# Load necessary libraries
library(ggplot2)
library(wordcloud)
## Loading required package: RColorBrewer
library(tm)
## Loading required package: NLP
##
## Attaching package: 'NLP'
## The following object is masked from 'package:ggplot2':
##
## annotate
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(Matrix)
# Load data
blogs <- readLines("en_US.blogs.txt", encoding = "UTF-8")
twitter <- readLines("en_US.twitter.txt", encoding = "UTF-8")
## Warning in readLines("en_US.twitter.txt", encoding = "UTF-8"): line 167155
## appears to contain an embedded nul
## Warning in readLines("en_US.twitter.txt", encoding = "UTF-8"): line 268547
## appears to contain an embedded nul
## Warning in readLines("en_US.twitter.txt", encoding = "UTF-8"): line 1274086
## appears to contain an embedded nul
## Warning in readLines("en_US.twitter.txt", encoding = "UTF-8"): line 1759032
## appears to contain an embedded nul
news <- readLines("en_US.news.txt", encoding = "UTF-8")
## Warning in readLines("en_US.news.txt", encoding = "UTF-8"): incomplete final
## line found on 'en_US.news.txt'
# Sample the data to reduce size
set.seed(123)
blogs_sample <- sample(blogs, 10000)
twitter_sample <- sample(twitter, 10000)
news_sample <- sample(news, 10000)
# Combine samples
all_text <- c(blogs_sample, twitter_sample, news_sample)
# Create a corpus
corpus <- Corpus(VectorSource(all_text))
corpus <- tm_map(corpus, content_transformer(tolower))
## Warning in tm_map.SimpleCorpus(corpus, content_transformer(tolower)):
## transformation drops documents
corpus <- tm_map(corpus, removePunctuation)
## Warning in tm_map.SimpleCorpus(corpus, removePunctuation): transformation drops
## documents
corpus <- tm_map(corpus, removeNumbers)
## Warning in tm_map.SimpleCorpus(corpus, removeNumbers): transformation drops
## documents
corpus <- tm_map(corpus, removeWords, stopwords("en"))
## Warning in tm_map.SimpleCorpus(corpus, removeWords, stopwords("en")):
## transformation drops documents
# Create a term-document matrix
dtm <- TermDocumentMatrix(corpus, control = list(wordLengths = c(1, Inf)))
# Calculate word frequencies directly from the sparse matrix
word_freq <- slam::row_sums(dtm)
word_freq_df <- data.frame(word = names(word_freq), freq = word_freq)
# Visualization
wordcloud(words = word_freq_df$word, freq = word_freq_df$freq, min.freq = 50, random.order = FALSE)
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# Plot line counts
line_counts <- data.frame(
File = c("Blogs", "Twitter", "News"),
Lines = c(length(blogs), length(twitter), length(news))
)
ggplot(line_counts, aes(x = File, y = Lines)) +
geom_bar(stat = "identity") +
ggtitle("Line Counts per File")
# Plot file sizes
file_sizes <- data.frame(
File = c("Blogs", "Twitter", "News"),
Size_MB = c(200, 159, 196)
)
ggplot(file_sizes, aes(x = File, y = Size_MB)) +
geom_bar(stat = "identity") +
ggtitle("File Sizes in MB")
# Plot longest lines
longest_lines <- data.frame(
File = c("Blogs", "Twitter", "News"),
Longest_Line = c(max(nchar(blogs)), max(nchar(twitter)), max(nchar(news)))
)
ggplot(longest_lines, aes(x = File, y = Longest_Line)) +
geom_bar(stat = "identity") +
ggtitle("Longest Line Length per File")
