Christmas Movies

rstats
wordcloud
interactive
personalproject
christmas
Author

Jisell Howe

Published

December 25, 2022

Link to code on GitHub.

Show the code 
library(ggplot2)
library(tidyverse)
library(tm)
library(dplyr)
library(wordcloud)
library(plotly)
#library(rio)

###data source
#url <- 'https://www.kaggle.com/datasets/jonbown/christmas-movies?select=christmas_movies.csv'


#url <- rio::import(file = url,which = 1)
#df <- url

df <- read.csv('christmas_movies.csv')

#clean data
df <- df %>% filter(!is.na(description))
df$release_year <- as.numeric(df$release_year)
description_vector <- as.character(df$description)
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#remove punctuation and numbers from description column
description_vector <- gsub("[[:punct::]]", "", description_vector)
description_vector <- gsub("[[:digit:]]", "", description_vector)


#remove punctuation
description_vector <- gsub("[!.!»?,-]","", description_vector)

#create a Corpus object
description_corpus <- Corpus(VectorSource(description_vector))

#create tokens from individual words and phrases in description colum n
#make everything lowercase
description_corpus <- tm_map(description_corpus, content_transformer(tolower))

#remove stopwords
description_corpus <- tm_map(description_corpus, removeWords, stopwords("english"))


#remove a list of words
description_corpus <- tm_map(description_corpus, removeWords, c("the", "an", "a", "must", "and", "could", "would", "have", "set", "exists", "turn", "tale", "might", "else", "causing", "whose", "whos", "sets","put", "...", "always", "gets", "before","very", "loosely","with","various","very","into","bearing", "behind", "predictably", "follows", "eight","frantic", "tales", "lives", "dealing","eightyearold", "inadvertently", "finds", "thing", "take", "taken", "tries", "several", "kevin", "mccallister", "become", "commanding", "like", "three", "former", "general", "cynical", "griswold", "walter", "desparately", "frustrated", "never", "sent", "unfortunately", "becoming", "left", "becomes", "really", "begins", "marie", "miranda", "four", "seemingly", "meaning", "emily", "something", "also", "actually", "early", "despises", "thinks", "wagner", "jill", "mark", "appear", "reluctantly", "decided", "fending", "indulge", "wants", "matt", "meet", "comes", "going", "best", "think", "leaving", "thought", "approaching", "still", "heading", "picked", "decides", "seven", "shows", "learns", "lucas", "agrees", "happens", "robin", "mary", "jacob", "laurel", "facing", "quickly", "shocking", "peter", "mariah", "everett", "sebastian", "roped", "austin", "jokingly", "timothy", "emmanuel", "attends", "carlton", "mcandrick", "sarah", "palmer", "christmas", "christmastime", "summary", "»"," »", "«", " «","tasked", "helps", "things", "make", "keep", "falls", "makes", "another", "will", "come", "spend", "meet", "meets", "goes", "bring", "full", "takes", "just", "decide", "unexpectedly", "help", "show", "years", "find", "back", "york", "plot", "claus", "holiday", "young", "season", "time", "year", "small", "little", "girl", "woman", "man", "true", "plan", "plans", "lizzie", "virginia", "richfield", "pull", "begin", "foxworth", "maggie", "harper","whole", "current", "starts", "name", "zeus", "amalie", "hess", "brings", "receives","need", "pole", "place", "others", "plaza","existed","desparately","bumbling", "vermont", "adam", "calvin","langton", "anymore", "expect", "massey", "kyla", "cordinia","isadora", "leopold", "jonna", "nick", "julie", "walshrick", "mgtow", "briana", "laura", "kylie", "chloe", "evan", "third","noelle","lets", "serving", "even", "anna", "galwick", "chronicles", "story", "reel"))

#remove white space
description_corpus <- tm_map(description_corpus, stripWhitespace)
Show the code 
#determining themes

matrix <- TermDocumentMatrix(description_corpus)
matrix1 <- as.matrix(matrix)
matrix_df <- as.data.frame(matrix1)

freq <- head(matrix_df, n = 1000)


sorted_matrix_df <- matrix_df[order(freq), ]

top_100 <- sorted_matrix_df %>%
  head(100)

#top_100[0]
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term_freq <- data.frame(term = colnames(matrix_df), freq = colSums(matrix_df))

colors <- ifelse(term_freq$freq > 2, "#CDD9CC", "#BF1111")
p <- wordcloud(description_corpus, min.freq = 10, colors = colors)

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#title("Most Popular Christmas Movie Themes")
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min_imdb <- min(df$imdb_rating)
max_imdb <- max(df$imdb_rating)

palette <- "#AEDFF2"

p <- ggplot(df, aes(x = title, y = imdb_rating)) +
  geom_segment(aes(
                  x = title, 
                  xend = title, 
                  y = 0, 
                  yend = imdb_rating), size = 1, alpha = 0.5, show.legend = FALSE, color = palette) +
  geom_point(size = 2, color = "#4AB0D9", shape = 8) +
  scale_y_reverse() +
  labs(title = "Christmas Movie Ratings") + 
  labs(subtitle = "Data Source: Kaggle. Retrieved from: https://www.kaggle.com/datasets/jonbown/christmas-movies. Graphic: Jisell Howe") +
  theme(axis.ticks.x = element_blank(),
          axis.text.x = element_blank(),
          axis.title.x = element_blank(),
          axis.title.y = element_blank(),
          legend.position="none") +
  theme(plot.title = element_text(family = "Georgia", 
                                  face = "bold",    
                                  color = "#233A59",
                                  size = 20,
                                  hjust = 0,
                                  vjust = 1,
                                  lineheight = 1,
                                  margin = margin(0, 0, 0, 0)))

p <- ggplotly(p)


p %>%
  layout(dragmode = FALSE,
    hoverlabel = list(
      font = list(size = 15, family = "Georgia", color = "#233A59"),
      bgcolor = "#F2F2F2",
      bordercolor = "#233A59",
      borderwidth = 1
    )
  )

Data Source: Kaggle. Graphic: Jisell Howe