R ggplot2如何分离图例元素

Question

我将此示例数据以 CSV 形式读取到 R 数据框中，

"TEST","PREVIOUS","LAST","Interaction"
0.592,0.689,0.708,"Insertion"
0.702,0.796,0.836,"Insertion"
0.687,0.738,0.738,"Circular permutation and repetition"
0.727,0.675,0.734,"Insertion"
0.944,0.919,0.925,"Insertion and conformational variability"
0.862,0.847,0.847,"Conformational variability"
0.808,0.763,0.827,"Insertion"
0.981,0.983,0.983,"Conformational variability"
0.703,0.786,0.788,"Circular permutation and conformational variability"
0.691,0.732,0.732,"Insertion and conformational variability"
0.659,0.641,0.655,"Insertion"
0.6,0.602,0.676,"Insertion and conformational variability"
0.355,0.704,0.707,"Circular permutation and insertion"
0.436,0.716,0.739,"Circular permutation and insertion"
0.386,0.677,0.699,"Circular permutation and insertion"
0.976,0.979,0.979,"Conformational variability"
0.73,0.757,0.757,"Circular permutation"
0.492,0.918,0.918,"Circular permutation"
0.688,0.672,0.685,"Circular permutation and insertion"
0.79,0.799,0.799,"Circular permutation"
0.756,0.817,0.834,"Circular permutation and insertion"
0.677,0.668,0.677,"Insertion"
0.892,0.884,0.89,"Conformational variability"
0.604,0.752,0.759,"Insertion and conformational variability"
0.589,0.721,0.73,"Insertion and repetition"
0.916,0.927,0.927,"Conformational variability and repetition"
0.665,0.667,0.667,"Insertion"
0.62,0.684,0.717,"Insertion"
0.683,0.714,0.714,"Circular permutation and conformational variability"
0.55,0.75,0.788,"Insertion"
0.584,0.67,0.704,"Insertion and repetition"
0.635,0.684,0.684,"Circular permutation"
0.45,0.605,0.623,"Insertion and conformational variability"
0.53,0.611,0.611,"Insertion"
0.792,0.778,0.811,"Insertion and repetition"
0.524,0.595,0.631,"Insertion"
0.591,0.719,0.771,"Insertion and conformational variability"
0.633,0.719,0.719,"Insertion"
0.785,0.803,0.816,"Insertion and repetition"
0.738,0.723,0.731,"Insertion and repetition"

然后使用 ggplot 和以下代码绘制下图：

library(ggplot2)

fig = ggplot(dta, aes(x=LAST, y=TEST, fill=Interaction)) +
geom_point(
    dta[which(dta["LAST"] == dta["PREVIOUS"]),],
    mapping =aes(x=LAST,y=TEST),
    shape=21, size=3,stroke = 0.25, alpha=0.9)  +


geom_point(
    dta[which(dta["LAST"] != dta["PREVIOUS"]),],
    mapping =aes(x=LAST,y=TEST),
    shape=21, size=3, stroke = 0.25, alpha=0.9)  +


geom_point(
    dta[which(dta["LAST"] != dta["PREVIOUS"]),],
    mapping =aes(x=LAST,y=TEST),
    shape=3, size=1,stroke = 0.25)  +


xlim(0.4, 1.0) +
ylim(0.4, 1.0) +
theme(legend.position="top")+
     xlab("LAST") + ylab("TEST") +
     guides(fill = guide_legend(ncol=1, title=""))

p4b = p4b + coord_fixed()+guides(fill =guide_legend(ncol=2, title="Interactions", title.position="top",
                                                  title.theme = element_text(
                                                  size = 15,
                                                  face = "bold",
                                                  colour = "black",
                                                  margin=margin(t = 0, r = 0, b = 0, l = 5, unit = "pt")
                                                ) 
                                                 )
                                )
p4b

我获得了该图。问题是我想要的是一个单独的图例元素，代表“+”形状，没有颜色。最后，图例中应该有 10 个元素，9 个实际彩色圆形（但内部没有“+”）和 1 个单独的“+”。

我看到有些人在 guid_legend 部分中使用 override.aes，但我就是无法让它工作，而且我已经在这个问题上呆了太久了.. 。

Answer 1

我是从原来的帖子开始工作的，其中没有任何点与它们相关联的 + 符号。假设这些是由形状= 3的geom_point层绘制的，取自数据框dta的子集，我们可以这样做：

ggplot(dta, aes(TEST1, TEST, fill = Interaction)) +
  annotation_custom(grob = g3, xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf) +
  geom_abline(intercept = 0, slope = 1, color="black", size = 1.0) +
  geom_abline(intercept = c(-0.05, 0.05),  slope = 1, 
              color = "black", linetype = "dotted", size = 0.25, alpha = 0.5) +
  geom_abline(intercept = c(-0.1, 0.1), slope = 1, color = "black", 
              linetype = "dashed", size = 0.25, alpha = 0.5) +
  geom_point(shape = 21, size = 4.5, stroke = 0.25, alpha = 0.9) +
  geom_point(aes(TEST1, TEST, shape = "Positive"), inherit.aes = FALSE,
             size = 1.5, data = dta[sample(nrow(dta), 12),]) +
  scale_shape_manual(values = 3, name = "Special") +
  xlim(0.4, 1.0) +
  ylim(0.4, 1.0) +
  theme(legend.position="top",
        legend.key.height= unit(.4, 'cm'),
        plot.background = element_blank(),
        panel.background = element_rect(fill = "transparent", colour = "gray"),
        panel.border = element_rect(fill = "transparent", colour = "black"),
        axis.text = element_text(color = "black"),
        legend.background = element_blank(),
        legend.box.background = element_blank(),
        legend.key = element_blank(),
        legend.text = element_text(size = 9),
        axis.text.x = element_text(size = 12),
        axis.text.y = element_text(size = 12),
        axis.title.x = element_text(color = "black",size = 13, face = "bold"),
        axis.title.y = element_text(color = "black", size=15, face = "bold")
       ) +
     xlab("TEST1") + 
  ylab("TEST") +
  coord_fixed() +
  guides(fill = guide_legend(ncol = 2, 
                             title = "Interactions", 
                             title.position = "top",
                             title.theme = 
                               element_text(
                                  size = 15,
                                  face = "bold",
                                  colour = "black",
                                  margin = margin(t = 0, r = 0, b = 0, l = 5, 
                                                  unit = "pt")
                                                ) 
                              ),
         shape = guide_legend(title = "Special", 
                             title.position = "top",
                             title.theme = element_text(
                                  size = 15,
                                  face = "bold",
                                  colour = "black",
                                  margin = margin(t = 0, r = 0, b = 0, l = 5, 
                                                  unit = "pt")
                                                ) 
                              ))

注意 I也有过尝试根据有根据的猜测重建 g3 对象。