大量地块的布置并与r中的线连接 [英] Arrangement of large number of plots and connect with lines in r

问题描述

我有大量的小块地需要放置在较大的块画布中，并将小块布置成直线并将它们连接起来.一个小例子如下:

I have a large number of small plots need to placed in a bigger plot canvase and arrange small plots into and connect them with lines. A small example will look like this:

A至L是独立曲线.给出了它们放置的坐标.

A to L are independent plots. The cordinate of their placement is given.

图网格坐标:PlotgridX和plotgridY可以决定何时需要将小图居中

plot grid coordinates: PlotgridX and plotgridY can decide when the small plot need to be centered

    plotcord <- data.frame (
plotname = c("A", "B", "C", "D",    "E",    "F",   "G", "H", "I", "J", "K", "L"),
plotgridX = c( 1.5, 2,   5,   5.5,   1.75,  5.25,  8   , 1 ,  2,   3.5,  6,  7.5),
 plotgridY = c( 3,  3,    3,    3,     2 ,    2,    2,   2  , 1,   1,   1,   1))


   plotname plotgridX plotgridY
1         A      1.50         3
2         B      2.00         3
3         C      5.00         3
4         D      5.50         3
5         E      1.75         2
6         F      5.25         2
7         G      8.00         2
8         H      1.00         2
9         I      2.00         1
10        J      3.50         1
11        K      6.00         1
12        L      7.50         1

连接线由以下数据框决定:

The connecting lines is decided by the following data frame:

connectd <- data.frame (id = c(  "E",    "F", "I", "J", "K", "L"),
                        parent1 = c("A",  "C", "H", "E" ,"E", "F"),
                      parent2 = c("B",  "D",  "E", "F", "F", "G"))
connectd
  id parent1 parent2
1  E       A       B
2  F       C       D
3  I       H       E
4  J       E       F
5  K       E       F
6  L       F       G

例如，在这里，图E应该同时连接到其上级1"A"和上级2"B"上，同时应连接"A"，"B"以使其成为"T形"连接.其他ID同样如此.

For example, here figure E should be connected to its parent1 "A" and parent 2 "B" figures at the same time "A", "B" should be connected to make it "T shaped" connection. Similarly for the other ids.

尽管我还有其他细节要在每个子图中绘制，就像概念证明一样，我想在每个具有n1和n2名称的图中绘制一个矩形，以绘制如下图:

Although I have other details to plot in each subplot, just as proof of concept I could like to plot one rectangles withing each plots with names n1 and n2, to make a plot like the following:

推荐答案

我正在写这个答案，部分是为了后代，部分是因为我一直在为一些正在尝试的其他人编写这样的函数进入R中的自定义可视化.

I'm writing this answer up, partly for posterity, and partly because I have been meaning to write up some functions like this for some others who have been trying to get into custom visualizations in R.

背景

在R中，许多人正确地抛弃了基本绘图功能，而开始求助于更灵活的包装程序"lattice"和"ggplot2".这些功能强大的工具可通过在单个绘图上应用逻辑层来快速浏览数据.然后，程序包将处理所有图层，并生成适当布置的一个绘图窗口.这些软件包很棒，我建议每个R用户至少学习其中一个.

In R, many people rightly leave behind the base plotting functions and begin to turn to the more flexible wrapper packages, 'lattice' and 'ggplot2'. These are powerful tools for rapidly exploring your data by applying layers of logic on top of a single plot. The packages then process all of the layers and produce one window of plots, arranged appropriately. These packages are wonderful, and I recommend every R user learn at least one of them.

不过，一个警告是，"lattice"和"ggplot2"软件包实际上更多地用于数据探索，而不是智能数据可视化.在创建自定义数据可视化时，这些程序包会为您做出过多的决定，因为这就是包装程序的目的:从您手中掌握一些决定.

One caveat, though, is that the 'lattice' and 'ggplot2' packages are really more for data exploration than intelligent data visualization. When creating a custom data visualization, these packages make too many decisions for you, because that's what a wrapper is for: taking some decisions out of your hands.

自定义可视化?输入网格"

基本的网格"程序包具有极高的绘图灵活性，部分原因是它扩展了基本绘图功能的功能，而不是包装它们.通过网格"功能，我们获得了使用各种不同的单元来放置和调整尺寸来创建视觉对象的能力，并且(这非常重要)我们获得了为对象的锚点使用对齐的能力.如果您想学习，Paul Murrell的书"R Graphics"是一本很好的参考资料.它的副本坐在我的桌子上.

The base 'grid' package is the ultimate in drawing flexibility, partly because it extends the functionality of the base plotting functions, rather than wrapping them. With 'grid' functions, we gain the ability to create visual objects using a variety of different units for placement and sizing, and (this is really important) we gain the ability to use justifications for our objects' anchors. Paul Murrell's book, "R Graphics," is an excellent resource if you're wanting to learn. A copy of it sits out on my desk.

如果您曾经使用过矢量图形绘图程序(例如Illustrator或Inkscape)，那么当我提到理由时，您可能已经知道我在说什么.这是通过引用其他项目的位置来排列项目的能力.我会谈论更多，但是我可以整天谈论它.让我们继续该过程.

If you've ever used a vector graphics drawing program (like Illustrator or Inkscape), you probably already know what I'm talking about when I mention justifications. This is the ability to line items up by referencing the locations of other items. I'd talk about this more, but I could talk about it all day. Let's move on to the process.

过程

现在，我应该先说一下，编写函数库花了大约两个小时，而编写演示代码则花了大约5分钟.将来，我将把函数库用作培训工具，任何人都可以随意使用/修改它.

Now, I should preface this by saying it took me about two hours to write the function library, and about 5 minutes to write the demo code. I will be using the function library in the future as a training tool, and anyone can feel free to use/modify it.

网格"过程分为三个基本步骤:

The 'grid' process works in three basic steps:

1. 创建视口
2. 绘制一些对象
3. 弹出视口

在创建视口时，我们使用'pushViewport'推动'viewport'对象，如下所示:

In making a viewport, we use 'pushViewport' to push the 'viewport' object, something like so:

pushViewport(viewport(x=0, y=1, xscale=c(1, 10), yscale=c(0, 100), width=0.25, height=0.25, default.units="npc", just=c("left","bottom"), clip="off"))

基本视口具有一组"npc"单位，其中x从0到1，从左到右，而y从0到1，从下到上.这意味着原点在左下角.上面的视口被创建为图的左下角的四分之一.但是，当我们指定"xscale"和"yscale"时，可以在绘制对象时引用单位"native".这意味着我们可以使用本机"单位绘制数据，而在绘制诸如轴和标签之类的东西时可以使用"npc"单位.

The basic viewport has an "npc" set of units where x goes from 0 to 1, left to right, and y goes 0 to 1, bottom to top. This means the origin is in the lower left-hand corner. The above viewport is created as one quarter of the plot in the lower left-hand corner. When we specify an "xscale" and "yscale", however, we gain the ability to reference the units "native" when drawing objects. This means we can use "native" units for drawing data and use "npc" units when drawing things like axes and labels.

在绘制对象时，我们使用"grid.lines"，"grid.polygon"，"grid.points"，"grid.circle"等功能.我做过的每一次可视化都使用了这些对象.通过手动指定这些对象来绘制数据时，您将获得大量控制权.填写折线图是增加功能的最明显示例之一.填充区域只是一个多边形，该多边形的点由数据指定，并添加了两个锚点.我用它来突出显示折线图的区域，或者使在同一图表上读取多条线更容易.

When drawing objects, we use functions like 'grid.lines', 'grid.polygon', 'grid.points', 'grid.circle', and so on. Every visualization I have ever made has used these objects. When you draw the data by specifying these objects by hand, you gain an enormous amount of control. Filling in a line chart is one of the most obvious examples of added capability. A filled in area is just a polygon with points of the polygon specified by data and with two anchor points added. I use this to highlight areas of a line chart or to make it easier to read multiple lines on the same chart.

您还可以发挥创意，例如，创建不是矩形的条形图，或者以更复杂的方式组合多个图.我和其他一些人最近运行了一个科幻主题的步行游戏，我们使用了一个自定义图表(由网格"制成)来显示我们的最终表现.该图表将幸存者"团队的天数合并为一个时间轴，以条形图的形式显示每天玩家与敌人的步数，并以实线图显示每天的玩家和敌人的累积步数.使用'lattice'或'ggplot2'包来创建可比的视觉效果会很困难.

You can also get creative, for instance, creating bars that aren't rectangles, or combining multiple plots in a more sophisticated way. I and some others recently ran a sci-fi themed walking game, and we used a custom chart (made with 'grid') to display our final performance. The chart combines the number of days on the "survivor" team as a time axis, displays player vs. enemy steps per day as a bar chart, and displays cumulative player and enemy steps per day as a filled line chart. I would have been hard-pressed to create a comparable visual using the 'lattice' or 'ggplot2' packages.

这里是其中一张图表的示例(不含真实玩家的名字)，以让您了解网格"视觉效果的灵活性:

Here is a sample of one of the charts (sans the real-life player name), to give an idea of just how flexible 'grid' visuals can be:

问题的概念证明

现在要专门解决OP提出的问题.在问题中，OP暗示他/她将在每个区域内绘制图表.当使用预构建的绘图程序包时，这可能会变得棘手，因为大多数绘图功能都会覆盖您已经设置的任何绘图规范.相反，使用诸如基本网格"功能之类的方法来指定绘图区域，然后在视口中绘制必要的数据对象，将更为可靠.

Now to specifically address the question posed by the OP. In the question, the OP implies that s/he will be plotting charts inside each area. This can get tricky when using pre-built plotting packages, because most plotting functions will overwrite any plot specifications you have already set up. Instead, it is more reliable to use something like the base 'grid' functions to specify plotting areas and then draw the necessary data objects within the viewports.

为了避免工作太辛苦，我首先编写了一个自定义函数库，该库可以设置各种图表参数并为我绘制每种图表.我不喜欢调试代码，因此函数是我逐步进行工作的方式.每次我正确获得一段代码后，都会将其放入函数中以供以后使用.

To keep from working too hard, I first wrote a custom function library that sets my various chart parameters and draws each type of chart for me. I don't like debugging code, so functions are the way I work through things in pieces. Each time I get a piece of code right, I throw it into a function for later use.

代码可能看起来有些复杂，但请记住三个网格"步骤:推入视口，绘制，弹出视口.这就是每个功能的作用.为了演示这项工作，我制作了四个不同的绘图功能:折线图，散点图，直方图和OP所建议的箱形图.每个函数都足够灵活，可以在每个图表中容纳多组数据值，设置Alpha值以进行补偿，并使我们能够看到绘制在彼此之上的值.

The code may look a little complicated, but remember the three 'grid' steps: push viewport, draw, pop viewport. This is what each function is doing. To demo the work, I made four different drawing functions: filled line chart, scatter plot, histogram, and a box drawing as suggested by the OP. Each function is flexible enough to accommodate multiple sets of data values in each chart, setting the alpha values to compensate and allowing us to see the values plotted over top of each other.

在这种情况下，您只能使函数具有所需的灵活性，因此我确实采取了一种捷径，并从演示中的大量代码中提取了它们，这些代码中进行了大量假设.不过，我仍然使用逻辑驱动的代码进行绘制，以演示如何使用简单的逻辑绘制更复杂的对象.

In a case like this, you only make your functions as flexible as you need to, so I did take one shortcut on the lines and drew them from a little bit of code in the demo that made a LOT of assumptions. I still drew it with logic-driven code, though, to demo how to draw more complex objects with simple logic.

这是演示代码的结果，它使用一些内置的R数据集获取简单数据(EuStockMarkets，nottem，sunspots.month):

Here is the result of the demo code, using some built-in R datasets for easy data (EuStockMarkets, nottem, sunspots.month):

自定义功能库:

library(grid)

# Specify general chart options.
chart_Fill = "lemonchiffon"
chart_Col = "snow3"
space_Background = "white"
title_CEX = 0.8
axis_CEX = 0.6
chart_Width <- 3/3
chart_Height <- 2/5

# Function to initialize a plotting area.
init_Plot <- function(
    .df,
    .x_Loc, 
    .y_Loc, 
    .justify, 
    .width, 
    .height
    ){

    # Initialize plotting area to fit data.
    # We have to turn off clipping to make it
    # easy to plot the labels around the plot.
    pushViewport(viewport(xscale=c(min(.df[,1]), max(.df[,1])), yscale=c(min(0,min(.df[,-1])), max(.df[,-1])), x=.x_Loc, y=.y_Loc, width=.width, height=.height, just=.justify, clip="off", default.units="native"))

    # Color behind text.
    grid.rect(x=0, y=0, width=unit(axis_CEX, "lines"), height=1, default.units="npc", just=c("right", "bottom"), gp=gpar(fill=space_Background, col=space_Background))
    grid.rect(x=0, y=1, width=1, height=unit(title_CEX, "lines"), default.units="npc", just=c("left", "bottom"), gp=gpar(fill=space_Background, col=space_Background))

    # Color in the space.
    grid.rect(gp=gpar(fill=chart_Fill, col=chart_Col))
}

# Function to finalize and label a plotting area.
finalize_Plot <- function(
    .df, 
    .plot_Title
    ){

    # Label plot using the internal reference
    # system, instead of the parent window, so
    # we always have perfect placement.
    grid.text(.plot_Title, x=0.5, y=1.05, just=c("center","bottom"), rot=0, default.units="npc", gp=gpar(cex=title_CEX))
    grid.text(paste(names(.df)[-1], collapse=" & "), x=-0.05, y=0.5, just=c("center","bottom"), rot=90, default.units="npc", gp=gpar(cex=axis_CEX))
    grid.text(names(.df)[1], x=0.5, y=-0.05, just=c("center","top"), rot=0, default.units="npc", gp=gpar(cex=axis_CEX))

    # Finalize plotting area.
    popViewport()
}

# Function to plot a filled line chart of
# the data in a data frame.  The first column
# of the data frame is assumed to be the
# plotting index, with each column being a
# set of y-data to plot.  All data is assumed
# to be numeric.
plot_Line_Chart <- function(
    .df,
    .x_Loc,
    .y_Loc,
    .justify,
    .width,
    .height,
    .colors,
    .plot_Title
    ){

    # Initialize plot.
    init_Plot(.df, .x_Loc, .y_Loc, .justify, .width, .height)

    # Calculate what value to use as the
    # return for the polygons.
    y_Axis_Min <- min(0, min(.df[,-1]))

    # Plot each set of data as a polygon,
    # so we can fill it in with color to
    # make it easier to read.
    for (i in 2:ncol(.df)){
        grid.polygon(x=c(min(.df[,1]),.df[,1], max(.df[,1])), y=c(y_Axis_Min,.df[,i], y_Axis_Min), default.units="native", gp=gpar(fill=.colors[i-1], col=.colors[i-1], alpha=1/ncol(.df)))
    }

    # Draw plot axes.
    grid.lines(x=0, y=c(0,1), default.units="npc")
    grid.lines(x=c(0,1), y=0, default.units="npc")

    # Finalize plot.
    finalize_Plot(.df, .plot_Title)

}

# Function to plot a scatterplot of
# the data in a data frame.  The
# assumptions are the same as 'plot_Line_Chart'.
plot_Scatterplot <- function(
    .df,
    .x_Loc,
    .y_Loc,
    .justify,
    .width,
    .height,
    .colors,
    .plot_Title
    ){

    # Initialize plot.
    init_Plot(.df, .x_Loc, .y_Loc, .justify, .width, .height)

    # Plot each set of data as colored points.
    for (i in 2:ncol(.df)){
        grid.points(x=.df[,1], y=.df[,i], pch=19, size=unit(1, "native"), default.units="native", gp=gpar(col=.colors[i-1], alpha=1/ncol(.df)))
    }

    # Draw plot axes.
    grid.lines(x=0, y=c(0,1), default.units="npc")
    grid.lines(x=c(0,1), y=0, default.units="npc")

    # Finalize plot.
    finalize_Plot(.df, .plot_Title)

}

# Function to plot a histogram of
# all the columns in a data frame,
# except the first, which is assumed to
# be an index.
plot_Histogram <- function(
    .df,
    .x_Loc,
    .y_Loc,
    .justify,
    .width,
    .height,
    .colors,
    .plot_Title,
    ...
    ){

    # Create a list containing the histogram
    # data for each data column and calculate
    # data ranges.  Any extra parameters
    # specified will pass to the 'hist' function.
    hist_Data <- list()
    hist_Count_Range <- c(0,NA)
    hist_Breaks_Range <- c(NA,NA)
    for (i in 2:ncol(.df)){
        hist_Data[[i]] <- hist(.df[,i], plot=FALSE, ...)
        hist_Count_Range[2] <- max(max(hist_Data[[i]]$counts), hist_Count_Range[2], na.rm=TRUE)
        hist_Breaks_Range <- c(min(min(hist_Data[[i]]$breaks), hist_Breaks_Range[1], na.rm=TRUE), max(max(hist_Data[[i]]$breaks), hist_Breaks_Range[2], na.rm=TRUE))
    }


    # Initialize plotting area to fit data.
    # We are doing this in a custom way to
    # allow more flexibility than built into
    # the 'init_Plot' function.
    # We have to turn off clipping to make it
    # easy to plot the labels around the plot.
    pushViewport(viewport(xscale=hist_Breaks_Range, yscale=hist_Count_Range, x=.x_Loc, y=.y_Loc, width=.width, height=.height, just=.justify, clip="off", default.units="native"))

    # Color behind text.
    grid.rect(x=0, y=0, width=unit(axis_CEX, "lines"), height=1, default.units="npc", just=c("right", "bottom"), gp=gpar(fill=space_Background, col=space_Background))
    grid.rect(x=0, y=1, width=1, height=unit(title_CEX, "lines"), default.units="npc", just=c("left", "bottom"), gp=gpar(fill=space_Background, col=space_Background))

    # Color in the space.
    grid.rect(gp=gpar(fill=chart_Fill, col=chart_Col))


    # Draw x axis.
    grid.lines(x=c(0,1), y=0, default.units="npc")

    # Plot each set of data as a histogram.
    for (i in 2:ncol(.df)){
        grid.rect(x=hist_Data[[i]]$mids, y=0, width=diff(hist_Data[[i]]$mids[1:2]), height=hist_Data[[i]]$counts, default.units="native", just=c("center","bottom"), gp=gpar(fill=.colors[i-1], col=.colors[i-1], alpha=1/ncol(.df)))
    }

    # Label plot using the internal reference
    # system, instead of the parent window, so
    # we always have perfect placement.
    grid.text(.plot_Title, x=0.5, y=1.05, just=c("center","bottom"), rot=0, default.units="npc", gp=gpar(cex=title_CEX))
    grid.text(paste(names(.df)[-1], collapse=" & "), x=-0.05, y=0.5, just=c("center","bottom"), rot=90, default.units="npc", gp=gpar(cex=axis_CEX))

    # Finalize plotting area.
    popViewport()
}

draw_Sample_Box <- function(
    .x_Loc,
    .y_Loc,
    .x_Scale,
    .y_Scale,
    .justify,
    .width,
    .height,
    .colors,
    .box_X,
    .box_Y,
    .plot_Title
    ){

    pushViewport(viewport(xscale=.x_Scale, yscale=.y_Scale, x=.x_Loc, y=.y_Loc, width=chart_Width, height=chart_Height, just=.justify, clip="off", default.units="native"))

    # Color behind text.
    grid.rect(x=0, y=1, width=1, height=unit(title_CEX, "lines"), default.units="npc", just=c("left", "bottom"), gp=gpar(fill=space_Background, col=space_Background))

    # Color in the space.
    grid.rect(gp=gpar(fill=chart_Fill, col=chart_Col))

    # Label plot.
    grid.text(.plot_Title, x=0.5, y=1.05, just=c("center","bottom"), rot=0, default.units="npc", gp=gpar(cex=title_CEX))

    # Draw box and label points.
    grid.polygon(x=.box_X, y=.box_Y, default.units="native", gp=gpar(fill=.colors[1], col=.colors[2]))
    grid.text(paste(.plot_Title, 1, sep=""), x=min(.box_X), y=min(.box_Y), default.units="native", just=c("right","top"), gp=gpar(cex=0.5))
    grid.text(paste(.plot_Title, 2, sep=""), x=max(.box_X), y=min(.box_Y), default.units="native", just=c("left","top"), gp=gpar(cex=0.5))

    # Finalize plot.
    popViewport()
}

---

演示代码:

# Draw twelve independent charts as
# a demo and connect with lines similar
# to a heiritage chart.
grid.newpage()

# Initialize a viewport to make our locations
# easier to map.
pushViewport(viewport(x=0, y=0, width=1, height=1, just=c("left","bottom"), xscale=c(0,10), yscale=c(0,4)))

# Color background of overall plot.
grid.rect(gp=gpar(fill=space_Background, col=space_Background))

# Store plot locations for convenience.
plot_Loc <- data.frame(x=c(2,4,6,8,1,3,7,9,2,4,6,8), y=c(3,3,3,3,2,2,2,2,1,1,1,1))

# Draw connecting lines.
connections <- data.frame(a=c(1, 3, 5, 6, 7, 1, 3, 5, 7, 6), b=c(2, 4, 6, 7, 8, 2, 4, 6, 8, 7), c=c(NA, NA, NA, NA, NA, 6, 7, 9, 12, 10), d=c(NA, NA, NA, NA, NA, NA, NA, NA, NA, 11))
for (i in 1:nrow(connections)){
    if (is.na(connections$c[i])){
        grid.lines(x=plot_Loc$x[unlist(connections[i,1:2])], y=plot_Loc$y[unlist(connections[i,1:2])], default.units="native")
    } else if (is.na(connections$d[i])) {
        grid.lines(x=median(plot_Loc$x[unlist(connections[i,1:2])]), y=plot_Loc$y[unlist(connections[i,2:3])], default.units="native")
    } else {
        grid.lines(x=median(plot_Loc$x[unlist(connections[i,1:2])]), y=c(plot_Loc$y[connections[i,2]], median(plot_Loc$y[unlist(connections[i,2:3])])), default.units="native")
        grid.lines(x=plot_Loc$x[unlist(connections[i,3:4])], y=median(plot_Loc$y[unlist(connections[i,2:3])]), default.units="native")
        grid.lines(x=plot_Loc$x[connections[i,3]], y=c(median(plot_Loc$y[unlist(connections[i,2:3])]), plot_Loc$y[connections[i,3]]), default.units="native")
        grid.lines(x=plot_Loc$x[connections[i,4]], y=c(median(plot_Loc$y[unlist(connections[i,2:3])]), plot_Loc$y[connections[i,4]]), default.units="native")
    }
}


# Draw four independent line charts.
p <- 1
plot_Line_Chart(data.frame(time=1:1860, EuStockMarkets)[1:3], .x_Loc=plot_Loc$x[p], .y_Loc=plot_Loc$y[p], .just=c("center","center"), .width=chart_Width, .height=chart_Height, c("dodgerblue", "deeppink"), "EU Stocks")
p <- 2
plot_Line_Chart(data.frame(time=1:1860, EuStockMarkets)[c(1,4,5)], .x_Loc=plot_Loc$x[p], .y_Loc=plot_Loc$y[p], .just=c("center","center"), .width=chart_Width, .height=chart_Height, c("green", "purple"), "EU Stocks")
p <- 3
plot_Line_Chart(data.frame(time=1:(12*20), sunspots=sunspot.month[(171*12+1):(171*12+12*20)]), .x_Loc=plot_Loc$x[p], .y_Loc=plot_Loc$y[p], .just=c("center","center"), .width=chart_Width, .height=chart_Height, c("darkgoldenrod"), "Sunspots")
p <- 4
plot_Line_Chart(data.frame(time=1:(12*20), temp=nottem), .x_Loc=plot_Loc$x[p], .y_Loc=plot_Loc$y[p], .just=c("center","center"), .width=chart_Width, .height=chart_Height, c("red"), "Nottem")

# Draw four independent scatterplots.
p <- 5
plot_Scatterplot(data.frame(time=1:(1860 + 1 - 1000), DAX=rowMeans(embed(EuStockMarkets[,1], 1000)), FTSE=rowMeans(embed(EuStockMarkets[,4], 1000))), .x_Loc=plot_Loc$x[p], .y_Loc=plot_Loc$y[p], .just=c("center","center"), .width=chart_Width, .height=chart_Height, c("deeppink", "purple"), "Smooth")
p <- 6
plot_Scatterplot(data.frame(time=1:1860, EuStockMarkets)[c(1,2,5)], .x_Loc=plot_Loc$x[p], .y_Loc=plot_Loc$y[p], .just=c("center","center"), .width=chart_Width, .height=chart_Height, c("deeppink", "purple"), "EU Stocks")
p <- 9
plot_Scatterplot(data.frame(time=1:(1860 + 1 - 20), DAX=rowMeans(embed(EuStockMarkets[,1], 20)), FTSE=rowMeans(embed(EuStockMarkets[,4], 20))), .x_Loc=plot_Loc$x[p], .y_Loc=plot_Loc$y[p], .just=c("center","center"), .width=chart_Width, .height=chart_Height, c("deeppink", "purple"), "Smooth*20")
p <- 10
plot_Scatterplot(data.frame(time=1:(1860 + 1 - 100), DAX=rowMeans(embed(EuStockMarkets[,1], 100)), FTSE=rowMeans(embed(EuStockMarkets[,4], 100))), .x_Loc=plot_Loc$x[p], .y_Loc=plot_Loc$y[p], .just=c("center","center"), .width=chart_Width, .height=chart_Height, c("deeppink", "purple"), "Smooth*100")


# Draw two independent histograms.
p <- 7
plot_Histogram(data.frame(time=1:(12*20), sunspots=sunspot.month[(171*12+1):(171*12+12*20)]), .x_Loc=plot_Loc$x[p], .y_Loc=plot_Loc$y[p], .just=c("center","center"), .width=chart_Width, .height=chart_Height, c("darkgoldenrod"), "Sunspots", breaks=6)
p <- 8
plot_Histogram(data.frame(time=1:(12*20), temp=nottem), .x_Loc=plot_Loc$x[p], .y_Loc=plot_Loc$y[p], .just=c("center","center"), .width=chart_Width, .height=chart_Height, c("red"), "Nottem", breaks=6)

# Draw sample objects in two charts spaces.
p <- 11
draw_Sample_Box(.x_Loc=plot_Loc$x[p], .y_Loc=plot_Loc$y[p], .x_Scale=c(0,10), .y_Scale=c(-10,0), .justify=c("center","center"), .width=chart_Width, .height=chart_Height, .colors=c("dodgerblue","blue"), .box_X=c(4,6,6,4), .box_Y=c(-4,-4,-5,-5), .plot_Title="K")
p <- 12
draw_Sample_Box(.x_Loc=plot_Loc$x[p], .y_Loc=plot_Loc$y[p], .x_Scale=c(-1,1), .y_Scale=c(0,1), .justify=c("center","center"), .width=chart_Width, .height=chart_Height, .colors=c("dodgerblue","blue"), .box_X=c(-0.5,0,0,-0.5), .box_Y=c(0.8,0.8,0.7,0.7), .plot_Title="L")

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