电子邮件的 R 数据整理

Question

需要帮助！这是一个与工作相关的项目。我需要清理 16,000 封电子邮件......预计需要手动完成 :( 我需要找到一个方法从电子邮件中提取域名并将其放入新列中，并将名称也解析到新列中，同时仍保留原始邮件。数据部分完整。

library(tidyr)
library(magrittr)

Email.Address <- c('john.doe@abccorp.com','jdoe@cisco.com','johnd@widgetco.com')
First.Name <- c('John', 'JDoe','NA' )
Last.Name <- c('Doe','NA','NA')
Company <- c('NA','NA','NA')

data <- data.frame(Email.Address, First.Name, Last.Name, Company)
separate_DF <- data %>% separate(Email.Address, c("Company"), sep="@")

Answer 1

试试这个

df  <-  data.frame(Email.Address, First.Name, Last.Name, Company, stringsAsFactors = FALSE)
Corp <- sapply(strsplit(sapply(strsplit(df$Email.Address,"@"),"[[",2),"[.]"),"[[",1)
F.Name <- sapply(strsplit(sapply(strsplit(df$Email.Address,"@"),"[[",1), "[.]"),"[[",1)
L.Name <- sapply(strsplit(sapply(strsplit(df$Email.Address,"@"),"[[",1),"[.]"),tail,n=1)
L.Name[L.Name == F.Name]    <-  NA
OUT <- data.frame(df$Email.Address, F.Name, L.Name, Corp)
df[df=="NA" |is.na(df)] <-  OUT[df=="NA" |is.na(df)]
df

tidyr 中的函数 separate 看起来也不错。

http://blog.rstudio.org/2014/07/22/introducing-tidyr/

根据您提供的信息，这也有效：

library(tidyr)

df  <-  data.frame(Email.Address, First.Name, Last.Name, Company)
df2 <-  separate(df, Email.Address, into = c("Name", "Corp"), sep = "@")
df2 <-  separate(df2, Name, into = c("F.Name", "L.Name"), sep = "[.]", extra = "drop")
df2 <-  separate(df2, Corp, into = c("Corp", "com"), sep = "[.]", extra = "drop")

电子邮件的 R 数据整理

R Data Wrangling for Emails

r

data-cleaning