dplyr - 安排两个变量中的缺失

Question

我已经被这个问题困扰了几个小时，这变得有点令人沮丧。基本上我想排列一些数据，以便 NA 基于分组结构首先出现。我可以做到其中的一部分，但我所做的一切都无法达到预期的结果。

有了这个代码，

df <-  df |> 
  group_by(AESOC, AEPT) |> 
  arrange(!is.na(AEPT), !is.na(Severity), .by_group = TRUE)

我已经能够实现图中所示的效果。

但我仍然想进一步安排，以便第 9-12 行出现在第 1 行之前，第 25-28 行出现在第 13 行之前（即在 AESOC 和 AEPT 确定的组的最开始处。

这个小数据包含在这里：

df <-  structure(list(AESOC = c("Blood and lymphatic system disorders", 
"Blood and lymphatic system disorders", "Blood and lymphatic system disorders", 
"Blood and lymphatic system disorders", "Blood and lymphatic system disorders", 
"Blood and lymphatic system disorders", "Blood and lymphatic system disorders", 
"Blood and lymphatic system disorders", "Blood and lymphatic system disorders", 
"Blood and lymphatic system disorders", "Blood and lymphatic system disorders", 
"Blood and lymphatic system disorders", "Cardiac disorders", 
"Cardiac disorders", "Cardiac disorders", "Cardiac disorders", 
"Cardiac disorders", "Cardiac disorders", "Cardiac disorders", 
"Cardiac disorders", "Cardiac disorders", "Cardiac disorders", 
"Cardiac disorders", "Cardiac disorders", "Cardiac disorders", 
"Cardiac disorders", "Cardiac disorders", "Cardiac disorders"
), AEPT = c("    Anaemia", "    Anaemia", "    Anaemia", "    Anaemia", 
"    Lymphopenia", "    Lymphopenia", "    Lymphopenia", "    Lymphopenia", 
NA, NA, NA, NA, "    Dizziness", "    Dizziness", "    Dizziness", 
"    Dizziness", "    Palpitations", "    Palpitations", "    Palpitations", 
"    Palpitations", "    Presyncope", "    Presyncope", "    Presyncope", 
"    Presyncope", NA, NA, NA, NA), Severity = c("        mild", 
"        moderate", "        severe", NA, "        mild", "        moderate", 
"        severe", NA, "    mild", "    moderate", "    severe", 
NA, "        mild", "        moderate", "        severe", NA, 
"        mild", "        moderate", "        severe", NA, "        moderate", 
"        mild", "        severe", NA, "    moderate", "    mild", 
"    severe", NA)), row.names = c(NA, -28L), class = c("tbl_df", 
"tbl", "data.frame"))

任何帮助将不胜感激。

Answer 1

您可以通过以下方式使用

arrange

：

library(dplyr)

df %>% arrange(AESOC, !is.na(AEPT), !is.na(Severity))

返回：

                                AESOC             AEPT         Severity
1  Blood and lymphatic system disorders             <NA>             <NA>
2  Blood and lymphatic system disorders             <NA>             mild
3  Blood and lymphatic system disorders             <NA>         moderate
4  Blood and lymphatic system disorders             <NA>           severe
5  Blood and lymphatic system disorders          Anaemia             <NA>
6  Blood and lymphatic system disorders      Lymphopenia             <NA>
7  Blood and lymphatic system disorders          Anaemia             mild
8  Blood and lymphatic system disorders          Anaemia         moderate
9  Blood and lymphatic system disorders          Anaemia           severe
10 Blood and lymphatic system disorders      Lymphopenia             mild
11 Blood and lymphatic system disorders      Lymphopenia         moderate
12 Blood and lymphatic system disorders      Lymphopenia           severe
13                    Cardiac disorders             <NA>             <NA>
14                    Cardiac disorders             <NA>         moderate
15                    Cardiac disorders             <NA>             mild
16                    Cardiac disorders             <NA>           severe
17                    Cardiac disorders        Dizziness             <NA>
18                    Cardiac disorders     Palpitations             <NA>
19                    Cardiac disorders       Presyncope             <NA>
20                    Cardiac disorders        Dizziness             mild
21                    Cardiac disorders        Dizziness         moderate
22                    Cardiac disorders        Dizziness           severe
23                    Cardiac disorders     Palpitations             mild
24                    Cardiac disorders     Palpitations         moderate
25                    Cardiac disorders     Palpitations           severe
26                    Cardiac disorders       Presyncope         moderate
27                    Cardiac disorders       Presyncope             mild
28                    Cardiac disorders       Presyncope           severe