基于python flask的疾病数据采集与可视化大屏，实现关联规则算法的治疗方法分析

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载入R包

library('networkdata')
library('ggraph')
library('igraph')
library('graphlayouts')
library('ggforce')

分区域网络图

xy <- layout_as_multilevel(multilvl_ex, type = "all", alpha = 25, beta = 45)
ggraph(multilvl_ex, "manual", x = xy[, 1], y = xy[, 2]) +
  geom_edge_link0(
    aes(filter = (node1.lvl == 1 & node2.lvl == 1)),
    edge_colour = "firebrick3",
    alpha = 0.5,
    edge_linewidth = 0.3
  ) +
  geom_edge_link0(
    aes(filter = (node1.lvl != node2.lvl)),
    alpha = 0.3,
    edge_linewidth = 0.1,
    edge_colour = "black"
  ) +
  geom_edge_link0(
    aes(filter = (node1.lvl == 2 &
                    node2.lvl == 2)),
    edge_colour = "goldenrod3",
    edge_linewidth = 0.3,
    alpha = 0.5
  ) +
  geom_node_point(aes(shape = as.factor(lvl)), fill = "grey25", size = 3) +
  scale_shape_manual(values = c(21, 22)) +
  theme_graph() +
  coord_cartesian(clip = "off", expand = TRUE) +
  theme(legend.position = "none")

为两个层级分别设置布局

xy <- layout_as_multilevel(multilvl_ex,
                           type = "separate",
                           FUN1 = layout_as_backbone,
                           FUN2 = layout_with_stress,
                           alpha = 25, beta = 45
)
​
cols2 <- c(
  "#3A5FCD", "#CD00CD", "#EE30A7", "#EE6363",
  "#CD2626", "#458B00", "#EEB422", "#EE7600")
​
ggraph(multilvl_ex, "manual", x = xy[, 1], y = xy[, 2]) +
  geom_edge_link0(aes(
    filter = (node1.lvl == 1 & node2.lvl == 1),
    edge_colour = col
  ),
  alpha = 0.5, edge_linewidth = 0.3
  ) +
  geom_edge_link0(
    aes(filter = (node1.lvl != node2.lvl)),
    alpha = 0.3,
    edge_linewidth = 0.1,
    edge_colour = "black"
  ) +
  geom_edge_link0(aes(
    filter = (node1.lvl == 2 & node2.lvl == 2),
    edge_colour = col
  ),
  edge_linewidth = 0.3, alpha = 0.5
  ) +
  geom_node_point(aes(
    fill = as.factor(grp),
    shape = as.factor(lvl),
    size = nsize
  )) +
  scale_shape_manual(values = c(21, 22)) +
  scale_size_continuous(range = c(1.5, 4.5)) +
  scale_fill_manual(values = cols2) +
  scale_edge_color_manual(values = cols2, na.value = "grey12") +
  scale_edge_alpha_manual(values = c(0.1, 0.7)) +
  theme_graph() +
  coord_cartesian(clip = "off", expand = TRUE) +
  theme(legend.position = "none")

固定某一层级

xy <- layout_as_multilevel(multilvl_ex,
                           type = "fix2",
                           FUN2 = layout_with_stress,
                           alpha = 25, beta = 45
)
​
ggraph(multilvl_ex, "manual", x = xy[, 1], y = xy[, 2]) +
  geom_edge_link0(aes(
    filter = (node1.lvl == 1 & node2.lvl == 1),
    edge_colour = col
  ),
  alpha = 0.5, edge_linewidth = 0.3
  ) +
  geom_edge_link0(
    aes(filter = (node1.lvl != node2.lvl)),
    alpha = 0.3,
    edge_linewidth = 0.1,
    edge_colour = "black"
  ) +
  geom_edge_link0(aes(
    filter = (node1.lvl == 2 & node2.lvl == 2),
    edge_colour = col
  ),
  edge_linewidth = 0.3, alpha = 0.5
  ) +
  geom_node_point(aes(
    fill = as.factor(grp),
    shape = as.factor(lvl),
    size = nsize
  )) +
  scale_shape_manual(values = c(21, 22)) +
  scale_size_continuous(range = c(1.5, 4.5)) +
  scale_fill_manual(values = cols2) +
  scale_edge_color_manual(values = cols2, na.value = "grey12") +
  scale_edge_alpha_manual(values = c(0.1, 0.7)) +
  theme_graph() +
  coord_cartesian(clip = "off", expand = TRUE) +
  theme(legend.position = "none")

三维网络图可视化

library(threejs)
xyz <- layout_as_multilevel(multilvl_ex,
                            type = "separate",
                            FUN1 = layout_as_backbone,
                            FUN2 = layout_with_stress,
                            project2D = FALSE)
multilvl_ex$layout <- xyz
V(multilvl_ex)$color <- c("#00BFFF", "#FF69B4")[V(multilvl_ex)$lvl]
V(multilvl_ex)$vertex.label <- V(multilvl_ex)$name
​
graphjs(multilvl_ex, bg = "black", vertex.shape = "sphere")