这是我对dplyr的处理方法：

female_names %>%
  full_join(male_names, by = "Name") %>%
  replace(is.na(.), 0) %>%
  mutate(Name, gender = ifelse(count.x > count.y, "female", "male"), .keep = "none") %>%
  right_join(customer_names, by = "Name")

结果：

Name gender
1  Binita female
2 Krishna   male
3    Ajit   male
4  Dhiraj   male

我建议您先将您的女性和男性姓名合并，然后求和，这样您就可以知道要为每个姓名指定什么性别。然后将您的客户加入到该表中。我在这里使用data.table，这只是我的喜好。

library(data.table)

gender_by_name <- setDT(merge(male_names, female_names, by = "Name", all = T))[, .(gender = ifelse(sum(count.x, -count.y, na.rm = T) > 0, "male", "female")), Name]
gender_by_name[setDT(customer_names), on = .(Name)]

• • 结果**

Name gender
1:    Ajit   male
2:  Binita female
3:  Dhiraj   male
4: Krishna   male

如果你只想要性别间数量最多的性别。
[Edit：意识到这将在数量相等的情况下保持两性，但问题是不清楚在这种情况下会发生什么]

bind_rows(male=male_names, female=female_names, .id = "gender") |> 
       group_by(Name) |>
       filter(count==max(count)) |>
       merge(customer_names)

     Name gender count
1    Ajit   male     4
2  Binita female     1
3  Dhiraj   male     4
4 Krishna   male     7

但我会仔细考虑你要用这些数据做什么，以及这是否是猜测性别的合适方法。