regex - 替代 for() 循环以将非常大的数据框列条目与非常大的向量列表进行比较

Question

我正在尝试获取一个数据框 profiles，其中包含一列 email 地址，并添加一个由每个地址的可注册域部分组成的新列电子邮件地址、域。

我单独创建了一个独特的 registerable_domains 向量，这个过程太复杂以至于无法针对数据框中的每一行运行，其结果是一个必然小于profiles 数据框中的行数。然后我检查 registerable_domains 向量中的每个条目是否出现在 profiles 数据框中每个 email 地址的末尾，并设置匹配的数据框的 domain 列条目。

下面的代码是可重现的数据，您可以在 R 中复制粘贴并执行，每行都带有注释以解释它的作用。

for() 循环做的正是我想做的:它在 profiles 数据的 domain 列中创建适当的条目框架。问题在于，在此示例中，profiles 数据框有 12 行，而 registerable_domains 向量有 8 个条目。在实际数据集中，profiles 数据框有大约 500,000 行，registerable_domains 向量有大约 110,000 个条目。因此，虽然 for() 循环在小数据集上工作得很好，但我需要一种不同的方法来处理非常大的数据集(我估计这种方法需要大约 75 年才能完成)在完整的数据集上完成!)。

非常感谢您帮助将此 for() 循环转换为大型数据集的时间实际操作。我查看了许多其他线程，但找不到解决此特定情况的任何答案(尽管解决了许多其他类似但不同的情况)。谢谢!

# Data frame consisting of a column of 12 emails, and a column of 12 NA entries:

email <- c( "john@doe.com",
            "mary@smith.co.uk",
            "peter@microsoft.com",
            "jane@admins.microsoft.com",
            "luke@star.wars.com",
            "leia@star.wars.com",
            "yoda@masters.star.wars.com",
            "grandma@bletchly.ww2.wars.com",
            "searchfor@janedoe.com",
            "fan@mail.starwars.com",
            "city@toronto.ca",
            "area@toronto.canada.ca");

domain <- c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA);

profiles <- data.frame(email, domain);

profiles; # See what the initial data frame looks like

#                            email domain
# 1                   john@doe.com     NA
# 2               mary@smith.co.uk     NA
# 3            peter@microsoft.com     NA
# 4      jane@admins.microsoft.com     NA
# 5             luke@star.wars.com     NA
# 6             leia@star.wars.com     NA
# 7     yoda@masters.star.wars.com     NA
# 8  grandma@bletchly.ww2.wars.com     NA
# 9          searchfor@janedoe.com     NA
# 10         fan@mail.starwars.com     NA
# 11               city@toronto.ca     NA
# 12        area@toronto.canada.ca     NA

# Vector consisting of email addresses stripped to registerable domain component only, created through a separate process that is too complex to run on each row entry:

registerable_domains <- c(  "doe.com",
                            "smith.co.uk",
                            "microsoft.com",
                            "wars.com",
                            "janedoe.com",
                            "starwars.com",
                            "toronto.ca",
                            "canada.ca");

# Credit to Nick Kennedy for his help with this original solution (http://stackoverflow.com/users/4998761/nick-kennedy)

for (domains in registerable_domains) {                                             # Iterate through each of the registerable domains
    domains_pattern <- paste("[.@]", domains, "$", sep="");                         # Add regex characters to ensure that it's only the end part to deal with nested domain names
    found <- grepl(domains_pattern, profiles$email, ignore.case=TRUE, perl=TRUE);   # Grep for the current domain pattern in all of the emails and build a boolean table for entry locations
    profiles[which(found & is.na(profiles$domain)), "domain"] <- domains;           # Modify profile data table at TRUE entry locations not yet set
}

profiles; # Expected and desired outcome:

#                            email        domain
# 1                   john@doe.com       doe.com
# 2               mary@smith.co.uk   smith.co.uk
# 3            peter@microsoft.com microsoft.com
# 4      jane@admins.microsoft.com microsoft.com
# 5             luke@star.wars.com      wars.com
# 6             leia@star.wars.com      wars.com
# 7     yoda@masters.star.wars.com      wars.com
# 8  grandma@bletchly.ww2.wars.com      wars.com
# 9          searchfor@janedoe.com   janedoe.com
# 10         fan@mail.starwars.com  starwars.com
# 11               city@toronto.ca    toronto.ca
# 12        area@toronto.canada.ca     canada.ca

Answer 1

这是一个使用 dplyr

的解决方案

library(dplyr)
person <- data_frame(Email = email) %>% 
  mutate(Domain = gsub("^.*@", "", Email)) # everything upto the last @
domain <- person %>% 
  select(Domain) %>% # select the Domain variable
  distinct() %>%  # keep only unique rows
  mutate(Original = Domain) # copy Domain into Original
extra <- domain %>% 
  mutate(Domain = gsub("^[[:alnum:]]*\\.", "", Domain)) %>% # remove all alphanumeric characters upto the first point and overwrite Domain
  filter(grepl("\\.", Domain)) # keep only observations where domain contains at least one point
while (nrow(extra) > 0){
  domain <- bind_rows(domain, extra) #add the rows from extra to domain
  extra <- extra %>% 
    mutate(Domain = gsub("^[[:alnum:]]*\\.", "", Domain)) %>% 
    filter(grepl("\\.", Domain))
}
register <- data_frame(Domain = registerable_domains)
register %>% 
  inner_join(domain, by = "Domain") %>% #join the two table on a common Domain
  inner_join(person, by = c("Original" = "Domain")) # join the resulting table to person where result.Original = person.Domain