geom_histogram {ggplot2} | R Documentation |
Histogram
geom_histogram(mapping=NULL, data=NULL, stat="bin", position="stack", ...)
mapping |
mapping between variables and aesthetics generated by aes |
data |
dataset used in this layer, if not specified uses plot dataset |
stat |
statistic used by this layer |
position |
position adjustment used by this layer |
... |
ignored |
geom_histogram is an alias for geom_bar + stat_bin so you will need to look at the documentation for those objects to get more information about the parameters.
This page describes geom_histogram, see layer
and qplot
for how to create a complete plot from individual components.
A layer
The following aesthetics can be used with geom_histogram. Aesthetics are mapped to variables in the data with the aes
function: geom\_histogram(\code{aes}(x = var))
x
: x position (required)
colour
: border colour
fill
: internal colour
size
: size
linetype
: line type
weight
: observation weight used in statistical transformation
geom_histogram only allows you to set the width of the bins (with the binwidth parameter), not the number of bins, and it certainly does not suport the use of common heuristics to select the number of bins. In practice, you will need to use multiple bin widths to discover all the signal in the data, and having bins with meaningful widths (rather than some arbitrary fraction of the range of the data) is more interpretable.
Hadley Wickham, http://had.co.nz/
stat_bin
: for more details of the binning alogirithm
position_dodge
: for creating side-by-side barcharts
position_stack
: for more info on stacking
## Not run: # Simple examles 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. 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 ggplot(diamonds, aes(x=price)) + geom_bar() hist_cut <- ggplot(diamonds, aes(x=price, fill=cut)) hist_cut + geom_bar() # defaults to stacking hist_cut + geom_bar(position="fill") hist_cut + geom_bar(position="dodge") ## End(Not run)