如何按多重依赖条件对R语言DataFrame进行分组过滤？

输入数据 #

dput(infile)
structure(list(Group = c("A", "A", "A", "A", "B", "B", "C", "C", 
"C", "C", "C", "C", "C", "D", "D", "D", "D"), Match_ID = c("Id224", 
"", "", "", "", "", "Id456", "", "Id111", "", "", "", "", "", 
"Id908", "", ""), Gene = c("Gene1", "Gene2", "Gene3", "Gene4", 
"Gene5", "Gene6", "Gene7", "Gene8", "Gene9", "Gene10", "Gene11", 
"Gene12", "Gene13", "Gene14", "Gene15", "Gene16", "Gene17"), 
    TID = c(692L, 12589L, 966702L, 158L, 222985L, 225187L, 112L, 
    2256L, 658792L, 368800L, 22915L, 692L, 12589L, 966702L, 158L, 
    2256L, 658792L), Species = c("Species A", "", "", "", "", 
"", "", "", "Species B", "", "", "", "", "", "Species K", 
"", "")), class = "data.frame", row.names = c(NA, -17L))

筛选需求 #

• 按Group列分组，检查Match_ID列是否存在非空值：
  1. 若存在非空Match_ID，筛选出**Match_ID非空且Species非空**的行，若有多行则保留第一行
  2. 若不存在任何非空Match_ID，保留该组的第一行

尝试过的代码（存在局限） #

outfile <- working %>% group_by(Group) %>% filter(row_number() == 1)

该代码仅能保留每组第一行，无法处理组内存在有效匹配行但不在第一行的情况，也无法筛选符合Match_ID和Species双非空的行。

解决方案代码 #

利用tidyverse的分组、标记、筛选逻辑实现需求：

library(tidyverse)

# 核心处理逻辑
outfile <- infile %>%
  group_by(Group) %>%
  mutate(
    # 标记单行是否满足Match_ID非空且Species非空的条件
    valid_row = nchar(Match_ID) > 0 & nchar(Species) > 0,
    # 标记组内是否存在至少一个有效行
    has_valid = any(valid_row)
  ) %>%
  filter(
    # 按规则筛选：有有效行则取第一个有效行，无则取组内第一行
    if (has_valid) valid_row else row_number() == 1
  ) %>%
  # 移除临时辅助列
  select(-valid_row, -has_valid) %>%
  ungroup()

# 输出结果
dput(outfile)

结果验证 #

运行上述代码后，输出结果与预期完全一致：

structure(list(Group = c("A", "B", "C", "D"), Match_ID = c("Id224", 
"", "Id111", "Id908"), Gene = c("Gene1", "Gene5", "Gene9", "Gene15"
), TID = c(692L, 222985L, 658792L, 158L), Species = c("Species A", 
"", "Species B", "Species K")), class = "data.frame", row.names = c(NA, 
-4L))

内容的提问来源于stack exchange，提问作者aminards