R：如何两个相关组合矩阵使用GGPLOT2

我有两个科尔矩阵，我想在1个情节相结合：对科尔矩阵1R：如何两个相关组合矩阵使用GGPLOT2

示例代码：

matrix_values <- c(-0.07, -0.03, 0.1, 0.11, 0.06, 0.16, 0.16, 0.13, 0.04, 0.06, 0.05, 0.04, 0.16, 0.07, 0.1, 0.08, 0.08, 0.17, 0.07, -0.13, 0.16, -0.07, 0.09, 0.07, -0.08, 0, 0.09, -0.02, 0.18, 0.09, 0.01, -0.1, -0.04, -0.12, -0.03, 0.03, 0.09, 0.09, 0.15, -0.01, 0.15, 0.09, 0.11, 0.09, 0.15, 0.19, -0.07, -0.04, 0, -0.12, NaN, -0.02, -0.11, 0.01, 0.1, -0.1, -0.1, 0.01, 0.04, 0.08, -0.02, -0.12, 0.09, -0.05, -0.07, -0.03, -0.19, -0.07, -0.16, -0.08, -0.05, -0.04, 0.03, -0.09, -0.09, -0.12, -0.07, 0.04, 0.07, 0.04, 0.02, -0.08, -0.03, -0.18, -0.02, 0.03, -0.06, 0.03, -0.07, 0.09, 0.04, -0.06, -0.1, -0.07, 0.1, 0.02, 0.06, -0.13, -0.14, -0.06, NaN, NaN, -0.07, -0.12, 0.02, -0.02, 0.01, 0.02, -0.01, -0.08, -0.03, -0.06, -0.05, -0.15, 0, -0.12, 0.13, -0.09, -0.05, 0.05, 0.08, -0.06, 0.16, 0.16, 0, 0.06, -0.05, -0.05, 0.14, -0.02, 0.12, 0.01, -0.07, -0.06, 0.07, 0.07, -0.13, 0.06, -0.05, -0.06, -0.15, -0.07, 0.11, 0.03, 0.1, 0.05, -0.12, 0.13, -0.1, 0.04, NaN, NaN, NaN, -0.03, -0.12, -0.02, 0.23, 0.13, 0.04, 0.01, 0.1, -0.01, 0.04, 0.03, -0.02, 0, -0.01, -0.08, -0.17, -0.05, 0, -0.07, -0.13, 0.1, -0.04, -0.01, 0.05, -0.03, -0.03, 0.13, -0.03, 0.01, 0.03, -0.03, 0.06, -0.01, -0.08, 0.05, 0.12, 0.09, 0.08, 0.07, -0.04, 0.09, 0.05, 0.1, 0.03, 0.05, 0.09, 0, NaN, NaN, NaN, NaN, 0.03, -0.03, 0.13, 0.14, 0.04, -0.03, 0.05, 0.14, 0.02, 0, -0.09, 0, 0, 0.01, -0.1, -0.14, 0, 0.02, 0.04, -0.07, -0.03, -0.07, -0.08, 0.1, 0.02, 0.18, 0.07, -0.16, 0.08, 0.03, -0.01, 0.03, -0.01, -0.07, 0.01, 0.1, 0.11, -0.11, 0.04, -0.08, -0.01, -0.03, -0.02, 0.09, 0.03, 0.13, NaN, NaN, NaN, NaN, NaN, -0.01, -0.05, 0.24, 0.02, 0, 0.11, 0.22, 0.22, 0.09, 0.06, 0.1, 0.09, 0.21, 0.16, 0.08, 0.08, 0.14, 0.05, 0.14, 0.15, -0.01, 0.05, 0.23, 0.13, 0.04, 0.06, 0.11, 0.05, 0.16, 0.03, 0.06, 0.01, -0.02, 0.23, -0.05, -0.09, 0.01, -0.02, 0.08, -0.07, 0.06, -0.01, -0.02, -0.03, 0.06, NaN, NaN, NaN, NaN, NaN, NaN, 0.05, -0.02, 0.08, -0.03, 0.02, -0.05, 0.13, 0.08, 0.08, 0.11, -0.04, -0.08, 0.03, 0.09, 0.1, -0.04, 0.12, 0.12, -0.06, 0.07, -0.09, 0.03, 0.03, -0.03, -0.02, 0.05, 0.04, -0.14, -0.05, 0.15, 0.06, -0.03, 0.04, -0.06, 0.21, 0.12, 0.2, -0.04, 0.05, 0.02, 0.14, 0, 0.12, 0.04, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.13, 0, 0.12, 0.13, 0.05, 0.03, 0.09, 0.13, -0.05, 0.1, 0.14, 0.05, 0.06, 0.11, 0.03, 0.09, 0.17, 0.04, 0.15, 0.03, 0.03, -0.1, 0.07, 0.01, 0.02, 0.04, -0.08, 0.06, 0.05, 0.14, 0.07, 0.03, 0, 0.14, 0.02, -0.01, 0.02, 0.13, 0.09, -0.16, 0.1, -0.06, -0.04, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.14, -0.05, 0.2, 0.05, -0.07, 0.1, 0.21, 0.14, -0.04, 0.01, 0.11, 0.1, 0.17, 0.21, 0.06, 0.09, 0.17, 0.17, 0.26, -0.04, 0.04, -0.01, 0.06, 0.14, -0.11, 0.05, 0.13, -0.05, 0.14, 0.06, 0.01, -0.05, 0.03, 0.04, 0.02, -0.08, -0.09, 0, -0.08, -0.21, -0.02, -0.03, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.16, -0.1, 0.03, 0.06, 0.03, 0.16, 0.07, 0.09, -0.05, 0.02, 0.02, 0.02, 0.15, 0.04, 0.11, 0.04, 0.03, 0.08, 0.1, 0.06, -0.09, -0.03, 0.25, 0.11, -0.12, -0.12, 0.07, 0.03, 0.12, 0.11, 0.07, -0.07, 0.1, 0.11, -0.08, -0.05, -0.1, 0.1, -0.04, 0.07, 0.07, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.06, -0.04, 0.19, 0.04, -0.04, 0.07, 0.09, 0.07, -0.04, 0.03, 0.06, 0.1, 0.01, 0, 0.16, -0.07, 0.12, 0.07, 0.11, 0, 0.02, 0.17, 0.19, 0.13, -0.15, -0.14, 0.26, 0.08, 0.02, 0.08, 0.17, -0.03, -0.02, 0.17, 0.03, 0.03, -0.1, 0.1, -0.02, -0.2, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.02, 0.15, -0.01, -0.02, -0.19, 0, 0.05, -0.08, -0.09, -0.15, 0.16, 0.12, 0.08, -0.03, 0.11, 0.09, 0.08, 0.06, 0.11, -0.07, 0.2, 0.05, 0.22, 0.05, -0.1, -0.07, -0.08, 0.07, 0.18, -0.06, 0.12, -0.06, -0.06, 0.09, -0.12, -0.15, -0.16, 0, -0.21, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.07, -0.1, 0.23, -0.08, 0.01, -0.02, 0.13, 0.13, -0.04, 0.14, 0.03, 0.14, 0.07, 0.15, -0.02, 0.01, 0.05, 0.03, 0, 0.15, -0.15, 0.1, 0.11, 0.17, 0, -0.06, 0.14, -0.14, 0.03, 0.16, -0.12, -0.15, -0.1, 0.17, 0.2, -0.13, -0.11, -0.11, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.02, 0, 0.13, 0.03, -0.04, 0.03, 0.06, -0.08, -0.11, -0.08, -0.09, 0.12, 0.1, -0.01, 0.04, -0.12, -0.1, 0.01, 0.09, 0.02, 0.04, -0.03, 0.04, 0.11, -0.11, -0.15, 0.07, -0.13, -0.05, 0.15, 0.02, -0.07, 0.12, 0, 0.06, -0.05, 0.09, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.25, -0.05, 0.29, -0.04, -0.06, 0.11, 0.16, 0.07, 0.05, 0.06, 0.12, 0.09, 0.22, 0.11, 0.17, 0.1, 0.19, 0.12, 0.17, 0.03, 0.03, 0.11, 0.19, 0.17, 0.02, 0.07, 0.27, -0.02, -0.05, 0.19, 0.16, 0, 0.11, 0.14, 0.04, 0.14, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.12, -0.08, 0.36, -0.08, 0.02, -0.03, -0.04, 0, -0.14, 0.02, -0.07, 0.05, 0.01, 0.03, -0.06, -0.03, 0.04, -0.05, 0.15, -0.03, -0.2, 0.03, 0.01, 0.1, 0.15, 0.21, 0.02, -0.2, -0.03, -0.01, -0.1, 0.02, 0.05, 0.1, -0.11, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.04, 0.08, 0.2, -0.06, 0.06, 0.12, 0.2, 0.12, 0.03, 0.06, 0.08, 0.12, 0.16, 0.11, 0.15, 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0.08, 0.01, -0.07, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.04, -0.13, 0.13, 0.15, 0.23, 0.23, 0.13, 0.1, 0.01, 0.04, 0.04, 0.08, 0.09, 0.08, 0.03, 0.03, 0.13, 0.14, 0.04, 0.01, 0.09, -0.03, 0.12, 0.01, -0.06, -0.11, 0.09, -0.13, 0.02, 0.17, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.07, 0.11, 0.09, -0.08, 0.01, -0.04, 0.05, 0.16, -0.03, 0.08, 0.02, 0.05, -0.11, 0.1, 0.01, -0.07, 0.05, 0, 0.05, 0.09, -0.22, -0.09, 0.05, -0.05, -0.05, -0.04, -0.02, -0.11, -0.09, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.24, 0.07, 0.05, 0.07, 0.11, -0.11, -0.08, -0.16, -0.13, -0.07, -0.03, 0.01, -0.06, -0.07, -0.01, -0.07, 0.04, 0.04, -0.1, -0.04, 0.06, 0.04, 0.16, 0.08, -0.05, -0.09, 0.13, 0.14, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 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-0.15, -0.22, -0.06, -0.07, 0.04, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.09, -0.04, 0.02, 0.14, 0.15, 0.13, 0.02, 0.07, -0.01, 0.08, 0.1, -0.13, 0.1, -0.02, 0.02, 0.01, 0.05, 0.07, -0.07, 0.01, 0.04, -0.13, 0.04, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.06, 0.14, 0.07, 0.15, 0.1, 0.09, 0.14, 0.09, 0.03, 0.04, 0.13, 0.02, 0.13, -0.02, 0.21, -0.03, 0.03, 0.12, -0.06, 0.08, 0.13, 0.01, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.04, -0.09, 0.08, 0.01, 0.04, 0.01, 0, 0.06, 0.04, 0.03, 0.09, -0.12, -0.06, -0.01, -0.09, -0.11, -0.07, -0.04, -0.05, -0.1, 0.01, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 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NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.21, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN) 

cor_matrix1 <- matrix(matrix_values, ncol = 51, nrow = 51) 
dat <- melt(cor_matrix1[-52, ]) 


    r <- ggplot(data = dat, aes(x = Var1, y = Var2)) + 
    geom_tile(aes(fill = value), color = "white") + 
    scale_fill_gradientn(values=c(1, .6, .5, .4, 0), colours=c("#770000", "red", "#ff8000", "#ffff00", "#ffffe5"))+ 
    theme(axis.title.x = element_blank(), 
    axis.title.y = element_blank(), 
    panel.background = element_blank())

样品用于更正件矩阵代码2：

cor_matrix2 <- matrix(matrix_values, ncol = 51, nrow = 51) 
dat <- melt(cor_matrix2[-52, ]) 
p <- ggplot(data = dat, aes(x = Var1, y = Var2)) + 
geom_tile(aes(fill = value), color = "white") + 
scale_fill_gradientn(values=c(1, .6, .5, .4, 0), colours=c("#00007f", "#1212b2", "cyan", "#b4b4cc", "white"))

1) combine the _r_ and _p_ matrices so that _r_ shows up in the lower diagonal and _p_ in the upper diagonnal; 2) have the main diagonal to be a *black* line (a line that separates the two matrices); 3)certain values can be highlighted

来源

2016-01-22 Nameis

您将需要在绘图之前合并数据，但对于同一件事物有多个图例并不容易在ggplot中完成。此外，为什么当他们有不同的范围/代表不同的实体时，他们需要进入同一个地块？ – Heroka

他们代表来自2个不同人群的数据，我想将它们并排放置以便于比较。我怎么能有多个传说？示例代码非常感谢！ – Nameis

另外，如何获得c值的黑色和固定（最小，最大）范围对角线？以及如何突出显示矩阵中的某些值（即黑色空心矩形）？ – Nameis

据我所知，ggplot不允许您在同一个绘图中使用多个色阶。它也使你的图表更难以解释。但是，你可以巧妙的搭配造型：

一些预处理来处理你的数据，我敢肯定你生成数据集时，你能避免一些这样的：

cor_matrix1 <- matrix(matrix_values, ncol = 51, nrow = 51) 
dat1 <- melt(cor_matrix1[-52, ]) 
cor_matrix2 <- matrix(matrix_values, ncol = 51, nrow = 51) 
dat2 <- melt(cor_matrix2[-52, ]) 
dat2$Var1 <- 52 - dat2$Var1 
dat2$Var2 <- 52 - dat2$Var2 
dat1$Class <- "A" 
dat2$Class <- "B" 
dat <- rbind(dat1,dat2) 
dat <- dat[!is.nan(dat$value),]

而不是使用geom_tile，尽量geom_point。这会给你使用形状的灵活性。（即一个额外的维度上进行细分数据）：

ggplot(data = dat, aes(x = Var1, y = Var2)) + 
    geom_point(size = 4, aes(color = value, pch = Class)) + 
    scale_color_gradientn(values=c(1, .6, .5, .4, 0), colours=c("#00007f", "#1212b2", "cyan", "#b4b4cc", "white")) + 
    geom_abline(slope = -1,intercept = 52 , size = 2) + 
    geom_rect(xmin = 30, xmax = 31, ymin = 30, ymax = 31, color = "red", fill = NA) + 
    theme(axis.title.x = element_blank(), 
      axis.title.y = element_blank(), 
      panel.background = element_blank())

其中给出：

几件事情会在这里：

的pch参数geom_point为每个人口设置不同的形状
geom_abline给你沿着图中心的黑线（你也可以用点来做这件事，但我认为这是更清晰
geom_rect参数创建红色矩形。根据需要调整分钟数/最大值，并根据需要放入。

此外，请注意，这里的负相关将变为白色（按照您定义的颜色比例）。我会发现这种误导。

来源

2016-01-22 22:26:54 Chris

R：如何两个相关组合矩阵使用GGPLOT2

回答

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