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ggplot2 Version of Figures in “Lattice: Multivariate Data Visualization with R” (Part 8)

August 3, 2009

This is the 8th post in a series attempting to recreate the figures in Lattice: Multivariate Data Visualization with R (R code available here) with ggplot2.

Previous parts in this series: Part 1, Part 2, Part 3, Part 4, Part 5, Part 6, Part 7.


Chapter 8 – Plot Coordinates and Axis Annotation

Topics covered:

  • Packets
  • The prepanel function, axis limits, and aspect ratio
  • Axis annotation
  • The scales argument

Figure 8.1

> library(lattice)
> library(ggplot2)

lattice

> pl <- stripplot(depth ~ factor(mag), data = quakes, jitter.data = TRUE,
+     scales = list(y = "free", rot = 0), prepanel = function(x,
+         y, ...) list(ylim = rev(range(y))), xlab = "Magnitude (Richter scale)")
> 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) + xlab("Magnitude (Richter)") +
+     ylab("Depth (km)") + theme_bw() + scale_y_reverse()
> print(pg)

chapter08-08_01_l_small.png chapter08-08_01_r_small.png

Note Compare to Figure 3.16. Y-Axes reversed.

Figure 8.2

> data(biocAccess, package = "latticeExtra")

lattice

> pl <- xyplot(counts/1000 ~ time | equal.count(as.numeric(time),
+     9, overlap = 0.1), biocAccess, type = "l", aspect = "xy",
+     strip = FALSE, ylab = "Numer of accesses (thousands)",
+     xlab = "", scales = list(x = list(relation = "sliced",
+         axs = "i"), y = list(alternating = FALSE)))
> print(pl)

ggplot2

> fn <- function(data = quakes$mag, number = 4, ...) {
+     intrv <<- as.data.frame(co.intervals(data, number,
+         ...))
+     mag <- sort(unique(data))
+     intervals <- ldply(mag, function(x) {
+         t(as.numeric(x < intrv$V2 & x > intrv$V1))
+     })
+     tmp <- melt(cbind(mag, intervals), id.var = 1)
+     tmp[tmp$value > 0, 1:2]
+ }
> biocAccess <- merge(biocAccess[, c("time", "counts")],
+     fn(data = as.numeric(biocAccess$time), number = 9,
+         overlap = 0.1), by.x = "time", by.y = "mag")
Note Using custom function fn(), first defined in Figure 5.5.
> pg <- ggplot(biocAccess, aes(time, counts/1000)) + geom_line() +
+     facet_wrap(~variable, scales = "free_x", ncol = 1) +
+     ylab("Numer of accesses (thousands)") + xlab("") +
+     opts(strip.background = theme_blank(), strip.text.x = theme_blank()) +
+     opts(panel.margin = unit(-0.25, "lines"))
> print(pg)

chapter08-08_02_l_small.png chapter08-08_02_r_small.png

Figure 8.3

> data(Earthquake, package = "MEMSS")

lattice

> pl <- xyplot(accel ~ distance, data = Earthquake, prepanel = prepanel.loess,
+     aspect = "xy", type = c("p", "g", "smooth"), scales = list(log = 2),
+     xlab = "Distance From Epicenter (km)", ylab = "Maximum Horizontal Acceleration (g)")
> print(pl)

ggplot2

> pg <- ggplot(Earthquake, aes(distance, accel)) + geom_point() +
+     geom_smooth(method = "loess", se = FALSE) + scale_x_log2() +
+     scale_y_log2() + xlab("Distance From Epicenter (km)") +
+     ylab("Maximum Horizontal Acceleration (g)")
> print(pg)
Note ggplot2 doesn’t have the equivalent of aspect="xy" in lattice, which “tries to compute the aspect based on the 45 degree banking rule”.

chapter08-08_03_l_small.png chapter08-08_03_r_small.png

Figure 8.4

lattice

> yscale.components.log2 <- function(...) {
+     ans <- yscale.components.default(...)
+     ans$right <- ans$left
+     ans$left$labels$labels <- parse(text = ans$left$labels$labels)
+     ans$right$labels$labels <- MASS::fractions(2^(ans$right$labels$at))
+     ans
+ }
> logTicks <- function(lim, loc = c(1, 5)) {
+     ii <- floor(log10(range(lim))) + c(-1, 2)
+     main <- 10^(ii[1]:ii[2])
+     r <- as.numeric(outer(loc, main, "*"))
+     r[lim[1] <= r & r <= lim[2]]
+ }
> xscale.components.log2 <- function(lim, ...) {
+     ans <- xscale.components.default(lim = lim, ...)
+     tick.at <- logTicks(2^lim, loc = c(1, 3))
+     ans$bottom$ticks$at <- log(tick.at, 2)
+     ans$bottom$labels$at <- log(tick.at, 2)
+     ans$bottom$labels$labels <- as.character(tick.at)
+     ans
+ }
> pl <- xyplot(accel ~ distance | cut(Richter, c(4.9, 5.5,
+     6.5, 7.8)), data = Earthquake, type = c("p", "g"),
+     scales = list(log = 2, y = list(alternating = 3)),
+     xlab = "Distance From Epicenter (km)", ylab = "Maximum Horizontal Acceleration (g)",
+     xscale.components = xscale.components.log2, yscale.components = yscale.components.log2)
> print(pl)

ggplot2

> Earthquake$magnitude <- cut(Earthquake$Richter, c(4.9,
+     5.5, 6.5, 7.8))
> ticks <- logTicks(range(Earthquake$distance), loc = c(1,
+     3))
> pg <- ggplot(Earthquake, aes(distance, accel)) + geom_point() +
+     facet_grid(~magnitude) + scale_y_log2() + scale_x_log2(breaks = ticks,
+     labels = ticks) + xlab("Distance From Epicenter (km)") +
+     ylab("Maximum Horizontal Acceleration (g)")
> print(pg)
Note ggplot2 does not support the addition of a secondary axes.

chapter08-08_04_l_small.png chapter08-08_04_r_small.png

Figure 8.5

> xscale.components.log10 <- function(lim, ...) {
+     ans <- xscale.components.default(lim = lim, ...)
+     tick.at <- logTicks(10^lim, loc = 1:9)
+     tick.at.major <- logTicks(10^lim, loc = 1)
+     major <- tick.at %in% tick.at.major
+     ans$bottom$ticks$at <- log(tick.at, 10)
+     ans$bottom$ticks$tck <- ifelse(major, 1.5, 0.75)
+     ans$bottom$labels$at <- log(tick.at, 10)
+     ans$bottom$labels$labels <- as.character(tick.at)
+     ans$bottom$labels$labels[!major] <- ""
+     ans$bottom$labels$check.overlap <- FALSE
+     ans
+ }

lattice

> pl <- xyplot(accel ~ distance, data = Earthquake, prepanel = prepanel.loess,
+     aspect = "xy", type = c("p", "g"), scales = list(log = 10),
+     xlab = "Distance From Epicenter (km)", ylab = "Maximum Horizontal Acceleration (g)",
+     xscale.components = xscale.components.log10)
> print(pl)

ggplot2

> ticks <- logTicks(range(Earthquake$distance), loc = c(1,
+     10))
> pg <- ggplot(Earthquake, aes(distance, accel)) + geom_point() +
+     scale_y_log10() + scale_x_log10(breaks = ticks, labels = ticks) +
+     xlab("Distance From Epicenter (km)") + ylab("Maximum Horizontal Acceleration (g)")
> print(pg)
Note ggplot2 doesn’t have the equivalent of aspect="xy" in lattice, which “tries to compute the aspect based on the 45 degree banking rule”.

chapter08-08_05_l_small.png chapter08-08_05_r_small.png

Figure 8.6

lattice

> axis.CF <- function(side, ...) {
+     if (side == "right") {
+         F2C <- function(f) 5 * (f - 32)/9
+         C2F <- function(c) 32 + 9 * c/5
+         ylim <- current.panel.limits()$ylim
+         prettyF <- pretty(ylim)
+         prettyC <- pretty(F2C(ylim))
+         panel.axis(side = side, outside = TRUE, at = prettyF,
+             tck = 5, line.col = "grey65", text.col = "grey35")
+         panel.axis(side = side, outside = TRUE, at = C2F(prettyC),
+             labels = as.character(prettyC), tck = 1,
+             line.col = "black", text.col = "black")
+     }
+     else axis.default(side = side, ...)
+ }
> pl <- xyplot(nhtemp ~ time(nhtemp), aspect = "xy", type = "o",
+     scales = list(y = list(alternating = 2, tck = c(1,
+         5))), axis = axis.CF, xlab = "Year", ylab = "Temperature",
+     main = "Yearly temperature in New Haven, CT", key = list(text = list(c("(Celcius)",
+         "(Fahrenheit)"), col = c("black", "grey35")),
+         columns = 2))
> print(pl)

ggplot2

> temp <- data.frame(nhtemp)
> temp$year <- seq(1912, 1971)
> pg <- ggplot(temp, aes(year, nhtemp)) + geom_line() +
+     xlab("Year") + ylab("Temperature") + opts(title = "Yearly temperature in New Haven, CT")
> print(pg)
Note ggplot2 doesn’t support the addition of secondary axes.

chapter08-08_06_l_small.png chapter08-08_06_r_small.png

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7 Comments leave one →
  1. Steve permalink
    August 3, 2009 5:19 pm

    Hi,

    I’ve been enjoying your lattics vs. ggplot2 posts. After having done these comparisons for a while now, I’m curious if you have any suggestions as to which graphics system someone should pickup, or what you like better in one vs. the other.

    Perhaps it might make for a good blog post ;-)

    Thanks,
    -steve

    • learnr permalink*
      August 3, 2009 9:38 pm

      Steve, I plan to make a post on this very subject once I have completed all the chapters.

      • Andreas permalink
        August 4, 2009 6:43 pm

        @learnr – thank you for your blog. Looking forward to read your “verdict” on ggplot2 and lattice. When that time comes, I would also be interested in your experience pure performance wise. I feel that ggplot2 is quite a bit slower than base graphics. I have no experience with lattice – but if lattice is as fast as base then that might convince me.

        Thanks again.

      • human mathematics permalink
        August 22, 2011 5:55 am

        Did you do that post yet? I prefer ggplot 2 — but I never really learned lattice. I like Hadley’s approach and I believe Grammar of Graphics has more depth / inherent power.

      • learnr permalink*
        September 12, 2011 1:04 pm

        Yes, I wrote a summary post on the lattice-ggplot2.

  2. Ben Bolker permalink
    August 5, 2009 8:54 am

    @Andreas —

    I wrote to Hadley Wickham about this earlier this summer,
    and his response was:

    “So far I have been completely focused on functionality,
    and not at all on speed. I would really like to spend some time profiling and optimised ggplot2 (I suspect an order of magnitude speed increase would be possible), but unfortunately my summer is filling up rapidly and I’m feeling some pressure to write papers rather than (more) code.”

    My impression is that lattice is a little bit slower than
    base graphics, but much faster than ggplot2 (which is
    still quite earlier in its development cycle).

    Don’t forget to factor in user time vs. computer time …

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  1. ggplot2 Version of Figures in “Lattice: Multivariate Data Visualization with R” (Final Part) « Learning R

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