Examining Email Addresses in R

August 22, 2015
By

(This article was first published on Mathew Analytics » R, and kindly contributed to R-bloggers)

I don’t normally work with personal identifiable information such as emails. However, the recent data dump from Ashley Madison got me thinking about how I’d examine a data set composed of email addresses. What are the characteristics of an email that I’d look to extract? How would I perform that task in R? Here’s some quick R code to extract the host, address type, and other information from a set of email strings. From there, we can obviously summarize the data according to a number of desired email characteristics.

df = data.frame(email = c("[email protected]","[email protected]","[email protected]","[email protected]","[email protected]",
                          "[email protected]","[email protected]","[email protected]","[email protected]","[email protected]"))
 
df$one <- sub("@.*$", "", df$email )
df$two <- sub('.*@', '', df$email )
df$three <- sub('.*\.', '', df$email )
 
num <- c(0:9); num
num_match <- str_c(num, collapse = "|"); num_match
df$num_yn <- as.numeric(str_detect(df$email, num_match))
und <- c("_"); und
und_match <- str_c(und, collapse = "|"); und_match
df$und_yn <- as.numeric(str_detect(df$email, und_match))
 
> df
             email    one        two three num_yn und_yn
1      [email protected]m    one    gkn.com   com      0      0
2  [email protected] two132   wern.com   com      1      0
3     [email protected]  three     fu.com   com      0      0
4     [email protected]   four    huo.com   com      0      0
5     [email protected]   five    hoi.net   net      0      0
6   [email protected]    ten hoinse.com   com      0      0
7   [email protected] four99    huo.com   com      1      0
8     [email protected]    two   wern.gov   gov      0      0
9    [email protected]  f_ive    hoi.com   com      0      1
10   [email protected]    six  ihoio.gov   gov      0      0

What about you? If you regularly work with email addresses and have some useful insights for the rest of us, please leave a comment below. How do you usually attack a data set where it’s just a large number of email addresses?

To leave a comment for the author, please follow the link and comment on their blog: Mathew Analytics » R.

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