`geom_histogram`

is an alias for
`geom_bar`

plus `stat_bin`

so you
will need to look at the documentation for those objects
to get more information about the parameters.```
geom_histogram(mapping = NULL, data = NULL, stat = "bin",
position = "stack", ...)
```

mapping

The aesthetic mapping, usually constructed
with

`aes`

or `aes_string`

. Only
needs to be set at the layer level if you are overriding
the plot defaults.data

A layer specific dataset - only needed if you
want to override the plot defaults.

stat

The statistical transformation to use on the
data for this layer.

position

The position adjustment to use for
overlappling points on this layer

`stat_bin`

uses 30 bins - this is not a
good default, but the idea is to get you experimenting
with different binwidths. You may need to look at a few
to uncover the full story behind your data.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)