Add set.seed() before runif(), rnorm(), and rpois()

R/aes-colour-fill-alpha.r

@@ -82,6 +82,7 @@
 #'
 #' # For large datasets with overplotting the alpha
 #' # aesthetic will make the points more transparent.
+#' set.seed(1)
 #' df <- data.frame(x = rnorm(5000), y = rnorm(5000))
 #' p  <- ggplot(df, aes(x,y))
 #' p + geom_point()

R/aes-position.r

@@ -65,6 +65,7 @@
 #'
 #' # You can also use geom_segment() to recreate plot(type = "h")
 #' # from base R:
+#' set.seed(1)
 #' 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))

R/coord-transform.r

@@ -68,6 +68,7 @@
 #'   geom_smooth(method = "lm")
 #'
 #' # Also works with discrete scales
+#' set.seed(1)
 #' df <- data.frame(a = abs(rnorm(26)),letters)
 #' plot <- ggplot(df,aes(a,letters)) + geom_point()
 #'

R/geom-boxplot.r

@@ -90,6 +90,7 @@
 #' \donttest{
 #' # It's possible to draw a boxplot with your own computations if you
 #' # use stat = "identity":
+#' set.seed(1)
 #' y <- rnorm(100)
 #' df <- data.frame(
 #'   x = 1,

R/geom-point.r

@@ -77,6 +77,7 @@
 #'
 #' # geom_point warns when missing values have been dropped from the data set
 #' # and not plotted, you can turn this off by setting na.rm = TRUE
+#' set.seed(1)
 #' mtcars2 <- transform(mtcars, mpg = ifelse(runif(32) < 0.2, NA, mpg))
 #' ggplot(mtcars2, aes(wt, mpg)) +
 #'   geom_point()

R/geom-polygon.r

@@ -50,6 +50,7 @@
 #' # Which seems like a lot of work, but then it's easy to add on
 #' # other features in this coordinate system, e.g.:
 #'
+#' set.seed(1)
 #' stream <- data.frame(
 #'   x = cumsum(runif(50, max = 0.1)),
 #'   y = cumsum(runif(50,max = 0.1))

R/geom-segment.r

@@ -61,6 +61,7 @@
 #'   xlim(0.5, 2)
 #'
 #' # You can also use geom_segment to recreate plot(type = "h") :
+#' set.seed(1)
 #' 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))

R/geom-spoke.r

@@ -10,6 +10,8 @@
 #' @export
 #' @examples
 #' df <- expand.grid(x = 1:10, y=1:10)
+#'
+#' set.seed(1)
 #' df$angle <- runif(100, 0, 2*pi)
 #' df$speed <- runif(100, 0, sqrt(0.1 * df$x))
 #'

R/geom-tile.r

@@ -40,6 +40,7 @@
 #' \donttest{
 #' # Justification controls where the cells are anchored
 #' df <- expand.grid(x = 0:5, y = 0:5)
+#' set.seed(1)
 #' df$z <- runif(nrow(df))
 #' # default is compatible with geom_tile()
 #' ggplot(df, aes(x, y, fill = z)) +

R/limits.r

@@ -56,6 +56,7 @@
 #' # There are two ways of setting the axis limits: with limits or
 #' # with coordinate systems. They work in two rather different ways.
 #'
+#' set.seed(1)
 #' last_month <- Sys.Date() - 0:59
 #' df <- data.frame(
 #'   date = last_month,

R/plot.r

@@ -38,6 +38,7 @@
 #' @examples
 #' # Generate some sample data, then compute mean and standard deviation
 #' # in each group
+#' set.seed(1)
 #' df <- data.frame(
 #'   gp = factor(rep(letters[1:3], each = 10)),
 #'   y = rnorm(30)

R/position-stack.r

@@ -59,6 +59,7 @@
 #'   geom_histogram(binwidth = 500, position = "fill")
 #'
 #' # Stacking is also useful for time series
+#' set.seed(1)
 #' series <- data.frame(
 #'   time = c(rep(1, 4),rep(2, 4), rep(3, 4), rep(4, 4)),
 #'   type = rep(c('a', 'b', 'c', 'd'), 4),

R/quick-plot.r

@@ -31,6 +31,7 @@
 #' qplot(mpg, wt, data = mtcars, facets = vs ~ am)
 #'
 #' \donttest{
+#' set.seed(1)
 #' qplot(1:10, rnorm(10), colour = runif(10))
 #' qplot(1:10, letters[1:10])
 #' mod <- lm(mpg ~ wt, data = mtcars)

R/scale-continuous.r

@@ -44,6 +44,7 @@
 #'
 #' # Typically you'll pass a function to the `labels` argument.
 #' # Some common formats are built into the scales package:
+#' set.seed(1)
 #' df <- data.frame(
 #'   x = rnorm(10) * 100000,
 #'   y = seq(0, 1, length.out = 10)

R/scale-date.r

@@ -34,6 +34,7 @@
 #' @seealso [sec_axis()] for how to specify secondary axes
 #' @examples
 #' last_month <- Sys.Date() - 0:29
+#' set.seed(1)
 #' df <- data.frame(
 #'   date = last_month,
 #'   price = runif(30)

R/scale-gradient.r

@@ -23,6 +23,7 @@
 #' @rdname scale_gradient
 #' @export
 #' @examples
+#' set.seed(1)
 #' df <- data.frame(
 #'   x = runif(100),
 #'   y = runif(100),

R/scale-steps.R

@@ -20,6 +20,7 @@
 #' @family colour scales
 #' @export
 #' @examples
+#' set.seed(1)
 #' df <- data.frame(
 #'   x = runif(100),
 #'   y = runif(100),

R/stat-ecdf.r

@@ -26,6 +26,7 @@
 #' }
 #' @export
 #' @examples
+#' set.seed(1)
 #' df <- data.frame(
 #'   x = c(rnorm(100, 0, 3), rnorm(100, 0, 10)),
 #'   g = gl(2, 100)

R/stat-summary.r

@@ -236,6 +236,7 @@ summarise_by_x <- function(data, summary, ...) {
 #' @name hmisc
 #' @examples
 #' if (requireNamespace("Hmisc", quietly = TRUE)) {
+#' set.seed(1)
 #' x <- rnorm(100)
 #' mean_cl_boot(x)
 #' mean_cl_normal(x)
@@ -285,6 +286,7 @@ median_hilow <- wrap_hmisc("smedian.hilow")
 #' }
 #' @export
 #' @examples
+#' set.seed(1)
 #' x <- rnorm(100)
 #' mean_se(x)
 mean_se <- function(x, mult = 1) {

R/utilities-break.r

@@ -15,6 +15,8 @@
 #' table(cut_interval(1:100, 10))
 #' table(cut_interval(1:100, 11))
 #'
+#' set.seed(1)
+#'
 #' table(cut_number(runif(1000), 10))
 #'
 #' table(cut_width(runif(1000), 0.1))