由于数据框中的“NA”是文字“NA”，我们需要将它转换为真实的的缺失值NA x x x na_if。然后使用case_when提供新列赋值的条件。我们需要rowwise才能在每行中工作。case_when中的最后一个TRUE ~ "unknown"捕获除了“pos”和“neg”之外的字符串在col1到col3中。
我添加了两个条目来显示当所有行都是NA时，或者当列中有拼写错误时的行为。

library(dplyr)

toy.df %>% 
  rowwise() %>%  
  mutate(across(everything(), ~na_if(.x, "NA")),
         col4 = case_when(all(is.na(c_across(col1:col3))) ~ NA,
                          all(c_across(col1:col3) == "pos", na.rm = T) ~ "pos",
                          all(c_across(col1:col3) == "neg", na.rm = T) ~ "neg",
                          all(c_across(col1:col3) %in% c("pos", "neg", NA)) ~ "both",
                          TRUE ~ "unknown")) %>% 
  ungroup()

# A tibble: 9 × 5
  Name   col1  col2  col3  col4   
  <chr>  <chr> <chr> <chr> <chr>  
1 group1 pos   pos   pos   pos    
2 group2 neg   pos   NA    both   
3 group3 NA    NA    pos   pos    
4 group4 pos   pos   NA    pos    
5 group5 neg   neg   neg   neg    
6 group6 NA    NA    neg   neg    
7 group7 pos   neg   neg   both   
8 group8 NA    NA    NA    NA     
9 group9 pos   pos   typo  unknown

数据

toy.df <- structure(list(Name = c("group1", "group2", "group3", "group4", 
"group5", "group6", "group7", "group8", "group9"), col1 = c("pos", 
"neg", "NA", "pos", "neg", "NA", "pos", NA, "pos"), col2 = c("pos", 
"pos", "NA", "pos", "neg", "NA", "neg", NA, "pos"), col3 = c("pos", 
"NA", "pos", "NA", "neg", "neg", "neg", NA, "typo")), class = "data.frame", row.names = c(NA, 
-9L))

另一种方式：

toy.df$group6 <- apply(toy.df, 1, \(x) {
  val <- sort(unique(x[2:4]))
  if (val[1] == "NA") val = val[2:length(val)]
  if (length(val) == 2) {
    "both"
  } else if (val=="pos")
    "pos"
  else 
    "neg"
})
toy.df

输出：

Name col1 col2 col3 group6
1 group1  pos  pos  pos    pos
2 group2  neg  pos   NA   both
3 group3   NA   NA  pos    pos
4 group4  pos  pos   NA    pos
5 group5  neg  neg  neg    neg
6 group6   NA   NA  neg    neg
7 group7  pos  neg  neg   both