ggalluvial-排序地层顺序

Question

我已经尝试了好几天来对层积和岩屑流进行排序。我想通过不同的筛查程序（X1，X2，X3，X4）可视化患者的流向，并根据最终诊断（X4中的值）对流向进行着色。

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？我希望所有红色，黄色和蓝色值在每个组中都堆叠在一起。

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

如果这在ggalluvial中是不可能的，但在其他软件包中是不可能的，我也想知道。

谢谢。

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
这些值不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
第一列中的流水线未排序。

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，即使地层（在每个轴上堆叠的不同值）可能具有自然顺序，代表个别病例或同类人群的Alluvia通常也没有。这意味着stat层（例如stat_alluvium()）的一项工作是确定每个阶层内矿脉的排序。 （然后确定层之间的流量。）

为提高清晰度，stat_alluvium()和stat_flow()使用案例或同类群组在附近轴上的层次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，改编自the alluvial package；有关其他选项，请参见the "lode guidance" documentation。

当用户希望在同一层内将同类群组归为一组时，例如当对波峰和流量分配美学效果（通常为fill，但可选地alpha，colour，{ {1}}和linetype）。 size参数通过在确定矿脉排列顺序时优先考虑邻近轴上的岩层（而不是岩层而不是岩层）而不是岩层优先顺序来解决此问题。

@Steen提供了一个句法答案，我将在这里基本上复制它。我将示例B中的aes.bind更改为stat_flow()，以说明stat_alluvium()可以传递给任一geom层，并可以由它们正确解释。

示例A：

aes.bind

示例B：

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()

^{由reprex package（v0.2.1）于2019-07-27创建}

Answer 2

就像科里·布伦森（Cory Brunson）在评论中写道：“关键是aes.bind = TRUE”

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_flow(aes(fill = fill_stat),
            aes.bind = TRUE) +
  geom_stratum() +
  geom_text(stat = "stratum")+
  scale_fill_manual(values=c("red", "yellow", "blue"))+
  theme_void()