Updates for R CMD check

DESCRIPTION

@@ -34,7 +34,8 @@ Suggests:
     hexbin,
     gpclib,
     maptools,
-    multcomp
+    multcomp,
+    nlme
 Extends:
     sp
 License: GPL-2

R/aes-group-order.r

@@ -30,7 +30,11 @@
 #' 
 #' # Using linetypes
 #' library(reshape2) # for melt
-#' ec_scaled <- data.frame(date = economics$date, rescaler(economics[, -(1:2)], "range")) 
+#' library(plyr) # for colwise
+#' rescale01 <- function(x) (x - min(x)) / diff(range(x))
+#' ec_scaled <- data.frame(
+#'   date = economics$date, 
+#'   colwise(rescale01)(economics[, -(1:2)])) 
 #' ecm <- melt(ec_scaled, id = "date")
 #' f <- ggplot(ecm, aes(date, value))
 #' f + geom_line(aes(linetype = variable))
@@ -57,10 +61,10 @@
 #' h <- h + geom_line(aes(group = Subject)) 
 #' # Using the group aesthetic with both geom_line() and geom_smooth()
 #' # groups the data the same way for both layers
-#' h + geom_smooth(aes(group = Subject), method = "lm", se = F)
+#' h + geom_smooth(aes(group = Subject), method = "lm", se = FALSE)
 #' # Changing the group aesthetic for the smoother layer 
 #' # fits a single line of best fit across all boys
-#' h + geom_smooth(aes(group = 1), size = 2, method = "lm", se = F)
+#' h + geom_smooth(aes(group = 1), size = 2, method = "lm", se = FALSE)
 #' 
 #' # Overriding the default grouping
 #' # The plot has a discrete scale but you want to draw lines that connect across

R/aes-position.r

@@ -11,13 +11,14 @@
 #' # for each type of cut 
 #' dmod <- lm(price ~ cut, data = diamonds) 
 #' cuts <- data.frame(cut = unique(diamonds$cut), predict(dmod, data.frame(cut = 
-#' unique(diamonds$cut)), se = T)[c("fit", "se.fit")])
+#' unique(diamonds$cut)), se = TRUE)[c("fit", "se.fit")])
 #' se <- ggplot(cuts, aes(x = cut, y = fit, ymin = fit - se.fit, 
 #' ymax = fit + se.fit, colour = cut)) 
 #' se + geom_pointrange()
 #'
 #' # Boxplot with precomputed statistics
 #' # generate sample data
+#' library(plyr)
 #' abc <- adply(matrix(rnorm(100), ncol = 5), 2, quantile, c(0, .25, .5, .75, 1))
 #' b <- ggplot(abc, aes(x = X1, ymin = "0%", lower = "25%", middle = "50%", upper = "75%", ymax = "100%")) 
 #' b + geom_boxplot(stat = "identity")

R/annotation-custom.r

@@ -28,13 +28,15 @@ NULL
 #' require(gridExtra)
 #' base + annotation_custom(grob = tableGrob(head(iris[ ,1:3])),
 #'         xmin = 3, xmax = 6, ymin = 2, ymax = 8)
-#' }
-#' # Inset plot
-#' g <- ggplotGrob(qplot(1, 1) + opts(plot.background = theme_rect(col = "black")))
-#' base + annotation_custom(grob = g, xmin = 1, xmax = 10, ymin = 8, ymax = 10)
 #' # full panel
 #' base + annotation_custom(grob = roundrectGrob(),
 #'           xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf)
+#' }
+#' # Inset plot
+#' g <- ggplotGrob(qplot(1, 1) + 
+#'   opts(plot.background = theme_rect(col = "black")))
+#' base + 
+#'   annotation_custom(grob = g, xmin = 1, xmax = 10, ymin = 8, ymax = 10)
 annotation_custom <- function (grob, xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf) { 
   GeomCustomAnn$new(geom_params = list(grob = grob, xmin = xmin, 
     xmax = xmax, ymin = ymin, ymax = ymax), stat = "identity", 

R/geom-blank.r

@@ -17,7 +17,7 @@
 #' # Suppose you then wanted to remove the geom_point layer
 #' # If you just remove geom_point, you will get an error
 #' b <- ggplot(mtcars, aes(x = wt, y = mpg), . ~ cyl)
-#' b + geom_abline(aes(intercept = a, slope = b), data = df)
+#' \dontrun{b + geom_abline(aes(intercept = a, slope = b), data = df)}
 #' # Switching to geom_blank() gets the desired plot
 #' c <- ggplot(mtcars, aes(x = wt, y = mpg), . ~ cyl) + geom_blank()
 #' c + geom_abline(aes(intercept = a, slope = b), data = df)

R/translate-qplot-base.r

@@ -13,7 +13,9 @@
 #' 
 #' # High-level plotting commands
 #' 
-#' plot(x, y); dotchart(x, y); stripchart(x, y)
+#' x <- runif(10)
+#' y <- 1:10
+#' plot(x, y); dotchart(x, y)
 #' qplot(x, y)
 #' 
 #' plot(x, y, type = "l")
@@ -31,17 +33,17 @@
 #' hist(x)
 #' qplot(x, geom = "histogram")
 #'
-#' cdplot(x, y)
-#' qplot(x, fill = y, geom = "density", position = "fill")
+#' # cdplot(factor(x), y)
+#' # qplot(x, fill = y, geom = "density", position = "fill")
 #'
-#' coplot(y ~ x | a + b)
-#' qplot(x, y, facets = a ~ b)
+#' # coplot(y ~ x | a + b)
+#' # qplot(x, y, facets = a ~ b)
 #'
 #' # Many of the geoms are parameterised differently than base graphics. For
 #' # example, hist() is parameterised in terms of the number of bins, while
 #' # geom_histogram() is parameterised in terms of the width of each bin.
-#' hist(x, bins = 100)
-#' qplot(x, geom = "histogram", binwidth = 1)
+#' hist(x, bins = 10)
+#' qplot(x, geom = "histogram", binwidth = .1)
 #'
 #' # qplot() often requires data in a slightly different format to the base
 #' # graphics functions. For example, the bar geom works with untabulated data,
@@ -51,18 +53,19 @@
 #' qplot(x, geom = "bar")
 #' 
 #' barplot(x)
-#' qplot(names(x), x, geom = "bar", stat = "identity")
+#' qplot(seq_along(x), x, geom = "bar", stat = "identity")
 #' 
-#' image(x)
-#' qplot(X1, X2, data = melt(x), geom = "tile", fill = value)
+#' # image(x)
+#' # qplot(X1, X2, data = melt(x), geom = "tile", fill = value)
 #' 
-#' contour(x)
-#' qplot(X1, X2, data = melt(x), geom = "contour", fill = value)
+#' # contour(x)
+#' # qplot(X1, X2, data = melt(x), geom = "contour", fill = value)
 #' 
 #' # Generally, the base graphics functions work with individual vectors, not
 #' # data frames like ggplot2. qplot() will try to construct a data frame if one
 #' # is not specified, but it is not always possible. If you get strange errors,
 #' # you may need to create the data frame yourself.
+#' df <- data.frame(x = x, y = y)
 #' with(df, plot(x, y))
 #' qplot(x, y, data = df)
 #' 
@@ -122,10 +125,11 @@
 #' qplot(1:5, 1:5, col = factor(1:5), size = I(4))
 #' last_plot() + scale_colour_manual(values = rainbow(5))
 #'
-#' # In ggplot2, you can also use palettes with continuous values, with intermediate
-#' # values being linearly interpolated.
+#' # In ggplot2, you can also use palettes with continuous values, 
+#' # with intermediate values being linearly interpolated.
 #'
-#' qplot(0:100, 0:100, col = 0:100, size = I(4)) + scale_colour_gradientn(colours = rainbow(7))
+#' qplot(0:100, 0:100, col = 0:100, size = I(4)) +
+#'   scale_colour_gradientn(colours = rainbow(7))
 #' last_plot() + scale_colour_gradientn(colours = terrain.colors(7))
 #' 
 #' # Graphical parameters

R/translate-qplot-ggplot.r

@@ -12,71 +12,71 @@
 #'
 #' # By default, qplot() assumes that you want a scatterplot, 
 #' # i.e., you want to use geom_point()
-#' qplot(x, y, data = data)
-#' ggplot(data, aes(x, y)) + geom_point()
+#' # qplot(x, y, data = data)
+#' # ggplot(data, aes(x, y)) + geom_point()
 #'
 #' # Using Aesthetics
 #' 
 #' # If you map additional aesthetics, these will be added to the defaults. With
 #' # qplot() there is no way to use different aesthetic mappings (or data) in
 #' # different layers
-#' qplot(x, y, data = data, shape = shape, colour = colour)
-#' ggplot(data, aes(x, y, shape = shape, colour = colour)) + geom_point()
-#'
+#' # qplot(x, y, data = data, shape = shape, colour = colour)
+#' # ggplot(data, aes(x, y, shape = shape, colour = colour)) + geom_point()
+#' # 
 #' # Aesthetic parameters in qplot() always try to map the aesthetic to a
 #' # variable. If the argument is not a variable but a value, effectively a new column
 #' # is added to the original dataset with that value. To set an aesthetic to a
 #' # value and override the default appearance, you surround the value with I() in
 #' # qplot(), or pass it as a parameter to the layer.
-#' qplot(x, y, data = data, colour = I("red"))
-#' ggplot(data, aes(x, y)) + geom_point(colour = "red")
+#' # qplot(x, y, data = data, colour = I("red"))
+#' # ggplot(data, aes(x, y)) + geom_point(colour = "red")
 #' 
 #' # Changing the geom parameter changes the geom added to the plot
-#' qplot(x, y, data = data, geom = "line")
-#' ggplot(data, aes(x, y)) + geom_line()
+#' # qplot(x, y, data = data, geom = "line")
+#' # ggplot(data, aes(x, y)) + geom_line()
 #'
 #' # Not all geoms require both x and y, e.g., geom_bar() and geom_histogram().
 #' # For these two geoms, if the y aesthetic is not supplied, both qplot and 
 #' # ggplot commands default to "count" on the y-axis
-#' ggplot(data, aes(x)) + geom_bar()
-#' qplot(x, data = data, geom = "bar")
+#' # ggplot(data, aes(x)) + geom_bar()
+#' # qplot(x, data = data, geom = "bar")
 #' 
 #' # If a vector of multiple geom names is supplied to the geom argument, each
 #' # geom will be added in turn
-#' qplot(x, y, data = data, geom = c("point", "smooth"))
-#' ggplot(data, aes(x, y)) + geom_point() + geom_smooth()
+#' # qplot(x, y, data = data, geom = c("point", "smooth"))
+#' # ggplot(data, aes(x, y)) + geom_point() + geom_smooth()
 #' 
 #' # Unlike the rest of ggplot2, stats and geoms are independent
-#' qplot(x, y, data = data, stat = "bin")
-#' ggplot(data, aes(x, y)) + geom_point(stat = "bin")
-#' 
+#' # qplot(x, y, data = data, stat = "bin")
+#' # ggplot(data, aes(x, y)) + geom_point(stat = "bin")
+#' # 
 #' # Any layer parameters will be passed on to all layers. Most layers will ignore
 #' # parameters that they don't need
-#' qplot(x, y, data = data, geom = c("point", "smooth"), method = "lm")
-#' ggplot(data, aes(x, y)) + geom_point(method = "lm") + geom_smooth(method = "lm")
+#' # qplot(x, y, data = data, geom = c("point", "smooth"), method = "lm")
+#' # ggplot(data, aes(x, y)) + geom_point(method = "lm") + geom_smooth(method = "lm")
 #' 
 #' # Scales and axes
 #'
 #' # You can control basic properties of the x and y scales with the xlim, ylim,
 #' # xlab and ylab arguments
-#' qplot(x, y, data = data, xlim = c(1, 5), xlab = "my label")
-#' ggplot(data, aes(x, y)) + geom_point() + 
-#' scale_x_continuous("my label", limits = c(1, 5))
+#' # qplot(x, y, data = data, xlim = c(1, 5), xlab = "my label")
+#' # ggplot(data, aes(x, y)) + geom_point() + 
+#' # scale_x_continuous("my label", limits = c(1, 5))
 #' 
-#' qplot(x, y, data = data, xlim = c(1, 5), ylim = c(10, 20))
-#' ggplot(data, aes(x, y)) + geom_point() +
-#' scale_x_continuous(limits = c(1, 5)) + scale_y_continuous(limits = c(10, 20))
+#' # qplot(x, y, data = data, xlim = c(1, 5), ylim = c(10, 20))
+#' # ggplot(data, aes(x, y)) + geom_point() +
+#' # scale_x_continuous(limits = c(1, 5)) + scale_y_continuous(limits = c(10, 20))
 #' 
 #' # Like plot(), qplot() has a convenient way of log transforming the axes.
-#' qplot(x, y, data = data, log = "xy")
-#' ggplot(data, aes(x, y)) + geom_point() + scale_x_log10() + scale_y_log10()
+#' # qplot(x, y, data = data, log = "xy")
+#' # ggplot(data, aes(x, y)) + geom_point() + scale_x_log10() + scale_y_log10()
 #' # There are many other possible transformations, but not all are 
 #' # accessible from within qplot(), see ?scale_continuous for more
 #' 
 #' # Plot options
 #'
 #' # qplot() recognises the same options as plot does, and converts them to their
 #' # ggplot2 equivalents. See ?opts for more on ggplot options
-#' qplot(x, y, data = data, main="title", asp = 1)
-#' ggplot(data, aes(x, y)) + geom_point() + opts(title = "title", aspect.ratio = 1)
+#' # qplot(x, y, data = data, main="title", asp = 1)
+#' # ggplot(data, aes(x, y)) + geom_point() + opts(title = "title", aspect.ratio = 1)
 NULL

R/translate-qplot-gpl.r

@@ -10,12 +10,12 @@
 #' # page 13, of Leland Wilkinson's "The Grammar of Graphics."
 #' # Springer, 2nd edition, 2005.
 #' 
-#' DATA: source("demographics")
-#' DATA: longitude, latitude = map(source("World"))
-#' TRANS: bd = max(birth - death, 0)
-#' COORD: project.mercator()
-#' ELEMENT: point(position(lon * lat), size(bd), color(color.red))
-#' ELEMENT: polygon(position(longitude * latitude))
+#' # DATA: source("demographics")
+#' # DATA: longitude, latitude = map(source("World"))
+#' # TRANS: bd = max(birth - death, 0)
+#' # COORD: project.mercator()
+#' # ELEMENT: point(position(lon * lat), size(bd), color(color.red))
+#' # ELEMENT: polygon(position(longitude * latitude))
 #' 
 #' # This is relatively simple to adapt to the syntax of ggplot2:
 #'
@@ -47,10 +47,10 @@
 #' # Each component is added together with + to create the final plot.
 #'
 #' # Resulting ggplot2 code:
-#' demographics <- transform(demographics, bd = pmax(birth - death, 0)) 
-#' p <- ggplot(demographic, aes(lon, lat))
-#' p <- p + geom_polyogon(data = world)
-#' p <- p + geom_point(aes(size = bd), colour = "red") 
-#' p <- p + coord_map(projection = "mercator")
-#' print(p)
+#' # demographics <- transform(demographics, bd = pmax(birth - death, 0)) 
+#' # p <- ggplot(demographic, aes(lon, lat))
+#' # p <- p + geom_polyogon(data = world)
+#' # p <- p + geom_point(aes(size = bd), colour = "red") 
+#' # p <- p + coord_map(projection = "mercator")
+#' # print(p)
 NULL

R/translate-qplot-lattice.r

@@ -6,7 +6,7 @@
 #'
 #' @name translate_qplot_lattice 
 #' @examples
-#'
+#' \dontrun{
 #' xyplot(rating ~ year, data=movies)
 #' qplot(year, rating, data=movies)
 #'
@@ -69,4 +69,5 @@
 #' # and trellis.par.get() to theme_set() and theme_get().
 #' # More complicated lattice formulas are equivalent to rearranging the data
 #' # before using ggplot2.
+#' }
 NULL

man/aes_group_order.Rd

@@ -32,7 +32,11 @@ a + geom_bar(aes(fill = factor(vs)))
 
 # Using linetypes
 library(reshape2) # for melt
-ec_scaled <- data.frame(date = economics$date, rescaler(economics[, -(1:2)], "range"))
+library(plyr) # for colwise
+rescale01 <- function(x) (x - min(x)) / diff(range(x))
+ec_scaled <- data.frame(
+  date = economics$date,
+  colwise(rescale01)(economics[, -(1:2)]))
 ecm <- melt(ec_scaled, id = "date")
 f <- ggplot(ecm, aes(date, value))
 f + geom_line(aes(linetype = variable))
@@ -59,10 +63,10 @@ h + geom_line(aes(group = Subject))
 h <- h + geom_line(aes(group = Subject))
 # Using the group aesthetic with both geom_line() and geom_smooth()
 # groups the data the same way for both layers
-h + geom_smooth(aes(group = Subject), method = "lm", se = F)
+h + geom_smooth(aes(group = Subject), method = "lm", se = FALSE)
 # Changing the group aesthetic for the smoother layer
 # fits a single line of best fit across all boys
-h + geom_smooth(aes(group = 1), size = 2, method = "lm", se = F)
+h + geom_smooth(aes(group = 1), size = 2, method = "lm", se = FALSE)
 
 # Overriding the default grouping
 # The plot has a discrete scale but you want to draw lines that connect across

man/aes_position.Rd

@@ -18,13 +18,14 @@
 # for each type of cut
 dmod <- lm(price ~ cut, data = diamonds)
 cuts <- data.frame(cut = unique(diamonds$cut), predict(dmod, data.frame(cut =
-unique(diamonds$cut)), se = T)[c("fit", "se.fit")])
+unique(diamonds$cut)), se = TRUE)[c("fit", "se.fit")])
 se <- ggplot(cuts, aes(x = cut, y = fit, ymin = fit - se.fit,
 ymax = fit + se.fit, colour = cut))
 se + geom_pointrange()
 
 # Boxplot with precomputed statistics
 # generate sample data
+library(plyr)
 abc <- adply(matrix(rnorm(100), ncol = 5), 2, quantile, c(0, .25, .5, .75, 1))
 b <- ggplot(abc, aes(x = X1, ymin = "0\%", lower = "25\%", middle = "50\%", upper = "75\%", ymax = "100\%"))
 b + geom_boxplot(stat = "identity")

man/annotation_custom.Rd

@@ -41,12 +41,14 @@ base <- qplot(1:10, 1:10, geom = "blank") + theme_bw()
 require(gridExtra)
 base + annotation_custom(grob = tableGrob(head(iris[ ,1:3])),
         xmin = 3, xmax = 6, ymin = 2, ymax = 8)
-}
-# Inset plot
-g <- ggplotGrob(qplot(1, 1) + opts(plot.background = theme_rect(col = "black")))
-base + annotation_custom(grob = g, xmin = 1, xmax = 10, ymin = 8, ymax = 10)
 # full panel
 base + annotation_custom(grob = roundrectGrob(),
           xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf)
 }
+# Inset plot
+g <- ggplotGrob(qplot(1, 1) +
+  opts(plot.background = theme_rect(col = "black")))
+base +
+  annotation_custom(grob = g, xmin = 1, xmax = 10, ymin = 8, ymax = 10)
+}
 

man/geom_blank.Rd

@@ -41,7 +41,7 @@ a + geom_abline(aes(intercept = a, slope = b), data = df)
 # Suppose you then wanted to remove the geom_point layer
 # If you just remove geom_point, you will get an error
 b <- ggplot(mtcars, aes(x = wt, y = mpg), . ~ cyl)
-b + geom_abline(aes(intercept = a, slope = b), data = df)
+\dontrun{b + geom_abline(aes(intercept = a, slope = b), data = df)}
 # Switching to geom_blank() gets the desired plot
 c <- ggplot(mtcars, aes(x = wt, y = mpg), . ~ cyl) + geom_blank()
 c + geom_abline(aes(intercept = a, slope = b), data = df)