set.seed(5689)
movies <- movies[sample(nrow(movies), 1000), ]
# Simple examples
qplot(rating, data=movies, geom="histogram")
qplot(rating, data=movies, weight=votes, geom="histogram")
qplot(rating, data=movies, weight=votes, geom="histogram", binwidth=1)
qplot(rating, data=movies, weight=votes, geom="histogram", binwidth=0.1)
# More complex
m <- ggplot(movies, aes(x=rating))
m + geom_histogram()
m + geom_histogram(aes(y = ..density..)) + geom_density()
m + geom_histogram(binwidth = 1)
m + geom_histogram(binwidth = 0.5)
m + geom_histogram(binwidth = 0.1)
# Add aesthetic mappings
m + geom_histogram(aes(weight = votes))
m + geom_histogram(aes(y = ..count..))
m + geom_histogram(aes(fill = ..count..))
# Change scales
m + geom_histogram(aes(fill = ..count..)) +
scale_fill_gradient("Count", low = "green", high = "red")
# Often we don't want the height of the bar to represent the
# count of observations, but the sum of some other variable.
# For example, the following plot shows the number of movies
# in each rating.
qplot(rating, data=movies, geom="bar", binwidth = 0.1)
# If, however, we want to see the number of votes cast in each
# category, we need to weight by the votes variable
qplot(rating, data=movies, geom="bar", binwidth = 0.1,
weight=votes, ylab = "votes")
m <- ggplot(movies, aes(x = votes))
# For transformed scales, binwidth applies to the transformed data.
# The bins have constant width on the transformed scale.
m + geom_histogram() + scale_x_log10()
m + geom_histogram(binwidth = 1) + scale_x_log10()
m + geom_histogram() + scale_x_sqrt()
m + geom_histogram(binwidth = 10) + scale_x_sqrt()
# For transformed coordinate systems, the binwidth applies to the
# raw data. The bins have constant width on the original scale.
# Using log scales does not work here, because the first
# bar is anchored at zero, and so when transformed becomes negative
# infinity. This is not a problem when transforming the scales, because
# no observations have 0 ratings.
should_stop(m + geom_histogram() + coord_trans(x = "log10"))
m + geom_histogram() + coord_trans(x = "sqrt")
m + geom_histogram(binwidth=1000) + coord_trans(x = "sqrt")
# You can also transform the y axis. Remember that the base of the bars
# has value 0, so log transformations are not appropriate
m <- ggplot(movies, aes(x = rating))
m + geom_histogram(binwidth = 0.5) + scale_y_sqrt()
m + geom_histogram(binwidth = 0.5) + scale_y_reverse()
# Set aesthetics to fixed value
m + geom_histogram(colour = "darkgreen", fill = "white", binwidth = 0.5)
# Use facets
m <- m + geom_histogram(binwidth = 0.5)
m + facet_grid(Action ~ Comedy)
# Often more useful to use density on the y axis when facetting
m <- m + aes(y = ..density..)
m + facet_grid(Action ~ Comedy)
m + facet_wrap(~ mpaa)
# Multiple histograms on the same graph
# see ?position, ?position_fill, etc for more details.
set.seed(6298)
diamonds_small <- diamonds[sample(nrow(diamonds), 1000), ]
ggplot(diamonds_small, aes(x=price)) + geom_bar()
hist_cut <- ggplot(diamonds_small, aes(x=price, fill=cut))
hist_cut + geom_bar() # defaults to stacking
hist_cut + geom_bar(position="fill")
hist_cut + geom_bar(position="dodge")
# This is easy in ggplot2, but not visually effective. It's better
# to use a frequency polygon or density plot. Like this:
ggplot(diamonds_small, aes(price, ..density.., colour = cut)) +
geom_freqpoly(binwidth = 1000)
# Or this:
ggplot(diamonds_small, aes(price, colour = cut)) +
geom_density()
# Or if you want to be fancy, maybe even this:
ggplot(diamonds_small, aes(price, fill = cut)) +
geom_density(alpha = 0.2)
# Which looks better when the distributions are more distinct
ggplot(diamonds_small, aes(depth, fill = cut)) +
geom_density(alpha = 0.2) + xlim(55, 70)Run the code above in your browser using DataCamp Workspace