Trim whitespace

R/aaa-.r

@@ -3,7 +3,7 @@
 
 # Upper case first letter of string
 # This comes from the examples of some R function.
-# 
+#
 # @keyword internal
 firstUpper <- function(s) {
   paste(toupper(substring(s, 1,1)), substring(s, 2), sep="")
@@ -13,7 +13,7 @@ TopLevel <- proto(expr = {
   find_all <- function(., only.documented = FALSE) {
     names <- ls(pattern=paste("^", firstUpper(.$class()), "[A-Z].+", sep=""), parent.env(TopLevel))
     objs <- structure(lapply(names, get), names=names)
-    
+
     if (only.documented) objs <- objs[sapply(objs, function(x) get("doc", x))]
     objs
   }
@@ -24,23 +24,23 @@ TopLevel <- proto(expr = {
     }
     get(fullname)
   }
-  
+
   my_name <- function(., prefix=TRUE) {
     if (!prefix) return(.$objname)
     paste(.$class(), .$objname, sep="_")
   }
   my_names <- function(.) .$my_name()
-  
+
   myName <- function(.) {
     ps(firstUpper(.$class()), ps(firstUpper(strsplit(.$objname, "_")[[1]])))
   }
 
   params <- function(.) {
     param <- .$parameters()
     if (length(param) == 0) return()
-  
+
     if(!exists("required_aes", .)) return(param)
-  
+
     aesthetics <- c(.$required_aes, names(.$default_aes()))
     param[setdiff(names(param), aesthetics)]
   }

R/aes-colour-fill-alpha.r

@@ -1,13 +1,13 @@
 #' Colour related aesthetics: colour, fill and alpha
-#' 
-#' This page demonstrates the usage of a sub-group 
+#'
+#' This page demonstrates the usage of a sub-group
 #' of aesthetics; colour, fill and alpha.
 #'
 #' @name aes_colour_fill_alpha
-#' @aliases colour color fill alpha 
+#' @aliases colour color fill alpha
 #' @examples
 #' \donttest{
-#' 
+#'
 #' # Bar chart example
 #' c <- ggplot(mtcars, aes(factor(cyl)))
 #' # Default plotting
@@ -18,7 +18,7 @@
 #' c + geom_bar(colour = "red")
 #' # Combining both, you can see the changes more clearly
 #' c + geom_bar(fill = "white", colour = "red")
-#' 
+#'
 #' # The aesthetic fill also takes different colouring scales
 #' # setting fill equal to a factor varible uses a discrete colour scale
 #' k <- ggplot(mtcars, aes(factor(cyl), fill = factor(vs)))
@@ -35,7 +35,7 @@
 #' b + geom_line(colour = "green")
 #' b + geom_point()
 #' b + geom_point(colour = "red")
-#' 
+#'
 #' # For large datasets with overplotting the alpha
 #' # aesthetic will make the points more transparent
 #' df <- data.frame(x = rnorm(5000), y = rnorm(5000))
@@ -44,15 +44,15 @@
 #' h + geom_point(alpha = 0.5)
 #' h + geom_point(alpha = 1/10)
 #'
-#' #If a geom uses both fill and colour, alpha will only modify the fill colour 
+#' #If a geom uses both fill and colour, alpha will only modify the fill colour
 #' c + geom_bar(fill = "dark grey", colour = "black")
 #' c + geom_bar(fill = "dark grey", colour = "black", alpha = 1/3)
-#' 
+#'
 #' # Alpha can also be used to add shading
 #' j <- b + geom_line()
 #' j
 #' yrng <- range(economics$unemploy)
-#' j <- j + geom_rect(aes(NULL, NULL, xmin = start, xmax = end, fill = party), 
+#' j <- j + geom_rect(aes(NULL, NULL, xmin = start, xmax = end, fill = party),
 #' ymin = yrng[1], ymax = yrng[2], data = presidential)
 #' j
 #' library(scales) # to access the alpha function

R/aes-group-order.r

@@ -1,21 +1,21 @@
 #' Aesthetics: group, order
-#' 
+#'
 #' @name aes_group_order
-#' @aliases group order 
-#' 
-#' @examples 
+#' @aliases group order
+#'
+#' @examples
 #' \donttest{
-#'  
+#'
 #' # By default, the group is set to the interaction of all discrete variables in the
 #' # plot. This often partitions the data correctly, but when it does not, or when
 #' # no discrete variable is used in the plot, you will need to explicitly define the
 #' # grouping structure, by mapping group to a variable that has a different value
 #' # for each group.
-#' 
-#' # For most applications you can simply specify the grouping with 
-#' # various aesthetics (colour, shape, fill, linetype) or with facets.    
 #'
-#' p <- ggplot(mtcars, aes(wt, mpg)) 
+#' # For most applications you can simply specify the grouping with
+#' # various aesthetics (colour, shape, fill, linetype) or with facets.
+#'
+#' p <- ggplot(mtcars, aes(wt, mpg))
 #' # A basic scatter plot
 #' p + geom_point(size = 4)
 #' # The colour aesthetic
@@ -28,45 +28,45 @@
 #' a + geom_bar()
 #' a + geom_bar(aes(fill = factor(cyl)))
 #' a + geom_bar(aes(fill = factor(vs)))
-#' 
+#'
 #' # Using linetypes
 #' library(reshape2) # for melt
 #' library(plyr) # for colwise
 #' rescale01 <- function(x) (x - min(x)) / diff(range(x))
 #' ec_scaled <- data.frame(
-#'   date = economics$date, 
-#'   colwise(rescale01)(economics[, -(1:2)])) 
+#'   date = economics$date,
+#'   colwise(rescale01)(economics[, -(1:2)]))
 #' ecm <- melt(ec_scaled, id = "date")
 #' f <- ggplot(ecm, aes(date, value))
 #' f + geom_line(aes(linetype = variable))
-#' 
+#'
 #' # Using facets
 #' k <- ggplot(diamonds, aes(carat, ..density..)) + geom_histogram(binwidth = 0.2)
 #' k + facet_grid(. ~ cut)
-#'  
+#'
 #' # There are three common cases where the default is not enough, and we
 #' # will consider each one below. In the following examples, we will use a simple
 #' # longitudinal dataset, Oxboys, from the nlme package. It records the heights
 #' # (height) and centered ages (age) of 26 boys (Subject), measured on nine
 #' # occasions (Occasion).
-#'  
+#'
 #' # Multiple groups with one aesthetic
 #' library(nlme)
 #' h <- ggplot(Oxboys, aes(age, height))
 #' # A single line tries to connect all the observations
 #' h + geom_line()
 #' # The group aesthetic maps a different line for each subject
 #' h + geom_line(aes(group = Subject))
-#' 
+#'
 #' # Different groups on different layers
-#' h <- 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 = FALSE)
-#' # Changing the group aesthetic for the smoother layer 
+#' # 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 = FALSE)
-#' 
+#'
 #' # Overriding the default grouping
 #' # The plot has a discrete scale but you want to draw lines that connect across
 #' # groups. This is the strategy used in interaction plots, profile plots, and parallel
@@ -79,12 +79,12 @@
 #' # we again need to override the default grouping for that layer with aes(group = Subject)
 #' boysbox <- boysbox + geom_boxplot()
 #' boysbox + geom_line(aes(group = Subject), colour = "blue")
-#' 
+#'
 #' # Use the order aesthetic to change stacking order of bar charts
-#' w <- ggplot(diamonds, aes(clarity, fill = cut)) 
+#' w <- ggplot(diamonds, aes(clarity, fill = cut))
 #' w + geom_bar()
 #' w + geom_bar(aes(order = desc(cut)))
-#' 
+#'
 #' # Can also be used to change plot order of scatter plots
 #' d <- ggplot(diamonds, aes(carat, price, colour = cut))
 #' d + geom_point()

R/aes-linetype-size-shape.r

@@ -1,10 +1,10 @@
 #' Differentiation related aesthetics: linetype, size, shape
-#' 
-#' This page demonstrates the usage of a sub-group 
+#'
+#' This page demonstrates the usage of a sub-group
 #' of aesthetics; linetype, size and shape.
-#' 
+#'
 #' @name aes_linetype_size_shape
-#' @aliases linetype size shape 
+#' @aliases linetype size shape
 #' @examples
 #'
 #' # Line types should be specified with either an integer, a name, or with a string of
@@ -18,8 +18,8 @@
 #' f + geom_line(linetype = 2)
 #' f + geom_line(linetype = "dotdash")
 #
-#' # An example with hex strings, the string "33" specifies three units on followed 
-#' # by three off and "3313" specifies three units on followed by three off followed 
+#' # An example with hex strings, the string "33" specifies three units on followed
+#' # by three off and "3313" specifies three units on followed by three off followed
 #' # by one on and finally three off.
 #' f + geom_line(linetype = "3313")
 #'
@@ -28,19 +28,19 @@
 #' library(reshape2)
 #' rescale01 <- function(x) (x - min(x)) / diff(range(x))
 #' ec_scaled <- data.frame(
-#'   date = economics$date, 
-#'   colwise(rescale01)(economics[, -(1:2)])) 
+#'   date = economics$date,
+#'   colwise(rescale01)(economics[, -(1:2)]))
 #' ecm <- melt(ec_scaled, id = "date")
 #' qplot(date, value, data = ecm, geom = "line", linetype = variable)
-#' 
+#'
 #' # Size examples
-#' # Should be specified with a numerical value (in millimetres), 
+#' # Should be specified with a numerical value (in millimetres),
 #' # or from a variable source
 #' p <- ggplot(mtcars, aes(wt, mpg))
 #' p + geom_point(size = 4)
 #' p + geom_point(aes(size = qsec))
 #' p + geom_point(size = 2.5) + geom_hline(yintercept = 25, size = 3.5)
-#' 
+#'
 #' # Shape examples
 #' # Shape takes four types of values: an integer in [0, 25],
 #' # a single character-- which uses that character as the plotting symbol,
@@ -54,15 +54,15 @@
 #'
 #' # Shape can also be mapped from a variable
 #' p + geom_point(aes(shape = factor(cyl)))
-#'  
+#'
 #' # A look at all 25 symbols
 #' df2 <- data.frame(x = 1:5 , y = 1:25, z = 1:25)
 #' s <- ggplot(df2, aes(x = x, y = y))
 #' s + geom_point(aes(shape = z), size = 4) + scale_shape_identity()
 #' # While all symbols have a foreground colour, symbols 19-25 also take a
 #' # background colour (fill)
-#' s + geom_point(aes(shape = z), size = 4, colour = "Red") + 
+#' s + geom_point(aes(shape = z), size = 4, colour = "Red") +
 #'   scale_shape_identity()
-#' s + geom_point(aes(shape = z), size = 4, colour = "Red", fill = "Black") + 
+#' s + geom_point(aes(shape = z), size = 4, colour = "Red", fill = "Black") +
 #'   scale_shape_identity()
 NULL

R/aes-position.r

@@ -1,42 +1,42 @@
 #' Position related aesthetics: x, y, xmin, xmax, ymin, ymax, xend, yend
-#' 
-#' This page demonstrates the usage of a sub-group 
+#'
+#' This page demonstrates the usage of a sub-group
 #' of aesthetics; x, y, xmin, xmax, ymin, ymax, xend, and yend.
 #'
 #' @name aes_position
-#' @aliases x y xmin xmax ymin ymax xend yend 
+#' @aliases x y xmin xmax ymin ymax xend yend
 #' @examples
-#' 
-#' # Generate data: means and standard errors of means for prices 
-#' # for each type of cut 
-#' dmod <- lm(price ~ cut, data = diamonds) 
-#' cuts <- data.frame(cut = unique(diamonds$cut), predict(dmod, data.frame(cut = 
+#'
+#' # Generate data: means and standard errors of means for prices
+#' # for each type of cut
+#' dmod <- lm(price ~ cut, data = diamonds)
+#' cuts <- data.frame(cut = unique(diamonds$cut), predict(dmod, data.frame(cut =
 #' unique(diamonds$cut)), se = TRUE)[c("fit", "se.fit")])
-#' se <- ggplot(cuts, aes(x = cut, y = fit, ymin = fit - se.fit, 
-#' ymax = fit + se.fit, colour = cut)) 
+#' se <- ggplot(cuts, aes(x = cut, y = fit, ymin = fit - se.fit,
+#' ymax = fit + se.fit, colour = cut))
 #' se + geom_pointrange()
 #'
 #' # Boxplot with precomputed statistics
 #' # generate sample data
 #' library(plyr)
 #' abc <- adply(matrix(rnorm(100), ncol = 5), 2, quantile, c(0, .25, .5, .75, 1))
-#' b <- ggplot(abc, aes(x = X1, ymin = "0%", lower = "25%", middle = "50%", upper = "75%", ymax = "100%")) 
+#' b <- ggplot(abc, aes(x = X1, ymin = "0%", lower = "25%", middle = "50%", upper = "75%", ymax = "100%"))
 #' b + geom_boxplot(stat = "identity")
 #'
 #' # Using annotate
 #' p <- ggplot(mtcars, aes(wt, mpg)) + geom_point()
 #' p + annotate("rect", xmin = 2, xmax = 3.5, ymin = 2, ymax = 25, fill = "dark grey", alpha = .5)
-#' 
+#'
 #' # Geom_segment examples
 #' library(grid)
-#' p + geom_segment(aes(x = 2, y = 15, xend = 2, yend = 25), arrow = arrow(length = unit(0.5, "cm"))) 
+#' p + geom_segment(aes(x = 2, y = 15, xend = 2, yend = 25), arrow = arrow(length = unit(0.5, "cm")))
 #' p + geom_segment(aes(x = 2, y = 15, xend = 3, yend = 15), arrow = arrow(length = unit(0.5, "cm")))
 #' p + geom_segment(aes(x = 5, y = 30, xend = 3.5, yend = 25), arrow = arrow(length = unit(0.5, "cm")))
 #'
-#' # You can also use geom_segment to recreate plot(type = "h") : 
-#' counts <- as.data.frame(table(x = rpois(100, 5))) 
-#' counts$x <- as.numeric(as.character(counts$x)) 
+#' # You can also use geom_segment to recreate plot(type = "h") :
+#' counts <- as.data.frame(table(x = rpois(100, 5)))
+#' counts$x <- as.numeric(as.character(counts$x))
 #' with(counts, plot(x, Freq, type = "h", lwd = 10))
-#' 
+#'
 #' qplot(x, Freq, data = counts, geom = "segment", yend = 0, xend = x, size = I(10))
 NULL