#1. This table displays the number of words (num_words) each individual inaugural speech contained, total number of diverse words (lex_diversity) in each inaugural speech, and the overall density of the distinct words (lex_density) in each inaugural speech. Density is calculated by the toal number of diverse words divided by the total number of words. Essentially this table allows us to see how diverse each President’s inaugural speech was. While Abraham Lincoln had the highest total number of diverse words used (1011), He also had the lowest rate of density (0.278). Furthermore, George Washington has the highest level of density (0.418) and lowest total amount of diverse words used (593).
inaugural_words |>mutate(word_length =nchar(word)) |>ggplot(aes(word_length)) +facet_wrap(vars(author), scales ="free_y") +geom_histogram(binwidth =1) +labs(title ="Word Length Distrbution for Each Inaugural Speech")
#2 The graphs above show distribution of word length between each President’s inaugural speeches. Referring back to question #1, we see that Abraham Lincoln had the greatest length of words.
inaugural_words |>group_by(author) |>count(word, sort = T) |>top_n(5) |>ungroup() |>mutate(word =reorder(word, n)) |>ggplot(aes(word, n, fill = author)) +geom_col(show.legend =FALSE) +coord_flip() +facet_wrap(~author, scales ="free") +scale_fill_viridis_d() +theme_minimal() +labs(x =NULL, y ="Most common words")
Selecting by n
#2 This chart displays the most common words used by each individual President in their Inaugural speeches. The most commonly used word for each President is the word “the”, and secondly, for the most part the next most commonly used word is “of”.
inaugural_words |>anti_join(snowball) |>group_by(author) |>count(word, sort = T) |>top_n(5) |>ungroup() |>mutate(word =reorder(word, n)) |>ggplot(aes(word, n, fill = author)) +geom_col(show.legend =FALSE) +labs(x =NULL, y ="Most common words (stop words removed)") +facet_wrap(vars(author), scales ="free") +scale_fill_viridis_d() +theme_minimal() +coord_flip()
Joining with `by = join_by(word)`
Selecting by n
#2 Similar to the previous chart, This chart also displays the most common words used y each President in their Inaugural speech. However, this chart eliminates the use of ‘stop words’. We see that for the most part the three most common words used are either ‘us’, ‘will’ , or ‘constitution’.
inaugural_word_counts <- inaugural_speeches |># This counts each word per authorunnest_tokens(word, text) |>count(author, word, sort =TRUE) total_words <- inaugural_word_counts |># This counts total words per authorgroup_by(author) |>summarize(total =sum(n))inaugural_word_counts <-left_join(inaugural_word_counts, total_words) # Joins the two
# A tibble: 5,265 × 7
author word n total tf idf tf_idf
<chr> <chr> <int> <int> <dbl> <dbl> <dbl>
1 FDR helped 7 1881 0.00372 1.95 0.00724
2 FDR leadership 7 1881 0.00372 1.95 0.00724
3 Lincoln while 13 3637 0.00357 1.95 0.00696
4 Kennedy both 10 1365 0.00733 0.847 0.00621
5 Kennedy arms 4 1365 0.00293 1.95 0.00570
6 FDR money 5 1881 0.00266 1.95 0.00517
7 Kennedy sides 8 1365 0.00586 0.847 0.00497
8 Lincoln case 9 3637 0.00247 1.95 0.00482
9 Lincoln union 20 3637 0.00550 0.847 0.00466
10 Jefferson principle 6 1730 0.00347 1.25 0.00434
# ℹ 5,255 more rows
#3 This table displays the unique words used by each President in their Inaugural speeches in comparison to how frequent the other President’s chose to use the word. For example, if the word was ‘helped’, FDR used the word a total of seven times (n=7) for a term frequency-inverse document frequency (TF-IDF) of 0.0072 which means that the term ‘help’ was used quite rarely in comparison to the other Presidents Inaugural speeches.
#3 This chart furthermore displays the results from the tables above Regarding the frequency and uniqueness of words used in each Presidents Inaugural speech.
bing <-get_sentiments("bing")
inaugural_words |>inner_join(bing) |>count(word, sentiment, sort =TRUE) |>group_by(sentiment) |>top_n(10) |>ungroup() |>mutate(word =reorder(word, n)) |>ggplot(aes(word, n, fill = sentiment)) +geom_col(show.legend =FALSE) +facet_wrap(vars(sentiment), scales ="free") +labs(y ="Inaugural Speeches: Words that contribute the most to each sentiment",x =NULL) +scale_fill_viridis_d() +coord_flip() +theme_minimal()
Joining with `by = join_by(word)`
Selecting by n
#4 These two bar graphs display the words that contributed the most to both positive and negative sentiment. For the left graph (negative) we see the words with the most power are ‘fear’ and ‘crisis. For the graph on the right (positive sentiment), the words with the most power are ’good’, great’, and ‘freedom’.
inaugural_speeches |>unnest_tokens(bigram, text, token ="ngrams", n =2) |>select(bigram) -> inaugural_bigram
inaugural_bigram |>count(bigram, sort = T)
# A tibble: 10,876 × 2
bigram n
<chr> <int>
1 of the 146
2 in the 80
3 of our 55
4 to the 55
5 and the 38
6 to be 37
7 it is 35
8 by the 34
9 for the 30
10 that the 29
# ℹ 10,866 more rows
#5 This table shows the bigrams from most-to-least common in all of the President’s Inaugural speeches.
first_word <-c("american", "fellow") inaugural_bigram |>count(bigram, sort = T) |>separate(bigram, c("word1", "word2"), sep =" ") |># separate the two wordsfilter(word1 %in% first_word) |># find first words from our listcount(word1, word2, wt = n, sort =TRUE) |>mutate(word2 =factor(word2, levels =rev(unique(word2)))) |># put the words in ordergroup_by(word1) |>top_n(5) |>ggplot(aes(word2, n, fill = word1)) +scale_fill_viridis_d() +# set the color palettegeom_col(show.legend =FALSE) +labs(x =NULL, y =NULL, title ="Word following: american and fellow") +facet_wrap(~word1, scales ="free") +coord_flip() +theme_minimal()
Selecting by n
#6 This graph shows across all of the studied Inaugural (n=7) of the most common words that follow either ‘American’ or ‘Fellow’. For the left graph (American), the most common word is ‘people’. For the right graph (Fellow), the most common word is ‘citizens’.
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