R ggplot2 ggplot 创建一个新的ggplot

ggplot() 初始化 ggplot 对象。它可用于声明图形的输入数据帧，并指定一组在所有后续层中通用的绘图美学，除非特别覆盖。

用法

ggplot(data = NULL, mapping = aes(), ..., environment = parent.frame())

参数

data: 用于绘图的默认数据集。如果还不是 data.frame，将由 fortify() 转换为 data.frame。如果未指定，则必须在添加到绘图的每个图层中提供。
mapping: 用于绘图的默认美学映射列表。如果未指定，则必须在添加到绘图的每个图层中提供。
...: 其他参数传递给方法。目前未使用。
environment: 在整洁评估之前使用。

细节

ggplot() 用于构造初始绘图对象，并且后面几乎总是跟有一个加号 ( + ) 以将组件添加到绘图中。

用于调用 ggplot() 的三种常见模式：

ggplot(data = df, mapping = aes(x, y, other aesthetics))
ggplot(data = df)
ggplot()

如果所有图层都使用相同的数据和相同的美学集，则建议使用第一种模式，尽管在使用来自另一个数据帧的数据添加图层时也可以使用此方法。

第二个模式指定用于绘图的默认 DataFrame ，但没有预先定义美观。当一个 DataFrame 主要用于绘图但美观程度因图层而异时，这非常有用。

第三种模式初始化一个骨架 ggplot 对象，该对象随着层的添加而充实。当使用多个数据帧生成不同的图层时(复杂图形中经常出现这种情况)，这非常有用。

参数中的 data = 和 mapping = 规范是可选的(在实践中经常被省略)，只要数据和映射值以正确的顺序传递到函数中即可。然而，在下面的示例中，为了清楚起见，它们保留在原处。

例子

# Create a data frame with some sample data, then create a data frame
# containing the mean value for each group in the sample data.
set.seed(1)

sample_df <- data.frame(
  group = factor(rep(letters[1:3], each = 10)),
  value = rnorm(30)
)

group_means_df <- setNames(
  aggregate(value ~ group, sample_df, mean),
  c("group", "group_mean")
)

# The following three code blocks create the same graphic, each using one
# of the three patterns specified above. In each graphic, the sample data
# are plotted in the first layer and the group means data frame is used to
# plot larger red points on top of the sample data in the second layer.

# Pattern 1
# Both the `data` and `mapping` arguments are passed into the `ggplot()`
# call. Those arguments are omitted in the first `geom_point()` layer
# because they get passed along from the `ggplot()` call. Note that the
# second `geom_point()` layer re-uses the `x = group` aesthetic through
# that mechanism but overrides the y-position aesthetic.
ggplot(data = sample_df, mapping = aes(x = group, y = value)) +
  geom_point() +
  geom_point(
    mapping = aes(y = group_mean), data = group_means_df,
    colour = 'red', size = 3
  )


# Pattern 2
# Same plot as above, passing only the `data` argument into the `ggplot()`
# call. The `mapping` arguments are now required in each `geom_point()`
# layer because there is no `mapping` argument passed along from the
# `ggplot()` call.
ggplot(data = sample_df) +
  geom_point(mapping = aes(x = group, y = value)) +
  geom_point(
    mapping = aes(x = group, y = group_mean), data = group_means_df,
    colour = 'red', size = 3
  )


# Pattern 3
# Same plot as above, passing neither the `data` or `mapping` arguments
# into the `ggplot()` call. Both those arguments are now required in
# each `geom_point()` layer. This pattern can be particularly useful when
# creating more complex graphics with many layers using data from multiple
# data frames.
ggplot() +
  geom_point(mapping = aes(x = group, y = value), data = sample_df) +
  geom_point(
    mapping = aes(x = group, y = group_mean), data = group_means_df,
    colour = 'red', size = 3
  )

源代码：R/plot.R

相关用法

注：本文由纯净天空筛选整理自Hadley Wickham等大神的英文原创作品 Create a new ggplot。非经特殊声明，原始代码版权归原作者所有，本译文未经允许或授权，请勿转载或复制。