#TidyTuesday horror movie 😱 descriptions

horror.md

library(tidyverse)
library(tidytext)
library(ggraph)
library(igraph)
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#> Attaching package: 'igraph'
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library(widyr)

horror_movies <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-11-01/horror_movies.csv')
#> Rows: 32540 Columns: 20
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr  (10): original_title, title, original_language, overview, tagline, post...
#> dbl   (8): id, popularity, vote_count, vote_average, budget, revenue, runtim...
#> lgl   (1): adult
#> date  (1): release_date
#> 
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horror_tidy <- 
    horror_movies %>%
    unnest_tokens(word, overview) %>%
    anti_join(get_stopwords())
#> Joining, by = "word"

words_cooccur <- 
    horror_tidy %>%
    group_by(word) %>%
    filter(n() > 300) %>%
    ungroup() %>%
    pairwise_cor(word, id, sort = TRUE, upper = FALSE) %>%
    filter(correlation > 0.15)
    
word_counts <- 
    horror_tidy %>%
    count(word, sort = TRUE) %>%
    filter(word %in% words_cooccur$item1 | word %in% words_cooccur$item2)

set.seed(2021)
words_cooccur %>%
    graph_from_data_frame(vertices = word_counts) %>%
    ggraph(layout = "nicely") +
    geom_edge_link(aes(edge_alpha = correlation),
                   edge_width = 0.5, show.legend = FALSE) +
    geom_node_point(aes(size = n), color = "midnightblue", alpha = 0.8) +
    geom_node_text(aes(label = name), repel = TRUE, family = "IBMPlexSans") +
    theme_graph(base_family = "IBMPlexSans-Bold") +
    theme(legend.position = "none") +
    labs(title = "What words are used to describe horror movies?",
         subtitle = "Common words that are highly correlated in #TidyTuesday horror movie overviews")

^{Created on 2022-11-03 with reprex v2.0.2}