• Build a plot layer by layer
  • Data
  • Aesthetic mappings
  • Geoms
  • Stats
  • Position adjustments
• Scales and guides
  • 图例合并与分割
  • Legend key glyphs
  • Legene position

Build a plot layer by layer

ggplot2绘图首先构建一幅图，该图有着默认的数据集以及参数映射。

p <- ggplot(mpg, aes(displ, hwy))

然后在这之上添加图层。

p + geom_point()

每一个图层都由5个组件构成

data: 数据来源，大多数时候每个图层的数据来源都是设置为NULL, 使用在ggplot中的数据，不过你也可以为每一个图层提供它自己的数据来源。 mapping: 数据到美学的映射，在aes函数中设置 geom: 用于绘制每个观测的几何对象，该几何对象接收额外的参数，可以通过参数...或者geom_params传递给该几何对象。 stat: 数据的统计变换名称，用于进行一些statistical summary, 尤其是在直方图以及一些平滑拟合方面。和geom参数一样，统计对象也可以接收一些额外的参数，用于控制统计变换的细节。可以通过参数...或者stat_params传递给它。 position: 提供用于调整重叠对象的方法，包括jitter, stacking or dodging.

Data

• 如果需要在每一层指定不同的数据来源，你必须显式地写出geom_text(data = outlier, aes(lable = model))，因为其参数位置并不与ggplot函数中参数的位置一致。

Aesthetic mappings

• 需要注意地就是映射地继承，在ggplot中地映射会在每一层中被继承，如果只想对某一层进行美学映射，应该在那一层单独映射。

• 另一个需要注意的就是mapping和settings的区别。geom_point(aes(color = "red"), color = "red")。aes是用于设置映射的，在里面写color = red只是将一个常数映射到了color，并不会将点的颜色设置为红色，当然你可以设置映射规则为scale_color_identity，这样可以实现你的想法。如果你只是想简单的改变它的颜色，在外面这样写上color = red即可以。将映射设置为常数，有一个用处就是生成对应的图例 - 在你想展示同样的几何对象，但是具有不同的参数时，或者加上缺失观测的图例-数据中没有该观测，但是其确实存在，你想在图例中展示这种缺失。

Geoms

各种几何对象可以根据其需要的变量数进行划分。每一个几何对象都有它自己理解的美学参数，可以在其帮助文档中查到。

• Graphical primitives:
  • geom_blank(): display nothing. Most useful for adjusting axes limits using data.
  • geom_point(): points.
  • geom_path(): paths.
  • geom_ribbon(): ribbons, a path with vertical thickness.
  • geom_segment(): a line segment, specified by start and end position.
  • geom_rect(): rectangles.
  • geom_polygon(): filled polygons.
  • geom_text(): text.
• One variable:
  • Discrete:
    • geom_bar(): display distribution of discrete variable.
  • Continuous:
    • geom_histogram(): bin and count continuous variable, display with bars.
    • geom_density(): smoothed density estimate.
    • geom_dotplot(): stack individual points into a dot plot.
    • geom_freqpoly(): bin and count continuous variable, display with lines.
• Two variables:
  • Both continuous:
    • geom_point(): scatterplot.
    • geom_quantile(): smoothed quantile regression.
    • geom_rug(): marginal rug plots.
    • geom_smooth(): smoothed line of best fit.
    • geom_text(): text labels.
  • Show distribution:
    • geom_bin2d(): bin into rectangles and count.
    • geom_density2d(): smoothed 2d density estimate.
    • geom_hex(): bin into hexagons and count.
  • At least one discrete:
    • geom_count(): count number of point at distinct locations
    • geom_jitter(): randomly jitter overlapping points.
  • One continuous, one discrete:
    • geom_bar(stat = "identity"): a bar chart of precomputed summaries.
    • geom_boxplot(): boxplots.
    • geom_violin(): show density of values in each group.
  • One time, one continuous:
    • geom_area(): area plot.
    • geom_line(): line plot.
    • geom_step(): step plot.
  • Display uncertainty:
    • geom_crossbar(): vertical bar with center.
    • geom_errorbar(): error bars.
    • geom_linerange(): vertical line.
    • geom_pointrange(): vertical line with center.
  • Spatial:
    • geom_map(): fast version of geom_polygon() for map data.
• Three variables:
  • geom_contour(): contours.
  • geom_tile(): tile the plane with rectangles.
  • geom_raster(): fast version of geom_tile() for equal sized tiles.

同样的几何对象，可能有不同的定义方式，例如geom_tile用中心坐标x, y, 以及width, height定义矩形。geom_rect使用ymax, xmin, ymin, xmax定义矩形，而geom_polygon使用四个顶点的x,y坐标定义矩形。

Stats

每个geom背后都有一个默认的统计变换，统计变换接收输入的数据框，返回一个新的含有新的计算出的变量的数据框。一些几何图形，默认的使用这些新的变量作为美学的映射。为了显式的使用这些经过统计变换计算出的新的变量，在使用的时候，使用after_stat包裹住这些向量。通过stat_*函数的文档，你可以查询到统计变换后得到的向量名称。比如下面使用比例绘制直方图，而不是数目。

ggplot(diamonds, aes(price)) + 
  geom_histogram(aes(y = after_stat(density)), binwidth = 500)

• stat_bin(): geom_bar(), geom_freqpoly(), geom_histogram()
• stat_bin2d(): geom_bin2d()
• stat_bindot(): geom_dotplot()
• stat_binhex(): geom_hex()
• stat_boxplot(): geom_boxplot()
• stat_contour(): geom_contour()
• stat_quantile(): geom_quantile()
• stat_smooth(): geom_smooth()
• stat_sum(): geom_count()

Other stats can’t be created with a geom_ function:

• stat_ecdf(): compute a empirical cumulative distribution plot.
• stat_function(): compute y values from a function of x values.
• stat_summary(): summarise y values at distinct x values.
• stat_summary2d(), stat_summary_hex(): summarise binned values.
• stat_qq(): perform calculations for a quantile-quantile plot.
• stat_spoke(): convert angle and radius to position.
• stat_unique(): remove duplicated rows.

ggplot(mpg, aes(trans, cty)) + 
  geom_point() + 
  geom_point(stat = "summary", fun = "mean", colour = "red", size = 4)

Position adjustments

用于bar的位置调整

• position_stack(): stack overlapping bars (or areas) on top of each other.
• position_fill(): stack overlapping bars, scaling so the top is always at 1.
• position_dodge(): place overlapping bars (or boxplots) side-by-side.

用于点的位置调整

• position_nudge(): move points by a fixed offset.
• position_jitter(): add a little random noise to every position.
• position_jitterdodge() : dodge points within groups, then add a little random noise.

如果想要给位置对象传递参数只能通过位置对象传递

ggplot(mpg, aes(displ, hwy)) + 
  geom_point(position = position_jitter(width = 0.05, height = 0.5))

Scales and guides

每一种映射都对应这一种图例，对应映射图例地修改，可以通过对应地图例函数修改，对应关系如下

• continuous scales for colour/fill aesthetics guide_colourbar
• binned scales for colour/fill aesthetics guide_coloursteps
• position scales (continuous, binned and discrete) guide_axis
• discrete scales (except position scales) guide_legend
• binned scales (except position/colour/fill scales) guide_bins

图例合并与分割

对于每一层的每一个变量的美学映射，都默认生成一个图例，ggplot会尽可能地合并这些图例，以展示这些映射地变量。合并同一个变量不同美学映射前提是这些映射有同样地名称。如果某一层对该几何对象设置了settings，而你也想为该settings生成图例，可以设置show.legend = TRUE生成图例，当然你也可以用该方式不生成某一层地映射变量地图例，设置show.legene = FALSE即可。

ggplot(toy, aes(up, up)) + 
  geom_point(size = 4, colour = "grey20", show.legend = TRUE) +
  geom_point(aes(colour = txt), size = 2) 

当多个变量映射到一个几何对象地同一个美学参数，就需要进行图例地分割，使用R包ggnewscale可以实现该功能

base <- ggplot(mpg, aes(displ, hwy)) + 
  geom_point(aes(colour = factor(year)), size = 5) + 
  scale_colour_brewer("year", type = "qual", palette = 5) 

base
base + 
  ggnewscale::new_scale_colour() + 
  geom_point(aes(colour = cyl == 4), size = 1, fill = NA) + 
  scale_colour_manual("4 cylinder", values = c("grey60", "black"))

Legend key glyphs

使用key_glyph参数可以改变图例中每一层地图例多边形

base <- ggplot(economics, aes(date, psavert, color = "savings"))

base + geom_line()
base + geom_line(key_glyph = "timeseries")

Legene position

legend.position 控制图例的位置，在四个角落top,bottom, left, right。

如果panel内有多余空间，你可能会想把图例放在其中，你可以给legend.position设定一个相对位置来实现

Alternatively, if there’s a lot of blank space in your plot you might want to place the legend inside the plot. You can do this by setting legend.position to a numeric vector of length two. The numbers represent a relative location in the panel area: c(0, 1) is the top-left corner and c(1, 0) is the bottom-right corner. You control which corner of the legend the legend.position refers to with legend.justification, which is specified in a similar way. Unfortunately positioning the legend exactly where you want it requires a lot of trial and error.

base <- ggplot(toy, aes(up, up)) + 
  geom_point(aes(colour = txt), size = 3)

base + 
  theme(
    legend.position = c(0, 1), 
    legend.justification = c(0, 1)
  )

base + 
  theme(
    legend.position = c(0.5, 0.5), 
    legend.justification = c(0.5, 0.5)
  )

• legend.direction: layout of items in legends (“horizontal” or “vertical”).

• legend.box: arrangement of multiple legends (“horizontal” or “vertical”).

• legend.box.just: justification of each legend within the overall bounding box, when there are multiple legends (“top”, “bottom”, “left”, or “right”).