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#0_Chapter_03_code.R
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#0_Chapter_03_code.R
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library(dplyr)
library(janeaustenr)
library(tidytext)
book_words <- austen_books() %>%
unnest_tokens(word, text) %>%
count(book, word, sort = TRUE) %>%
ungroup()
total_words <- book_words %>%
group_by(book) %>%
summarise(total = sum(n))
book_words <- left_join(book_words, total_words)
library(ggplot2)
ggplot(book_words, aes(n/total, fill = book)) +
geom_histogram(show.legend = FALSE) +
xlim(NA, 0.0009) +
facet_wrap(~book, ncol = 2, scales = "free_y")
#zipf's law code below. Plotting term frequency vs rank should give a straight line according to the law
freq_by_rank <- book_words %>%
group_by(book) %>%
mutate(rank = row_number(),
`term frequency` = n/total)
freq_by_rank
zipf_plot <- freq_by_rank %>%
ggplot(aes(rank, `term frequency`, color = book)) +
geom_line(size = 1.1, alpha = 0.8, show.legend = FALSE) +
scale_x_log10() +
scale_y_log10()
rank_subset <- freq_by_rank %>%
filter(rank < 500,
rank > 10)
lm(log10(`term frequency`) ~log10(rank), data = rank_subset)
fited_zipf_plot <- freq_by_rank %>%
ggplot(aes(rank, `term frequency`, color = book)) +
geom_abline(intercept = -0.62, slope = -1.1, color = "gray50", linetype = 2) +
geom_line(size = 1.1, alpha = 0.8, show.legend = FALSE) +
scale_x_log10() +
scale_y_log10()
book_words <- book_words %>%
bind_tf_idf(word, book, n)
book_words
book_words %>%
select(-total) %>%
arrange(desc(tf_idf))
high_tf_idf_plot <- book_words %>%
arrange(desc(tf_idf)) %>%
mutate(word = factor(word, levels = rev(unique(word)))) %>%
group_by(book) %>%
top_n(15) %>%
ungroup() %>%
ggplot(aes(word, tf_idf, fill = book)) +
geom_col(show.legend = FALSE) +
labs(x = NULL, y = "tf-idf") +
facet_wrap(~book, ncol = 2, scales = "free") +
coord_flip()
library(gutenbergr)
physics <- gutenberg_download(c(37729, 14725, 13476),
meta_fields = "author")
library(readr)
write_csv(physics, "data/physics.csv")
physics <- read_csv("data/physics.csv")
conn = file("data/relativity.txt", "r")
relativity <- readLines(conn)
relativity <- tibble(gutenberg_id = 5001, text = relativity, author = "Einstein, Albert")
physics <- bind_rows(physics, relativity)
physics$text <- ifelse(is.na(physics$text), " ", physics$text)
physics_words <- physics %>%
unnest_tokens(word, text) %>%
count(author, word, sort = TRUE) %>%
ungroup()
physics_words
plot_physics <- physics_words %>%
bind_tf_idf(word, author, n) %>%
arrange(desc(tf_idf)) %>%
mutate(word = factor(word, levels = rev(unique(word)))) %>%
mutate(author = factor(author, levels = c("Galilei, Galileo",
"Huygens, Christiaan",
"Tesla, Nikola",
"Einstein, Albert")))
plot_physics %>%
group_by(author) %>%
top_n(15, tf_idf) %>%
ungroup() %>%
mutate(word = reorder(word, tf_idf)) %>%
ggplot(aes(word, tf_idf, fill = author)) +
geom_col(show.legend = FALSE) +
labs(x = NULL, y = "tf-idf") +
facet_wrap(~author, ncol = 2, scales = "free") +
coord_flip()
library(stringr)
#checking to see the occurrences of the word "eq" are
physics %>%
filter(str_detect(text, "eq\\.")) %>%
select(text)
#checking to see what the occurrences of the word "K1" are
physics %>%
filter(str_detect(text, "K1")) %>%
select(text)
physics %>%
filter(str_detect(text, "AK")) %>%
select(text)
#some custom stopwords for this corpus
mystopwords <- data_frame(word = c("eq", "co", "rc", "ac", "ak", "bn",
"fig", "file", "cg", "cb", "cm"))
physics_words <- anti_join(physics_words, mystopwords, by = "word")
plot_physics <- physics_words %>%
bind_tf_idf(word, author, n) %>%
arrange(desc(tf_idf)) %>%
mutate(word = factor(word, levels = rev(unique(word)))) %>%
group_by(author) %>%
top_n(15, tf_idf) %>%
ungroup %>%
mutate(author = factor(author, levels = c("Galilei, Galileo",
"Huygens, Christiaan",
"Tesla, Nikola",
"Einstein, Albert")))
ggplot(plot_physics, aes(word, tf_idf, fill = author)) +
geom_col(show.legend = FALSE) +
labs(x = NULL, y = "tf-idf") +
facet_wrap(~author, ncol = 2, scales = "free") +
coord_flip()