email {openintro}

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

email

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

Number of people cc'ed.

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.

Source

David Diez's Gmail Account, early months of 2012. All personally identifiable information has been removed.

See Also

email50

Examples

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 == 1) / 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)