Figure 3.8

> library("latticeExtra")

lattice

> pl <- ecdfplot(~gcsescore | factor(score), data = Chem97,
+     groups = gender, auto.key = list(columns = 2), subset = gcsescore >
+         0, xlab = "Average GCSE Score")
> print(pl)

ggplot2

> Chem97.ecdf <- ddply(Chem97, .(score, gender), transform,
+     ecdf = ecdf(gcsescore)(gcsescore))
> p <- ggplot(Chem97.ecdf, aes(gcsescore, ecdf, colour = gender))
> pg <- p + geom_step(subset = .(gcsescore > 0)) + facet_wrap(~score,
+     as.table = F) + xlab("Average GCSE Score") + ylab("Empirical CDF")
> print(pg)

chapter03-03_08_l_small.png chapter03-03_08_r_small.png

Figure 3.9

lattice

> pl <- qqmath(~gcsescore | factor(score), data = Chem97,
+     groups = gender, auto.key = list(points = FALSE,
+         lines = TRUE, columns = 2), subset = gcsescore >
+         0, type = "l", distribution = qunif, prepanel = prepanel.qqmathline,
+     aspect = "xy", xlab = "Standard Normal Quantiles",
+     ylab = "Average GCSE Score")
> print(pl)

ggplot2

> p <- ggplot(Chem97, aes(sample = gcsescore, colour = gender))
> pg <- p + geom_path(subset = .(gcsescore > 0), stat = "qq",
+     distribution = qunif) + facet_grid(~score) + scale_x_continuous("Standard Normal Quantiles") +
+     scale_y_continuous("Average GCSE Score")
> print(pg)

chapter03-03_09_l_small.png chapter03-03_09_r_small.png

Figure 3.10

lattice

> pl <- qq(gender ~ gcsescore | factor(score), Chem97,
+     f.value = ppoints(100), aspect = 1)
> print(pl)

ggplot2

> q <- function(x, probs = ppoints(100)) {
+     data.frame(q = probs, value = quantile(x, probs))
+ }
> Chem97.q <- ddply(Chem97, c("gender", "score"), function(df) q(df$gcsescore))
> Chem97.df <- recast(Chem97.q, score + q ~ gender, id.var = 1:3)
> pg <- ggplot(Chem97.df) + geom_point(aes(M, F)) + geom_abline() +
+     facet_wrap(~score) + coord_equal()
> print(pg)

chapter03-03_10_l_small.png chapter03-03_10_r_small.png

Figure 3.11

lattice

> pl <- bwplot(factor(score) ~ gcsescore | gender, data = Chem97,
+     xlab = "Average GCSE Score")
> print(pl)

ggplot2

> pg <- ggplot(Chem97, aes(factor(score), gcsescore)) +
+     geom_boxplot() + coord_flip() + ylab("Average GCSE score") +
+     facet_wrap(~gender)
> print(pg)

chapter03-03_11_l_small.png chapter03-03_11_r_small.png

Figure 3.12

lattice

> pl <- bwplot(gcsescore^2.34 ~ gender | factor(score),
+     Chem97, varwidth = TRUE, layout = c(6, 1), ylab = "Transformed GCSE score")
> print(pl)

ggplot2

> p <- ggplot(Chem97, aes(factor(gender), gcsescore^2.34))
> pg <- p + geom_boxplot() + facet_grid(~score) + ylab("Transformed GCSE score")
> print(pg)

chapter03-03_12_l_small.png chapter03-03_12_r_small.png

Figure 3.13

lattice

> pl <- bwplot(Days ~ log(FSC.H), data = gvhd10, xlab = "log(Forward Scatter)",
+     ylab = "Days Past Transplant")
> print(pl)

ggplot2

> p <- ggplot(gvhd10, aes(factor(Days), log(FSC.H)))
> pg <- p + geom_boxplot() + coord_flip() + labs(y = "log(Forward Scatter)",
+     x = "Days Past Transplant")
> print(pg)

chapter03-03_13_l_small.png chapter03-03_13_r_small.png

Figure 3.14

lattice

> pl <- bwplot(Days ~ log(FSC.H), gvhd10, panel = panel.violin,
+     box.ratio = 3, xlab = "log(Forward Scatter)", ylab = "Days Past Transplant")
> print(pl)

ggplot2

> p <- ggplot(gvhd10, aes(log(FSC.H), Days))
> pg <- p + geom_ribbon(aes(ymax = ..density.., ymin = -..density..),
+     stat = "density") + facet_grid(Days ~ ., as.table = F,
+     scales = "free_y") + labs(x = "log(Forward Scatter)",
+     y = "Days Past Transplant")
> print(pg)

chapter03-03_14_l_small.png chapter03-03_14_r_small.png

Figure 3.15

lattice

> pl <- stripplot(factor(mag) ~ depth, quakes)
> print(pl)

ggplot2

> pg <- ggplot(quakes) + geom_point(aes(depth, mag), shape = 1)
> print(pg)

chapter03-03_15_l_small.png chapter03-03_15_r_small.png

Figure 3.16

lattice

> pl <- stripplot(depth ~ factor(mag), quakes, jitter.data = TRUE,
+     alpha = 0.6, xlab = "Magnitude (Richter)", ylab = "Depth (km)")
> print(pl)

ggplot2

> p <- ggplot(quakes, aes(factor(mag), depth))
> pg <- p + geom_point(position = position_jitter(width = 0.15),
+     alpha = 0.6, shape = 1) + theme_bw() + xlab("Magnitude (Richter)") +
+     ylab("Depth (km)")
> print(pg)

chapter03-03_16_l_small.png chapter03-03_16_r_small.png

Figure 3.17

lattice

> pl <- stripplot(sqrt(abs(residuals(lm(yield ~ variety +
+     year + site)))) ~ site, data = barley, groups = year,
+     jitter.data = TRUE, auto.key = list(points = TRUE,
+         lines = TRUE, columns = 2), type = c("p", "a"),
+     fun = median, ylab = expression(abs("Residual Barley Yield")^{
+         1/2
+     }))
> print(pl)

ggplot2

> p <- ggplot(barley, aes(site, sqrt(abs(residuals(lm(yield ~
+     variety + year + site)))), colour = year, group = year))
> pg <- p + geom_jitter(position = position_jitter(width = 0.2)) +
+     geom_line(stat = "summary", fun.y = "mean") + labs(x = "",
+     y = expression(abs("Residual Barley Yield")^{
+         1/2
+     }))
> print(pg)

chapter03-03_17_l_small.png chapter03-03_17_r_small.png

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17 Comments leave one →
  1. July 2, 2009 4:57 pm

    Great again :) You can simplify the code to produce the ecdf dataset a little:

    Chem97.ecdf <- ddply(Chem97, .(score, gender), transform,
    ecdf = ecdf(gcsescore)(gcsescore)
    )

    • learnr permalink*
      August 3, 2009 9:49 am

      Thanks. I have updated the post.

  2. July 2, 2009 8:45 pm

    I’m really enjoying your tutorial series. You’ve sunk a lot of time into these!

    Drop me an email when you get a chance. A group of R folks have been organizing some community online activities and I would like your input on a few things.

    -JD

  3. Ben Bolker permalink
    July 6, 2009 6:55 am

    truly tiny, but: any way to get rid of the extra labels on the RHS in figure 3.14?

    very nice (and impressive to define the violin plot on the fly like that!)

    • learnr permalink*
      July 7, 2009 2:33 am

      I would rather get rid of the the labels on the left.
      However, to get rid of the labels on the right the following code should do the trick (hides them almost completely):
      pg + opts(strip.background = theme_rect(fill = NA, colour = NA), strip.text.y = theme_text(size = 0))

  4. asmus permalink
    July 29, 2009 1:21 pm

    Hi,

    this is just what I needed to find. Thanks a lot for the very fine examples!

  5. Dennis Murphy permalink
    December 8, 2009 3:47 pm

    Hi – very nice job! One question: shouldn’t the second piece of ggplot2 code for Figure 3.12 be

    pg <- p + geom_boxplot() + facet_grid(~score) + ylab("Transformed GCSE score")

    ?

    • learnr permalink*
      December 8, 2009 5:35 pm

      Thank you for spotting the typo. It is fixed now.

  6. January 1, 2010 4:39 am

    Your blog is very nice! Good luck to you.

  7. November 2, 2010 10:38 am

    WOW. This is a _great_ comparison.

    But just like excelcharts.com’s fools in his office that like a multi-coloured bubble chart better than an unfilled grey one … I can’t tell which I like better!

    I’m not even sure whether the ggplot axes are Tufte-compliant. I mean, you’re erasing, but you’re also using more ink. Does “erasing the paper” count as reducing ink? Or adding colour?

    So I guess the main issue is, I can’t tell if the grid lines are distracting or add clarity.

  8. Evgenia Martynova permalink
    December 29, 2010 5:52 pm

    Hi,

    First of all thank you for the tutorial. But when I am trying the above examples, I’ve got the following annoying error:
    gender))

    > p p
    Ошибка: No layers in plot
    Could you please help what is wrong? R version (2.12.0) for Windows

    • Evgenia Martynova permalink
      December 29, 2010 6:34 pm

      Just upgraded R to 2.12.1 – everything is working now. Thanks a again!!!

Trackbacks

  1. ggplot2 Version of Figures in “Lattice: Multivariate Data Visualization with R” (Part 5) « Learning R
  2. ggplot2 Version of Figures in “Lattice: Multivariate Data Visualization with R” (Part 6) « Learning R
  3. ggplot2 Version of Figures in “Lattice: Multivariate Data Visualization with R” (Part 10) « Learning R
  4. ggplot2 Version of Figures in “Lattice: Multivariate Data Visualization with R” (Part 13) « Learning R
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