43 lines
1006 B
R
43 lines
1006 B
R
# install.packages("bayesrules")
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library(bayesrules)
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library(dplyr)
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data(fake_news)
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# make it a tibble for the sake printing
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fake_news <- tibble::as_tibble(fake_news)
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fake_news |> glimpse()
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# proportion of each type of article
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fake_news |>
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group_by(type) |>
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summarise(
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n = n(),
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prop = n / nrow(fake_news) # <- is there a NSE way
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# of getting total rows of
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# original DF?
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)
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# we can also do this with the tally function
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fake_news|>
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group_by(type) |>
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tally() |>
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mutate(prop = n / sum(n))
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# usage of "!" in fake vs real articles
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fake_news |>
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group_by(type, title_has_excl) |>
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summarise(
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total_usage_of_excl = n()
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) |>
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ungroup() |>
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group_by(type) |>
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summarise(
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prop_excl_within_type = total_usage_of_excl / sum(total_usage_of_excl),
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title_has_excl
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) |>
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filter(title_has_excl)
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# suppose we get a new article that is titled
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