r - ggalluvial - 对地层的顺序进行排序

Question

我几天来一直在尝试对 ggalluvial 中的地层和流量进行排序。我想通过不同的筛查程序(X1、X2、X3、X4)可视化患者的流程，并根据最终诊断(X4 中的值)对流程进行着色。

您能帮我对示例 A 和 B 的第一列中的组内的值进行排序吗？我希望每个组中的所有红色、黄色和蓝色值都堆叠在一起。

到目前为止，我已经尝试了宽格式、aes.flow“向后”和“向前”、lode.guidance 和 lode.ordering 的各种组合...

如果这在 ggalluvial 中不可能，但在其他软件包中可能，我也想知道。

提前致谢。

宽格式数据:

set.seed(1)
data <- tibble(
  ID = 1:879,
  X1 = sample(c("only_parent", "parent_and_3D", "only_3D"), size = 879, replace = TRUE, prob = c(0.1, 0.8, 0.1))) %>% 
  mutate(
    X2 = case_when(
      X1 == "only_parent" ~ sample(c("only_I", "not_identified"), size = n(), prob = c(0.1, 0.9), replace = TRUE),
      X1 == "parent_and_3D" ~ sample(c("only_I", "both_I_and_II", "only_II", "not_identified"), size = n(), prob = c(0.05, 0.05, 0.2, 0.7), replace = TRUE),
      X1 == "only_3D"~ sample(c("only_II", "not_identified"), size = n(), prob = c(0.1, 0.9), replace = TRUE),
      TRUE ~ NA_character_)) %>% 
  mutate(
    X3 = case_when(
      X2 == "only_I" ~ "PO_only",
      X2 == "both_I_and_II" ~ sample(c("PO_and_EHL", "PO_and_F/T", "PO_and_F/T_and_EHL"), size = n(), prob = c(0.3, 0.5, 0.2), replace = TRUE),
      X2 == "only_II"~ sample(c("F/T", "F/T_and_EHL", "EHL"), size = n(), prob = c(0.1, 0.6, 0.4), replace = TRUE),
      X2 == "not_identified" ~ "not_identified",
      TRUE ~ NA_character_)) %>% 
  mutate(
    X4 = case_when(
      X3 == "PO_only"    ~ sample(c("Two_primary_ind", "One_primary_ind", "No TW"), size = n(), prob = c(0.02, 0.1, 0.88), replace = TRUE),
      X3 == "PO_and_EHL" ~ sample(c("Two_primary_ind", "One_primary_ind", "No TW"), size = n(), prob = c(0.05, 0.2, 0.75), replace = TRUE),
      X3 == "PO_and_F/T" ~ sample(c("Two_primary_ind", "One_primary_ind", "No TW"), size = n(), prob = c(0.05, 0.2, 0.75), replace = TRUE),
      X3 == "PO_and_F/T_and_EHL" ~ sample(c("Two_primary_ind", "One_primary_ind", "No TW"), size = n(), prob = c(0.05, 0.2, 0.75), replace = TRUE),
      X3 == "F/T" ~ sample(c("Two_primary_ind", "One_primary_ind", "No TW"), size = n(), prob = c(0.02, 0.1, 0.88), replace = TRUE),
      X3 == "F/T_and_EHL" ~ sample(c("Two_primary_ind", "One_primary_ind", "No TW"), size = n(), prob = c(0.05, 0.2, 0.75), replace = TRUE),
      X3 == "EHL" ~ sample(c("Two_primary_ind", "One_primary_ind", "No TW"), size = n(), prob = c(0.02, 0.2, 0.88), replace = TRUE),
      X3 == "not_identified" ~ "not_identified",
      TRUE ~ NA_character_ ))

head(data)

# A tibble: 6 x 5
     ID X1            X2             X3             X4            
  <int> <chr>         <chr>          <chr>          <chr>         
1     1 parent_and_3D not_identified not_identified not_identified
2     2 parent_and_3D only_II        F/T_and_EHL    No TW         
3     3 parent_and_3D not_identified not_identified not_identified
4     4 only_parent   only_I         PO_only        No TW         
5     5 parent_and_3D only_II        F/T_and_EHL    No TW         
6     6 only_3D       not_identified not_identified not_identified

示例 A
这些值未在第一列的底部框中排序。

data_long_a <- data %>% 
  group_by(X1, X2, X3, X4) %>% 
  count() %>% 
  mutate(
    fill_stat = factor(X4, levels = c("not_identified", "No TW", "One_primary_ind", "Two_primary_ind"))) %>% 
  ungroup  %>%
  arrange(fill_stat) %>% 
  mutate(subject = seq(1, n())) %>% 
  gather(key, value, -n , -subject, -fill_stat) %>% 
  mutate(
    key = factor(key, levels = c("X1", "X2", "X3", "X4"))) %>% 
  arrange(key, fill_stat) 



data_long_a %>% 
  filter(key %in% c("X1", "X2")) %>% 
  ggplot(
    aes(x = key,
        y = n,
        stratum = value, 
        alluvium = subject,
        label = value))+
  geom_flow(aes(fill = fill_stat)) +
  geom_stratum() +
  geom_text(stat = "stratum")+
  scale_fill_manual(values=c("#BAB3B3EB", "red", "yellow", "blue"))+
  theme_void()

示例 B
第一列中的流程线未排序。

data_long_b <- data %>%
  select(-X1) %>% 
  filter(X4 != "not_identified") %>% 
  group_by(X2, X3, X4) %>% 
  count() %>% 
  mutate(
    fill_stat = factor(X4, levels = c("not_identified", "No TW", "One_primary_ind", "Two_primary_ind"))) %>% 
  ungroup  %>%
  arrange(fill_stat) %>% 
  mutate(subject = seq(1, n())) %>% 
  gather(key, value, -n , -subject, -fill_stat) %>% 
  mutate(
    key = factor(key, levels = c("X2", "X3", "X4"))) %>% 
  arrange(key, fill_stat) 


data_long_b %>% 
  ggplot(
    aes(x = key,
        y = n,
        stratum = value, 
        alluvium = subject,
        label = value))+
  geom_flow(aes(fill = fill_stat),
            aes.flow = "backward") +
  geom_stratum() +
  geom_text(stat = "stratum")+
  scale_fill_manual(values=c("red", "yellow", "blue"))+
  theme_void()

Answer 1

这里的背景是，尽管地层(每个轴上堆积的不同值)可能具有自然顺序，但代表个体或群体的冲积层通常没有自然顺序。这意味着统计层(例如stat_alluvium())的一项工作是确定每个层内矿脉的顺序。 (这决定了层之间的流量。)

为了提高清晰度，stat_alluvium() 和 stat_flow() 使用附近轴处的病例或群组的分层来指导它们在给定轴上的定位。默认情况下，它以“之字形”顺序执行此操作，改编自 the alluvial package ;请参阅the "lode guidance" documentation以获得更多选项。

当用户想要在层内将群组分组在一起时，例如当矿脉和流量被分配美观时(通常是 fill，但也可以选择 alpha、颜色、线型和尺寸)。 aes.bind 参数通过在确定矿脉顺序时优先考虑附近轴中的美学之前(但不是而不是)地层来解决此问题。 p>

@Steen 提供了一个语法答案，我基本上将复制到这里。我在示例 B 中进行了一项更改，从 stat_flow() 更改为 stat_alluvium()，以说明 aes.bind 可以传递给并将可以被任意一个 geom 层正确解释。

示例 A:

data_long_a %>% 
  filter(key %in% c("X1", "X2")) %>% 
  ggplot(
    aes(x = key,
        y = n,
        stratum = value, 
        alluvium = subject,
        label = value))+
  geom_flow(aes(fill = fill_stat), aes.bind = TRUE) +
  geom_stratum() +
  geom_text(stat = "stratum")+
  scale_fill_manual(values=c("#BAB3B3EB", "red", "yellow", "blue"))+
  theme_void()

示例 B:

data_long_b %>% 
  ggplot(
    aes(x = key,
        y = n,
        stratum = value, 
        alluvium = subject,
        label = value))+
  geom_alluvium(aes(fill = fill_stat),
                aes.bind = TRUE) +
  geom_stratum() +
  geom_text(stat = "stratum")+
  scale_fill_manual(values=c("red", "yellow", "blue"))+
  theme_void()

^{由reprex package于2019年7月27日创建(v0.2.1)}