# ggplot

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##### Create a new ggplot

ggplot() initializes a ggplot object. It can be used to declare the input data frame for a graphic and to specify the set of plot aesthetics intended to be common throughout all subsequent layers unless specifically overridden.

##### Usage
ggplot(data = NULL, mapping = aes(), ..., environment = parent.frame())
##### Arguments
data
Default dataset to use for plot. If not already a data.frame, will be converted to one by fortify. If not specified, must be suppled in each layer added to the plot.
mapping
Default list of aesthetic mappings to use for plot. If not specified, must be suppled in each layer added to the plot.
...
Other arguments passed on to methods. Not currently used.
environment
If an variable defined in the aesthetic mapping is not found in the data, ggplot will look for it in this environment. It defaults to using the environment in which ggplot() is called.
##### Details

ggplot() is used to construct the initial plot object, and is almost always followed by + to add component to the plot. There are three common ways to invoke ggplot:

1. ggplot(df, aes(x, y, ))
2. ggplot(df)
3. ggplot()

The first method is recommended if all layers use the same data and the same set of aesthetics, although this method can also be used to add a layer using data from another data frame. See the first example below. The second method specifies the default data frame to use for the plot, but no aesthetics are defined up front. This is useful when one data frame is used predominantly as layers are added, but the aesthetics may vary from one layer to another. The third method initializes a skeleton ggplot object which is fleshed out as layers are added. This method is useful when multiple data frames are used to produce different layers, as is often the case in complex graphics.

• ggplot
##### Examples
library(ggplot2) # Generate some sample data, then compute mean and standard deviation # in each group df <- data.frame( gp = factor(rep(letters[1:3], each = 10)), y = rnorm(30) ) ds <- plyr::ddply(df, "gp", plyr::summarise, mean = mean(y), sd = sd(y)) # The summary data frame ds is used to plot larger red points on top # of the raw data. Note that we don't need to supply data or mapping # in each layer because the defaults from ggplot() are used. ggplot(df, aes(gp, y)) + geom_point() + geom_point(data = ds, aes(y = mean), colour = 'red', size = 3) # Same plot as above, declaring only the data frame in ggplot(). # Note how the x and y aesthetics must now be declared in # each geom_point() layer. ggplot(df) + geom_point(aes(gp, y)) + geom_point(data = ds, aes(gp, mean), colour = 'red', size = 3) # Alternatively we can fully specify the plot in each layer. This # is not useful here, but can be more clear when working with complex # mult-dataset graphics ggplot() + geom_point(data = df, aes(gp, y)) + geom_point(data = ds, aes(gp, mean), colour = 'red', size = 3) + geom_errorbar( data = ds, aes(gp, mean, ymin = mean - sd, ymax = mean + sd), colour = 'red', width = 0.4 ) 
Documentation reproduced from package ggplot2, version 2.2.1, License: GPL-2 | file LICENSE

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