Spam and num_char

Is there an association between spam and the length of an email? You could imagine a story either way:

Spam is more likely to be a short message tempting me to click on a link, or
My normal email is likely shorter since I exchange brief emails with my friends all the time.

Here, you'll use the email dataset to settle that question. Begin by bringing up the help file and learning about all the variables with ?email.

As you explore the association between spam and the length of an email, use this opportunity to try out linking a dplyr chain with the layers in a ggplot2 object.

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Análisis exploratorio de datos en R

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Instrucciones de ejercicio

Using the email dataset

Load the packages ggplot2, dplyr, and openintro.
Compute appropriate measures of the center and spread of num_char for both spam and not-spam using group_by() and summarize(). No need to name the new columns created by summarize().
Construct side-by-side box plots to visualize the association between the same two variables. It will be useful to mutate() a new column containing a log-transformed version of num_char.

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# Load packages




# Compute summary statistics
email %>%
  ___ %>%
  ___

# Create plot
email %>%
  mutate(log_num_char = ___) %>%
  ggplot(aes(x = ___, y = log_num_char)) +
  ___

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