R/data_manipulation.R
In szymanskir/HackerTracker: An Amazing Shiny App
Defines functions
calculate_sentiment
calculate_word_frequencies
clean_sentences
remove_html
remove_stop_words
comments_to_graph_data
Documented in
calculate_sentiment
calculate_word_frequencies
clean_sentences
remove_html
remove_stop_words
#' Calculates the sentiment values for each sentences on by one
#'
#' @param sentences vector of strings for which the sentiment values
#' should be calculated
#'
#' @return vector of sentiment values corresponding to the inout
#' sentences
#'
#' @import dplyr
#' @import stringr
#' @import tidyr
#' @import tidytext
#'
calculate_sentiment <- function(sentences) {
sentiment_index <- get_sentiments("bing")
calculate_single_sentence_sentiment <- function(sentence) {
words <- sentence %>%
clean_sentences() %>%
remove_stop_words()
words_df <- data.frame(word = words, stringsAsFactors = FALSE)
words_df %>%
inner_join(sentiment_index, by = "word") %>%
mutate(sentiment = factor(sentiment, levels = c("positive", "negative"))) %>%
count(word, sentiment, .drop = FALSE) %>%
spread(sentiment, n, fill = 0) %>%
mutate(sentiment_score = positive - negative) %>%
summarize(total_sentiment_score = sum(sentiment_score)) %>%
pull(total_sentiment_score)
}
sapply(sentences, calculate_single_sentence_sentiment, USE.NAMES = FALSE)
}
#' Calculates the global frequencies of words in the given sentences
#'
#' @param sentences vector of strings for which the word frequencies
#' should be calculated
#'
#' @return data.frame with word frequencies
#'
#' @import stringr
#'
calculate_word_frequencies <- function(sentences) {
words <- sentences %>%
clean_sentences() %>%
remove_stop_words()
table(words) %>%
as.data.frame()
}
#' Cleans input senteces from html, converts to lower
#' characters and simplifies white spaces.
#'
#' @param sentences vector of input sentences
#'
#' @return vector of words
#'
#' @import stringr
#'
clean_sentences <- function(sentences) {
sentences %>%
remove_html() %>%
str_to_lower() %>%
str_replace_all('[^A-Z|a-z]', ' ') %>%
str_replace_all('\\s\\s*', ' ') %>%
str_split(' ', simplify = TRUE)
}
#' Removes html elements from the input string
#'
#' @param texts vector of input string to remove html from
#'
#' @return vector of strings with removed html elements
#'
#' @importFrom rvest html_text
#' @importFrom xml2 read_html
#'
remove_html <- function(texts) {
html_strings <- sprintf("<body>%s<body>", texts)
sapply(html_strings, function(html_string) {
html_string %>%
read_html() %>%
html_text()
}, USE.NAMES = FALSE)
}
#' Removes stop words from the given vector of words
#'
#' @param words vector of words from which stop words
#' should be removed
#'
#' @return vector of words with stop_words removed
#'
remove_stop_words <- function(words) {
stop_words <- tidytext::stop_words
words[!words %in% c("", " ", stop_words$word)]
}
comments_to_graph_data <- function(comments) {
color_palette <- grDevices::colorRampPalette(c('red','blue'))
root_node <- setdiff(comments$parent, comments$id) %>%
unique()
nodes <- data.frame(
id = comments$id,
label = comments$by,
color = color_palette(10)[as.numeric(cut(comments$sentiment,breaks = 10))],
stringsAsFactors = FALSE
)
nodes$size <- 1
nodes <- rbind(nodes, data.frame(id = root_node, size = 2, color = -10, label = "root", stringsAsFactors = FALSE))
edges <- data.frame(
id = seq_len(nrow(comments)),
source = comments$parent,
target = comments$id,
stringsAsFactors = FALSE
)
list(
nodes = nodes,
edges = edges
)
}
szymanskir/HackerTracker documentation built on Jan. 24, 2020, 12:12 a.m.
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Defines functions calculate_sentiment calculate_word_frequencies clean_sentences remove_html remove_stop_words comments_to_graph_data
Documented in calculate_sentiment calculate_word_frequencies clean_sentences remove_html remove_stop_words
#' Calculates the sentiment values for each sentences on by one
#'
#' @param sentences vector of strings for which the sentiment values
#' should be calculated
#'
#' @return vector of sentiment values corresponding to the inout
#' sentences
#'
#' @import dplyr
#' @import stringr
#' @import tidyr
#' @import tidytext
#'
calculate_sentiment <- function(sentences) {
sentiment_index <- get_sentiments("bing")
calculate_single_sentence_sentiment <- function(sentence) {
words <- sentence %>%
clean_sentences() %>%
remove_stop_words()
words_df <- data.frame(word = words, stringsAsFactors = FALSE)
words_df %>%
inner_join(sentiment_index, by = "word") %>%
mutate(sentiment = factor(sentiment, levels = c("positive", "negative"))) %>%
count(word, sentiment, .drop = FALSE) %>%
spread(sentiment, n, fill = 0) %>%
mutate(sentiment_score = positive - negative) %>%
summarize(total_sentiment_score = sum(sentiment_score)) %>%
pull(total_sentiment_score)
}
sapply(sentences, calculate_single_sentence_sentiment, USE.NAMES = FALSE)
}
#' Calculates the global frequencies of words in the given sentences
#'
#' @param sentences vector of strings for which the word frequencies
#' should be calculated
#'
#' @return data.frame with word frequencies
#'
#' @import stringr
#'
calculate_word_frequencies <- function(sentences) {
words <- sentences %>%
clean_sentences() %>%
remove_stop_words()
table(words) %>%
as.data.frame()
}
#' Cleans input senteces from html, converts to lower
#' characters and simplifies white spaces.
#'
#' @param sentences vector of input sentences
#'
#' @return vector of words
#'
#' @import stringr
#'
clean_sentences <- function(sentences) {
sentences %>%
remove_html() %>%
str_to_lower() %>%
str_replace_all('[^A-Z|a-z]', ' ') %>%
str_replace_all('\\s\\s*', ' ') %>%
str_split(' ', simplify = TRUE)
}
#' Removes html elements from the input string
#'
#' @param texts vector of input string to remove html from
#'
#' @return vector of strings with removed html elements
#'
#' @importFrom rvest html_text
#' @importFrom xml2 read_html
#'
remove_html <- function(texts) {
html_strings <- sprintf("<body>%s<body>", texts)
sapply(html_strings, function(html_string) {
html_string %>%
read_html() %>%
html_text()
}, USE.NAMES = FALSE)
}
#' Removes stop words from the given vector of words
#'
#' @param words vector of words from which stop words
#' should be removed
#'
#' @return vector of words with stop_words removed
#'
remove_stop_words <- function(words) {
stop_words <- tidytext::stop_words
words[!words %in% c("", " ", stop_words$word)]
}
comments_to_graph_data <- function(comments) {
color_palette <- grDevices::colorRampPalette(c('red','blue'))
root_node <- setdiff(comments$parent, comments$id) %>%
unique()
nodes <- data.frame(
id = comments$id,
label = comments$by,
color = color_palette(10)[as.numeric(cut(comments$sentiment,breaks = 10))],
stringsAsFactors = FALSE
)
nodes$size <- 1
nodes <- rbind(nodes, data.frame(id = root_node, size = 2, color = -10, label = "root", stringsAsFactors = FALSE))
edges <- data.frame(
id = seq_len(nrow(comments)),
source = comments$parent,
target = comments$id,
stringsAsFactors = FALSE
)
list(
nodes = nodes,
edges = edges
)
}
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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