email: Data frame representing information about a collection of emails

Description

These data represent incoming emails for the first three months of 2012 for an email account (see Source).

Usage

data(email)
data(email_test)

Arguments

Format

A email (email_sent) data frame has 3921 (1252) observations on the following 21 variables.

spam: Indicator for whether the email was spam.
to_multiple: Indicator for whether the email was addressed to more than one recipient.
from: Whether the message was listed as from anyone (this is usually set by default for regular outgoing email).
cc: Indicator for whether anyone was CCed.
sent_email: Indicator for whether the sender had been sent an email in the last 30 days.
time: Time at which email was sent.
image: The number of images attached.
attach: The number of attached files.
dollar: The number of times a dollar sign or the word “dollar” appeared in the email.
winner: Indicates whether “winner” appeared in the email.
inherit: The number of times “inherit” (or an extension, such as “inheritance”) appeared in the email.
viagra: The number of times “viagra” appeared in the email.
password: The number of times “password” appeared in the email.
num_char: The number of characters in the email, in thousands.
line_breaks: The number of line breaks in the email (does not count text wrapping).
format: Indicates whether the email was written using HTML (e.g. may have included bolding or active links).
re_subj: Whether the subject started with “Re:”, “RE:”, “re:”, or “rE:”
exclaim_subj: Whether there was an exclamation point in the subject.
urgent_subj: Whether the word “urgent” was in the email subject.
exclaim_mess: The number of exclamation points in the email message.
number: Factor variable saying whether there was no number, a small number (under 1 million), or a big number.

References

~~ OpenIntro Statistics, openintro.org ~~

Examples

Run this code

# NOT RUN {
data(email)
e <- email

#______ Variables For Logistic Regression ______#
# Variables are modified to match
#   OpenIntro Statistics, Second Edition
# As Is (7): spam, to_multiple, winner, format,
#            re_subj, exclaim_subj
# Omitted (6): from, sent_email, time, image,
#              viagra, urgent_subj, number
# Become Indicators (5): cc, attach, dollar,
#                        inherit, password
e$cc       <- ifelse(email$cc > 0, 1, 0)
e$attach   <- ifelse(email$attach > 0, 1, 0)
e$dollar   <- ifelse(email$dollar > 0, 1, 0)
e$inherit  <- ifelse(email$inherit > 0, 1, 0)
e$password <- ifelse(email$password > 0, 1, 0)
# Transform (3): num_char, line_breaks, exclaim_mess
#e$num_char     <- cut(email$num_char, c(0,1,5,10,20,1000))
#e$line_breaks  <- cut(email$line_breaks, c(0,10,100,500,10000))
#e$exclaim_mess <- cut(email$exclaim_mess, c(-1,0,1,5,10000))
g <- glm(spam ~ to_multiple + winner + format + 
                re_subj + exclaim_subj +
                cc + attach + dollar +
                inherit + password, # +
                #num_char + line_breaks + exclaim_mess,
                data=e, family=binomial)
summary(g)


#______ Variable Selection Via AIC ______#
g. <- step(g)
plot(predict(g., type="response"), e$spam)


#______ Splitting num_char by html ______#
x   <- log(email$num_char)
bw  <- 0.004
R   <- range(x) + c(-1, 1)
wt  <- sum(email$format)/nrow(email)
htmlAll   <- density(x, bw=0.4, from=R[1], to=R[2])
htmlNo    <- density(x[email$format != 1], bw=0.4,
                     from=R[1], to=R[2])
htmlYes   <- density(x[email$format == 1], bw=0.4,
                     from=R[1], to=R[2])
htmlNo$y  <- htmlNo$y #* (1-wt)
htmlYes$y <- htmlYes$y #* wt + htmlNo$y
plot(htmlAll, xlim=c(-4, 6), ylim=c(0, 0.4))
lines(htmlNo, col=4)
lines(htmlYes, lwd=2, col=2)
# }