Fix line widths

hadley · hadley · commit 04067257ab4a · 2014-04-23T15:47:30.000-05:00
diff --git a/R/aes-position.r b/R/aes-position.r
@@ -20,23 +20,29 @@
 #' # 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)
+#' 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 = 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")))
+#' 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))
 #' with(counts, plot(x, Freq, type = "h", lwd = 10))
 #'
-#' qplot(x, Freq, data = counts, geom = "segment", yend = 0, xend = x, size = I(10))
+#' qplot(x, Freq, data = counts, geom = "segment", yend = 0, xend = x,
+#'   size = I(10))
 NULL
diff --git a/R/geom-boxplot.r b/R/geom-boxplot.r
@@ -92,7 +92,8 @@
 #' # Using precomputed statistics
 #' # generate sample data
 #' 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")
 #' b + geom_boxplot(stat = "identity") + coord_flip()
 #' b + geom_boxplot(aes(fill = X1), stat = "identity")
diff --git a/R/geom-linerange.r b/R/geom-linerange.r
@@ -14,7 +14,8 @@
 #' # 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")])
+#' cuts <- data.frame(cut = unique(diamonds$cut),
+#'   predict(dmod, data.frame(cut = unique(diamonds$cut)), se=TRUE)[c("fit","se.fit")])
 #'
 #' qplot(cut, fit, data=cuts)
 #' # With a bar chart, we are comparing lengths, so the y-axis is
diff --git a/R/geom-map.r b/R/geom-map.r
@@ -50,9 +50,15 @@ NULL
 #' crimesm <- melt(crimes, id = 1)
 #' if (require(maps)) {
 #'   states_map <- map_data("state")
-#'   ggplot(crimes, aes(map_id = state)) + geom_map(aes(fill = Murder), map = states_map) + expand_limits(x = states_map$long, y = states_map$lat)
+#'   ggplot(crimes, aes(map_id = state)) +
+#'     geom_map(aes(fill = Murder), map = states_map) +
+#'     expand_limits(x = states_map$long, y = states_map$lat)
+#'
 #'   last_plot() + coord_map()
-#'   ggplot(crimesm, aes(map_id = state)) + geom_map(aes(fill = value), map = states_map) + expand_limits(x = states_map$long, y = states_map$lat) + facet_wrap( ~ variable)
+#'   ggplot(crimesm, aes(map_id = state)) +
+#'     geom_map(aes(fill = value), map = states_map) +
+#'     expand_limits(x = states_map$long, y = states_map$lat) +
+#'     facet_wrap( ~ variable)
 #' }
 geom_map <- function(mapping = NULL, data = NULL, map, stat = "identity", ...) {
 
diff --git a/R/geom-segment.r b/R/geom-segment.r
@@ -12,7 +12,8 @@
 #' @examples
 #' library(grid) # needed for arrow function
 #' p <- ggplot(seals, aes(x = long, y = lat))
-#' (p <- p + geom_segment(aes(xend = long + delta_long, yend = lat + delta_lat), arrow = arrow(length = unit(0.1,"cm"))))
+#' (p <- p + geom_segment(aes(xend = long + delta_long, yend = lat + delta_lat),
+#'   arrow = arrow(length = unit(0.1,"cm"))))
 #'
 #' if (require("maps")) {
 #'
@@ -40,7 +41,8 @@
 #' b <- ggplot(mtcars, aes(wt, mpg)) + geom_point()
 #' b + geom_segment(aes(x = 2, y = 15, xend = 2, yend = 25))
 #' b + geom_segment(aes(x = 2, y = 15, xend = 3, yend = 15))
-#' b + geom_segment(aes(x = 5, y = 30, xend = 3.5, yend = 25), arrow = arrow(length = unit(0.5, "cm")))
+#' b + geom_segment(aes(x = 5, y = 30, xend = 3.5, yend = 25),
+#'    arrow = arrow(length = unit(0.5, "cm")))
 geom_segment <- function (mapping = NULL, data = NULL, stat = "identity",
   position = "identity", arrow = NULL, lineend = "butt", na.rm = FALSE, ...) {
 
diff --git a/R/geom-tile.r b/R/geom-tile.r
@@ -65,7 +65,8 @@
 #' # You can manually set the colour of the tiles using
 #' # scale_manual
 #' col <- c("darkblue", "blue", "green", "orange", "red")
-#' qplot(x, y, fill=col[z], data=example, geom="tile", width=w, group=1) + scale_fill_identity(labels=letters[1:5], breaks=col)
+#' qplot(x, y, fill=col[z], data=example, geom="tile", width=w, group=1) +
+#'   scale_fill_identity(labels=letters[1:5], breaks=col)
 #' }
 geom_tile <- function (mapping = NULL, data = NULL, stat = "identity", position = "identity", ...) {
   GeomTile$new(mapping = mapping, data = data, stat = stat, position = position, ...)
diff --git a/R/guide-legend.r b/R/guide-legend.r
@@ -516,3 +516,5 @@ guide_gengrob.legend <- function(guide, theme) {
 
   gt
 }
+
+globalVariables(c("R", "key.row", "key.col", "label.row", "label.col"))
diff --git a/R/stat-function.r b/R/stat-function.r
@@ -30,11 +30,13 @@
 #' # Or
 #' qplot(c(-5, 5), geom = "blank") + stat_function(fun = dnorm)
 #' # To specify a different mean or sd, use the args parameter to supply new values
-#' ggplot(data.frame(x = c(-5, 5)), aes(x)) + stat_function(fun = dnorm, args = list(mean = 2, sd = .5))
+#' ggplot(data.frame(x = c(-5, 5)), aes(x)) +
+#'   stat_function(fun = dnorm, args = list(mean = 2, sd = .5))
 #'
 #' # Two functions on the same plot
 #' f <- ggplot(data.frame(x = c(0, 10)), aes(x))
-#' f + stat_function(fun = sin, colour = "red") + stat_function(fun = cos, colour = "blue")
+#' f + stat_function(fun = sin, colour = "red") +
+#'   stat_function(fun = cos, colour = "blue")
 #'
 #' # Using a custom function
 #' test <- function(x) {x ^ 2 + x + 20}
diff --git a/man/aes_position.Rd b/man/aes_position.Rd
@@ -28,24 +28,30 @@ se + geom_pointrange()
 # 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)
+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 = 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")))
+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))
 with(counts, plot(x, Freq, type = "h", lwd = 10))
 
-qplot(x, Freq, data = counts, geom = "segment", yend = 0, xend = x, size = I(10))
+qplot(x, Freq, data = counts, geom = "segment", yend = 0, xend = x,
+  size = I(10))
 }
 
diff --git a/man/geom_boxplot.Rd b/man/geom_boxplot.Rd
@@ -117,7 +117,8 @@ qplot(year, budget, data = movies, geom = "boxplot",
 # Using precomputed statistics
 # generate sample data
 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")
 b + geom_boxplot(stat = "identity") + coord_flip()
 b + geom_boxplot(aes(fill = X1), stat = "identity")
diff --git a/man/geom_linerange.Rd b/man/geom_linerange.Rd
@@ -35,7 +35,8 @@ An interval represented by a vertical line.
 # 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")])
+cuts <- data.frame(cut = unique(diamonds$cut),
+  predict(dmod, data.frame(cut = unique(diamonds$cut)), se=TRUE)[c("fit","se.fit")])
 
 qplot(cut, fit, data=cuts)
 # With a bar chart, we are comparing lengths, so the y-axis is
diff --git a/man/geom_map.Rd b/man/geom_map.Rd
@@ -68,9 +68,15 @@ library(reshape2) # for melt
 crimesm <- melt(crimes, id = 1)
 if (require(maps)) {
   states_map <- map_data("state")
-  ggplot(crimes, aes(map_id = state)) + geom_map(aes(fill = Murder), map = states_map) + expand_limits(x = states_map$long, y = states_map$lat)
+  ggplot(crimes, aes(map_id = state)) +
+    geom_map(aes(fill = Murder), map = states_map) +
+    expand_limits(x = states_map$long, y = states_map$lat)
+
   last_plot() + coord_map()
-  ggplot(crimesm, aes(map_id = state)) + geom_map(aes(fill = value), map = states_map) + expand_limits(x = states_map$long, y = states_map$lat) + facet_wrap( ~ variable)
+  ggplot(crimesm, aes(map_id = state)) +
+    geom_map(aes(fill = value), map = states_map) +
+    expand_limits(x = states_map$long, y = states_map$lat) +
+    facet_wrap( ~ variable)
 }
 }
 
diff --git a/man/geom_segment.Rd b/man/geom_segment.Rd
@@ -42,7 +42,8 @@ Single line segments.
 \examples{
 library(grid) # needed for arrow function
 p <- ggplot(seals, aes(x = long, y = lat))
-(p <- p + geom_segment(aes(xend = long + delta_long, yend = lat + delta_lat), arrow = arrow(length = unit(0.1,"cm"))))
+(p <- p + geom_segment(aes(xend = long + delta_long, yend = lat + delta_lat),
+  arrow = arrow(length = unit(0.1,"cm"))))
 
 if (require("maps")) {
 
@@ -70,7 +71,8 @@ library(grid) # needed for arrow function
 b <- ggplot(mtcars, aes(wt, mpg)) + geom_point()
 b + geom_segment(aes(x = 2, y = 15, xend = 2, yend = 25))
 b + geom_segment(aes(x = 2, y = 15, xend = 3, yend = 15))
-b + geom_segment(aes(x = 5, y = 30, xend = 3.5, yend = 25), arrow = arrow(length = unit(0.5, "cm")))
+b + geom_segment(aes(x = 5, y = 30, xend = 3.5, yend = 25),
+   arrow = arrow(length = unit(0.5, "cm")))
 }
 \seealso{
 \code{\link{geom_path}} and \code{\link{geom_line}} for multi-
diff --git a/man/geom_tile.Rd b/man/geom_tile.Rd
@@ -89,7 +89,8 @@ qplot(x, y, fill=factor(z), data=example, geom="tile", width=w)
 # You can manually set the colour of the tiles using
 # scale_manual
 col <- c("darkblue", "blue", "green", "orange", "red")
-qplot(x, y, fill=col[z], data=example, geom="tile", width=w, group=1) + scale_fill_identity(labels=letters[1:5], breaks=col)
+qplot(x, y, fill=col[z], data=example, geom="tile", width=w, group=1) +
+  scale_fill_identity(labels=letters[1:5], breaks=col)
 }
 }
 
diff --git a/man/label_wrap_gen.Rd b/man/label_wrap_gen.Rd
@@ -6,7 +6,7 @@
 label_wrap_gen(width = 25)
 }
 \arguments{
-  \item{width}{integer, target column width for output.}
+\item{width}{integer, target column width for output.}
 }
 \description{
 Uses \code{\link[base]{strwrap}} for line wrapping.
diff --git a/man/labeller.Rd b/man/labeller.Rd
@@ -6,13 +6,11 @@
 labeller(..., keep.as.numeric = FALSE)
 }
 \arguments{
-  \item{...}{Named arguments of the form
-  \code{variable=values}, where \code{values} could be a
-  vector or method.}
+\item{...}{Named arguments of the form \code{variable=values},
+where \code{values} could be a vector or method.}
 
-  \item{keep.as.numeric}{logical, default TRUE. When FALSE,
-  converts numeric values supplied as margins to the facet
-  to characters.}
+\item{keep.as.numeric}{logical, default TRUE. When FALSE, converts numeric
+values supplied as margins to the facet to characters.}
 }
 \value{
 Function to supply to
diff --git a/man/stat_function.Rd b/man/stat_function.Rd
@@ -60,11 +60,13 @@ qplot(c(-5, 5), stat = "function", fun = dnorm, geom = "line")
 # Or
 qplot(c(-5, 5), geom = "blank") + stat_function(fun = dnorm)
 # To specify a different mean or sd, use the args parameter to supply new values
-ggplot(data.frame(x = c(-5, 5)), aes(x)) + stat_function(fun = dnorm, args = list(mean = 2, sd = .5))
+ggplot(data.frame(x = c(-5, 5)), aes(x)) +
+  stat_function(fun = dnorm, args = list(mean = 2, sd = .5))
 
 # Two functions on the same plot
 f <- ggplot(data.frame(x = c(0, 10)), aes(x))
-f + stat_function(fun = sin, colour = "red") + stat_function(fun = cos, colour = "blue")
+f + stat_function(fun = sin, colour = "red") +
+  stat_function(fun = cos, colour = "blue")
 
 # Using a custom function
 test <- function(x) {x ^ 2 + x + 20}

Original file line number	Diff line number	Diff line change
`@@ -516,3 +516,5 @@ guide_gengrob.legend <- function(guide, theme) {`
`516`	`516`
`517`	`517`	`gt`
`518`	`518`	`}`
	`519`	`+`
	`520`	`+globalVariables(c("R", "key.row", "key.col", "label.row", "label.col"))`