r - 带有 dplyr 的条件过滤器

Question

这是一个数据框：

  cluster_names Species values   Nsp Nsp_MRCA Event NB_Event Nsp_losses
 1 Group1        Sp1          1     3        3     1        2          0
 2 Group1        Sp1          4     3        3     1        2          0
 3 Group1        Sp2         78    NA       NA     1        2         NA
 4 Group1        Sp3         NA     3       12     2        2          9
 5 Group1        Sp4         NA     3        3     2        2          0
 6 Group2        Sp2          3     2        3     2        2          1
 7 Group2        Sp3          9     2       40     2        2         38
 8 Group2        Sp4          8    NA       NA     2        2         NA
 9 Group3        Sp1          9     2        2     1        1          0
10 Group3        Sp3         10     3        3     1        1          0
11 Group3        Sp3         12     3       20     1        1         17
12 Group3        Sp3         14     2        3     1        1          1
13 Group4        Sp4         23     3      112     1        1        109
14 Group5        Sp3         34     5      114     1        1        109
15 Group6        Sp4          2     3        3     1        1          0

我怎么能说deplyr，只保留Groups在哪里：

• Nsp > 1至少对于one row
• Nsp == Nsp_MRCA至少一排
• 如果所有Nsp_losses < 3Nsp 都在 5 和 2 之间并且所有Nsp_losses < 20
• 一切NB_Event都必须是< 3

有了这样的过滤器，我应该得到一个新的 df ：

  cluster_names Species values   Nsp Nsp_MRCA Event NB_Event Nsp_losses
 1 Group1        Sp1          1     3        3     1        2          0
 2 Group1        Sp1          4     3        3     1        2          0
 3 Group1        Sp2         78    NA       NA     1        2         NA
 4 Group1        Sp3         NA     3       12     2        2          9
 5 Group1        Sp4         NA     3        3     2        2          0
 9 Group3        Sp1          9     2        2     1        1          0
10 Group3        Sp3         10     3        3     1        1          0
11 Group3        Sp3         12     3       20     1        1         17
12 Group3        Sp3         14     2        3     1        1          1
15 Group6        Sp4          2     3        3     1        1          0

细节：

• Group1被保留是因为介于Nsp两者5之间2Nsp_losses < 20
• Group2被删除是因为Nsp_losses = 38
• Group3被保留是因为 介于Nsp两者5之间2Nsp_losses < 20
• Groups 4并被5删除，因为Nsp_losses = 38
• Group6保留，因为Nsp == Nsp_MRCA至少一排

并且所有这些至少有一行 Nsp > 1

到目前为止，我尝试了以下代码：

tab %>%
  group_by(cluster_names) %>%
    mutate(NB_Event = max(Event,na.rm=TRUE))  %>%
    filter(any(Nsp > 1 |is.na(Nsp))) %>%
    filter(any(Nsp == Nsp_MRCA)) %>%
    mutate(Nsp_losses = abs(Nsp - Nsp_MRCA)) %>%
    filter(all(Nsp <=5 |is.na(Nsp)) & all(Nsp > 1 |is.na(Nsp) & all(Nsp_losses < 20 |is.na(Nsp_losses))))  %>%

这是数据框

structure(list(cluster_names = structure(c(1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 5L, 6L), .Label = c("Group1", 
"Group2", "Group3", "Group4", "Group5", "Group6"), class = "factor"), 
    Species = structure(c(1L, 1L, 2L, 3L, 4L, 2L, 3L, 4L, 1L, 
    3L, 3L, 3L, 4L, 3L, 4L), .Label = c("Sp1", "Sp2", "Sp3", 
    "Sp4"), class = "factor"), values = c(1L, 4L, 78L, NA, NA, 
    3L, 9L, 8L, 9L, 10L, 12L, 14L, 23L, 34L, 2L), Nsp = c(3L, 
    3L, NA, 3L, 3L, 2L, 2L, NA, 2L, 3L, 3L, 2L, 3L, 5L, 3L), 
    Nsp_MRCA = c(3L, 3L, NA, 12L, 3L, 3L, 40L, NA, 2L, 3L, 20L, 
    3L, 112L, 114L, 3L), Event = c(1L, 1L, 1L, 2L, 2L, 2L, 2L, 
    2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L)), class = "data.frame", row.names = c(NA, 
-15L))

感谢您的帮助和时间。

标签： rdplyr

Answer 1

The presence of NAs makes it a bit tricky hence, I remove them first using na.omit and find out groups (cluster_names) which satisfy the conditions given and later filter based on that.

library(dplyr)

tab %>%
  filter(cluster_names %in% (tab %>%
  na.omit() %>%
  mutate(Nsp_losses = abs(Nsp - Nsp_MRCA)) %>%
  group_by(cluster_names) %>%
  filter(any(Nsp > 1 & Nsp == Nsp_MRCA) & all(Event < 3) &
       (if(all(Nsp %in% 2:5)) all(Nsp_losses < 20) else all(Nsp_losses < 3))) %>%
   pull(cluster_names) %>% unique))


#  cluster_names Species values Nsp Nsp_MRCA Event
#1         Group1     Sp1      1   3        3     1
#2         Group1     Sp1      4   3        3     1
#3         Group1     Sp2     78  NA       NA     1
#4         Group1     Sp3     NA   3       12     2
#5         Group1     Sp4     NA   3        3     2
#6         Group3     Sp1      9   2        2     1
#7         Group3     Sp3     10   3        3     1
#8         Group3     Sp3     12   3       20     1
#9         Group3     Sp3     14   2        3     1
#10        Group6     Sp4      2   3        3     1