R/word_frequencies_by_category.R
In fahadshery/textsummary: Summarise text, build LDA, tf-idf models, create word clouds and other summary plots
#' @title Return top n word frequencies in a collection of text documents, segmented by categorical variable
#'
#' @description Splits the text verbatim into words, count the number of words by each level in a categorical variable. Either plots or returns the top n words in each level of categorical variable in a whole dataset or collection of text documents.
#' @param df a tidy dataframe/tribble.
#' @param text_col the name of the text column within df
#' @param categories_col the name of the factor/categorical column to calculate the words in each category or level
#' @param number_of_words return a plot/df of single, bigram or trigrams within each category? returns single words in each category by default
#' @param plot return a ggplot2? TRUE by default
#' @param number_of_words_to_plot how many words/terms to plot within each level of categories_col? Plots Top 10 words in each category by default
#' @param clean_text pre-process text? FALSE by default Lammatizes and get rid of extra spaces before and words before counting
#' @name word_frequencies_by_category
#' @rdname word_frequencies_by_category
#' @importFrom magrittr %>%
#' @importFrom rlang .data
#' @export
#' @seealso \code{\link[textSummary]{word_frequencies}}
#' @examples \dontrun{
#' data("text_data")
#' word_frequencies_by_category(verbatim,text_col = text,categories_col = Qtr)
#' word_frequencies_by_category(verbatim,text_col = text,categories_col = Qtr,clean_text = TRUE)
#' word_frequencies_by_category(verbatim,text,Qtr,number_of_words = 2,number_of_words_to_plot = 20)
#' word_frequencies_by_category(verbatim,text,Qtr,clean_text = TRUE,number_of_words = 3)
#'}
word_frequencies_by_category <- function(df,
text_col,
categories_col,
number_of_words = 1,
number_of_words_to_plot = 10,
plot = TRUE,
clean_text = FALSE)
{
## quiets concerns of R CMD check re: the vars's having no visible binding
p <- line_no <- n <- word <- word_total_by_this_category <- total_comments_by_this_category <- term <- word_prop_of_total_comments_by_this_category <- bigram <- bigram_total_by_this_category <- bigram_total_by_this_category <- bigram_prop_of_total_comments_by_this_category <- bigram_total_by_this_category <- trigram <- trigram_total_by_this_category <- trigram_prop_of_total_comments_by_this_category <- word1 <- word2 <- word3 <- NULL
if (number_of_words > 0 & number_of_words < 4) {
categories_col <- rlang::enquo(categories_col)
text_col <- rlang::enquo(text_col)
df <- df %>%
dplyr::group_by(!! categories_col)%>%
dplyr::mutate(line_no = dplyr::row_number()) %>%
dplyr::ungroup()
if (number_of_words == 1) {
all_words <- df %>%
tidytext::unnest_tokens(word, !! text_col, drop = FALSE) %>%
dplyr::filter(!is.na(word))
if (clean_text == TRUE) {
all_words$word <- all_words$word %>%
stringr::str_replace_all('[:space:]',' ')%>%
stringr::str_trim() %>%
# pluralize::singularize()%>%
textstem::lemmatize_words()
}
all_words <- all_words%>%
dplyr::distinct(!! categories_col,line_no, word, .keep_all = TRUE)%>%
dplyr::group_by(!! categories_col,word) %>%
dplyr::mutate(word_total_by_this_category = n()) %>%
dplyr::ungroup()%>%
dplyr::group_by(!! categories_col)%>%
dplyr::mutate(total_comments_by_this_category = max(line_no))%>%
dplyr::mutate(word_prop_of_total_comments_by_this_category =
word_total_by_this_category / total_comments_by_this_category)
if (plot == FALSE) {
return(all_words)
} else{
#calculate and get top n words
top_terms_by_category <- all_words %>%
dplyr::filter(!is.na(word))%>%
dplyr::distinct(!! categories_col,word, .keep_all = TRUE)%>% #dedup before getting top 10 by category_col
dplyr::group_by(!! categories_col) %>%
dplyr::top_n(n = number_of_words_to_plot, wt = word_total_by_this_category)%>%
dplyr::ungroup()
# count the number of words so that each word bar is coloured differently from each other
number_of_top_words <- top_terms_by_category %>% dplyr::summarise(count_all = dplyr::n_distinct(word))
number_of_top_words <- number_of_top_words$count_all
# create BT colour pallet for each word to be plotted on y-axis
my_palette <- rep(c("#6400AA","#E60050","#00A0D6","#E60014","#14AA37","#FFDC00","#333333","#666666",
"#DDDDDD"), length.out = number_of_top_words)
# plot the top n terms
p <- top_terms_by_category %>%
dplyr::mutate(term = drlib::reorder_within(word, word_total_by_this_category, !! categories_col)) %>% #order group within group
ggplot2::ggplot(ggplot2::aes(term, word_prop_of_total_comments_by_this_category, fill = factor(word))) + # plot beta by theme
ggplot2::geom_col(show.legend = FALSE) + # as a bar plot
drlib::scale_x_reordered()+
ggplot2::scale_y_continuous(labels = scales::percent_format()) +
ggplot2::facet_wrap(ggplot2::vars(!!categories_col), scales = "free") + # which each categories_col in a seperate plot
theme_BT()+
ggplot2::scale_fill_manual(values = my_palette)+
ggplot2::labs(title = paste("Top",number_of_words_to_plot,"terms in",rlang::quo_name(categories_col)),x = NULL, y = "% of comments containing the word") + # no x label, change y label
ggplot2::coord_flip() # turn bars sideways
print(p)
}
}
if (number_of_words == 2) {
all_bigrams <- df %>%
tidytext::unnest_tokens(bigram, !! text_col,token = "ngrams", n = 2, drop = FALSE) %>%
dplyr::filter(!is.na(bigram))
if (clean_text == TRUE) {
all_bigrams_seps <- all_bigrams %>%
tidyr::separate(bigram, c("word1","word2"),sep = " ")
all_bigrams_seps$word1 <- all_bigrams_seps$word1 %>%
stringr::str_replace_all('[:space:]',' ')%>%
stringr::str_trim() %>%
# pluralize::singularize()%>%
textstem::lemmatize_words()
all_bigrams_seps$word2 <- all_bigrams_seps$word2 %>%
stringr::str_replace_all('[:space:]',' ')%>%
stringr::str_trim() %>%
# pluralize::singularize()%>%
textstem::lemmatize_words()
all_bigrams <- all_bigrams_seps %>%
tidyr::unite(bigram, word1, word2, sep=" ")
}
all_bigrams <- all_bigrams%>%
dplyr::distinct(!! categories_col,line_no, bigram)%>%
dplyr::group_by(!! categories_col,bigram) %>%
dplyr::mutate(bigram_total_by_this_category = n()) %>%
dplyr::ungroup()%>%
dplyr::group_by(!! categories_col)%>%
dplyr::mutate(total_comments_by_this_category = max(line_no))%>%
dplyr::mutate(bigram_prop_of_total_comments_by_this_category = bigram_total_by_this_category /
total_comments_by_this_category)
if (plot == FALSE) {
return(all_bigrams)
} else{
#calculate and get top n words
top_terms_by_category <- all_bigrams %>%
dplyr::filter(!is.na(bigram))%>%
dplyr::distinct(!! categories_col,bigram, .keep_all = TRUE)%>% #dedup before getting top 10 by categories_col
dplyr::group_by(!! categories_col) %>%
dplyr::top_n(n = number_of_words_to_plot, wt = bigram_total_by_this_category)%>%
dplyr::ungroup()
# count the number of words so that each word bar is coloured differently from each other
number_of_top_words <- top_terms_by_category %>% dplyr::summarise(count_all = dplyr::n_distinct(bigram))
number_of_top_words <- number_of_top_words$count_all
# create BT colour pallet for each word to be plotted on y-axis
my_palette <- rep(c("#6400AA","#E60050","#00A0D6","#E60014","#14AA37","#FFDC00","#333333","#666666",
"#DDDDDD"), length.out = number_of_top_words)
# plot the top n terms
p <- top_terms_by_category %>%
dplyr::mutate(term = drlib::reorder_within(bigram, bigram_total_by_this_category, !! categories_col)) %>% #order group within group
ggplot2::ggplot(ggplot2::aes(term, bigram_prop_of_total_comments_by_this_category, fill = factor(bigram))) + # plot beta by theme
ggplot2::geom_col(show.legend = FALSE) + # as a bar plot
drlib::scale_x_reordered()+
ggplot2::scale_y_continuous(labels = scales::percent_format()) +
ggplot2::facet_wrap(ggplot2::vars(!!categories_col), scales = "free") + # which each group_var in a seperate plot
theme_BT()+
ggplot2::scale_fill_manual(values = my_palette)+
ggplot2::labs(title = paste("Top",number_of_words_to_plot,"terms in",rlang::quo_name(categories_col)),x = NULL, y = "% of verbatim containing the bigram") + # no x label, change y label
ggplot2::coord_flip() # turn bars sideways
print(p)
}
}
if (number_of_words == 3) {
all_trigrams <- df %>%
tidytext::unnest_tokens(trigram, !! text_col,token = "ngrams", n = 3, drop = FALSE) %>%
dplyr::filter(!is.na(trigram))
if (clean_text == TRUE) {
all_trigram_seps <- all_trigrams %>%
tidyr::separate(trigram, c("word1","word2","word3"),sep = " ")
all_trigram_seps$word1 <- all_trigram_seps$word1 %>%
stringr::str_replace_all('[:space:]',' ')%>%
stringr::str_trim() %>%
#pluralize::singularize()%>%
textstem::lemmatize_words()
all_trigram_seps$word2 <- all_trigram_seps$word2 %>%
stringr::str_replace_all('[:space:]',' ')%>%
stringr::str_trim() %>%
# pluralize::singularize()%>%
textstem::lemmatize_words()
all_trigram_seps$word3 <- all_trigram_seps$word3 %>%
stringr::str_replace_all('[:space:]',' ')%>%
stringr::str_trim() %>%
#pluralize::singularize()%>%
textstem::lemmatize_words()
all_trigrams <- all_trigram_seps %>%
tidyr::unite(trigram, word1, word2,word3, sep=" ")
}
all_trigrams <- all_trigrams%>%
dplyr::distinct(!! categories_col,line_no, trigram)%>%
dplyr::group_by(!! categories_col,trigram) %>%
dplyr::mutate(trigram_total_by_this_category = n()) %>%
dplyr::ungroup()%>%
dplyr::group_by(!! categories_col)%>%
dplyr::mutate(total_comments_by_this_category = max(line_no))%>%
dplyr::mutate(trigram_prop_of_total_comments_by_this_category = trigram_total_by_this_category /
total_comments_by_this_category)
if (plot == FALSE) {
return(all_trigrams)
} else{
#calculate and get top n words
top_terms_by_category <- all_trigrams %>%
dplyr::filter(!is.na(trigram))%>%
dplyr::distinct(!! categories_col,trigram, .keep_all = TRUE)%>% #dedup before getting top 10 by categories_col
dplyr::group_by(!! categories_col) %>%
dplyr::top_n(n = number_of_words_to_plot, wt = trigram_total_by_this_category)%>%
dplyr::ungroup()
# count the number of words so that each word bar is coloured differently from each other
number_of_top_words <- top_terms_by_category %>% dplyr::summarise(count_all = dplyr::n_distinct(trigram))
number_of_top_words <- number_of_top_words$count_all
# create BT colour pallet for each word to be plotted on y-axis
my_palette <- rep(c("#6400AA","#E60050","#00A0D6","#E60014","#14AA37","#FFDC00","#333333","#666666",
"#DDDDDD"), length.out = number_of_top_words)
# plot the top n terms
p <- top_terms_by_category %>%
dplyr::mutate(term = drlib::reorder_within(trigram, trigram_total_by_this_category, !! categories_col)) %>% #order group within group
ggplot2::ggplot(ggplot2::aes(term, trigram_prop_of_total_comments_by_this_category, fill = factor(trigram))) + # plot beta by theme
ggplot2::geom_col(show.legend = FALSE) + # as a bar plot
drlib::scale_x_reordered()+
ggplot2::scale_y_continuous(labels = scales::percent_format()) +
theme_BT()+
ggplot2::scale_fill_manual(values = my_palette)+
ggplot2::facet_wrap(ggplot2::vars(!!categories_col), scales = "free") + # which each categories_col in a seperate plot
ggplot2::labs(title = paste("Top",number_of_words_to_plot,"terms in",rlang::quo_name(categories_col)),x = NULL, y = "% of verbatim containing the trigram") + # no x label, change y label
ggplot2::coord_flip() # turn bars sideways
print(p)
}
}
} else
stop("you can ask for single, 2-words and 3-words together only, therefore enter a number between 1-3")
}
fahadshery/textsummary documentation built on May 6, 2019, 7:02 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#' @title Return top n word frequencies in a collection of text documents, segmented by categorical variable
#'
#' @description Splits the text verbatim into words, count the number of words by each level in a categorical variable. Either plots or returns the top n words in each level of categorical variable in a whole dataset or collection of text documents.
#' @param df a tidy dataframe/tribble.
#' @param text_col the name of the text column within df
#' @param categories_col the name of the factor/categorical column to calculate the words in each category or level
#' @param number_of_words return a plot/df of single, bigram or trigrams within each category? returns single words in each category by default
#' @param plot return a ggplot2? TRUE by default
#' @param number_of_words_to_plot how many words/terms to plot within each level of categories_col? Plots Top 10 words in each category by default
#' @param clean_text pre-process text? FALSE by default Lammatizes and get rid of extra spaces before and words before counting
#' @name word_frequencies_by_category
#' @rdname word_frequencies_by_category
#' @importFrom magrittr %>%
#' @importFrom rlang .data
#' @export
#' @seealso \code{\link[textSummary]{word_frequencies}}
#' @examples \dontrun{
#' data("text_data")
#' word_frequencies_by_category(verbatim,text_col = text,categories_col = Qtr)
#' word_frequencies_by_category(verbatim,text_col = text,categories_col = Qtr,clean_text = TRUE)
#' word_frequencies_by_category(verbatim,text,Qtr,number_of_words = 2,number_of_words_to_plot = 20)
#' word_frequencies_by_category(verbatim,text,Qtr,clean_text = TRUE,number_of_words = 3)
#'}
word_frequencies_by_category <- function(df,
text_col,
categories_col,
number_of_words = 1,
number_of_words_to_plot = 10,
plot = TRUE,
clean_text = FALSE)
{
## quiets concerns of R CMD check re: the vars's having no visible binding
p <- line_no <- n <- word <- word_total_by_this_category <- total_comments_by_this_category <- term <- word_prop_of_total_comments_by_this_category <- bigram <- bigram_total_by_this_category <- bigram_total_by_this_category <- bigram_prop_of_total_comments_by_this_category <- bigram_total_by_this_category <- trigram <- trigram_total_by_this_category <- trigram_prop_of_total_comments_by_this_category <- word1 <- word2 <- word3 <- NULL
if (number_of_words > 0 & number_of_words < 4) {
categories_col <- rlang::enquo(categories_col)
text_col <- rlang::enquo(text_col)
df <- df %>%
dplyr::group_by(!! categories_col)%>%
dplyr::mutate(line_no = dplyr::row_number()) %>%
dplyr::ungroup()
if (number_of_words == 1) {
all_words <- df %>%
tidytext::unnest_tokens(word, !! text_col, drop = FALSE) %>%
dplyr::filter(!is.na(word))
if (clean_text == TRUE) {
all_words$word <- all_words$word %>%
stringr::str_replace_all('[:space:]',' ')%>%
stringr::str_trim() %>%
# pluralize::singularize()%>%
textstem::lemmatize_words()
}
all_words <- all_words%>%
dplyr::distinct(!! categories_col,line_no, word, .keep_all = TRUE)%>%
dplyr::group_by(!! categories_col,word) %>%
dplyr::mutate(word_total_by_this_category = n()) %>%
dplyr::ungroup()%>%
dplyr::group_by(!! categories_col)%>%
dplyr::mutate(total_comments_by_this_category = max(line_no))%>%
dplyr::mutate(word_prop_of_total_comments_by_this_category =
word_total_by_this_category / total_comments_by_this_category)
if (plot == FALSE) {
return(all_words)
} else{
#calculate and get top n words
top_terms_by_category <- all_words %>%
dplyr::filter(!is.na(word))%>%
dplyr::distinct(!! categories_col,word, .keep_all = TRUE)%>% #dedup before getting top 10 by category_col
dplyr::group_by(!! categories_col) %>%
dplyr::top_n(n = number_of_words_to_plot, wt = word_total_by_this_category)%>%
dplyr::ungroup()
# count the number of words so that each word bar is coloured differently from each other
number_of_top_words <- top_terms_by_category %>% dplyr::summarise(count_all = dplyr::n_distinct(word))
number_of_top_words <- number_of_top_words$count_all
# create BT colour pallet for each word to be plotted on y-axis
my_palette <- rep(c("#6400AA","#E60050","#00A0D6","#E60014","#14AA37","#FFDC00","#333333","#666666",
"#DDDDDD"), length.out = number_of_top_words)
# plot the top n terms
p <- top_terms_by_category %>%
dplyr::mutate(term = drlib::reorder_within(word, word_total_by_this_category, !! categories_col)) %>% #order group within group
ggplot2::ggplot(ggplot2::aes(term, word_prop_of_total_comments_by_this_category, fill = factor(word))) + # plot beta by theme
ggplot2::geom_col(show.legend = FALSE) + # as a bar plot
drlib::scale_x_reordered()+
ggplot2::scale_y_continuous(labels = scales::percent_format()) +
ggplot2::facet_wrap(ggplot2::vars(!!categories_col), scales = "free") + # which each categories_col in a seperate plot
theme_BT()+
ggplot2::scale_fill_manual(values = my_palette)+
ggplot2::labs(title = paste("Top",number_of_words_to_plot,"terms in",rlang::quo_name(categories_col)),x = NULL, y = "% of comments containing the word") + # no x label, change y label
ggplot2::coord_flip() # turn bars sideways
print(p)
}
}
if (number_of_words == 2) {
all_bigrams <- df %>%
tidytext::unnest_tokens(bigram, !! text_col,token = "ngrams", n = 2, drop = FALSE) %>%
dplyr::filter(!is.na(bigram))
if (clean_text == TRUE) {
all_bigrams_seps <- all_bigrams %>%
tidyr::separate(bigram, c("word1","word2"),sep = " ")
all_bigrams_seps$word1 <- all_bigrams_seps$word1 %>%
stringr::str_replace_all('[:space:]',' ')%>%
stringr::str_trim() %>%
# pluralize::singularize()%>%
textstem::lemmatize_words()
all_bigrams_seps$word2 <- all_bigrams_seps$word2 %>%
stringr::str_replace_all('[:space:]',' ')%>%
stringr::str_trim() %>%
# pluralize::singularize()%>%
textstem::lemmatize_words()
all_bigrams <- all_bigrams_seps %>%
tidyr::unite(bigram, word1, word2, sep=" ")
}
all_bigrams <- all_bigrams%>%
dplyr::distinct(!! categories_col,line_no, bigram)%>%
dplyr::group_by(!! categories_col,bigram) %>%
dplyr::mutate(bigram_total_by_this_category = n()) %>%
dplyr::ungroup()%>%
dplyr::group_by(!! categories_col)%>%
dplyr::mutate(total_comments_by_this_category = max(line_no))%>%
dplyr::mutate(bigram_prop_of_total_comments_by_this_category = bigram_total_by_this_category /
total_comments_by_this_category)
if (plot == FALSE) {
return(all_bigrams)
} else{
#calculate and get top n words
top_terms_by_category <- all_bigrams %>%
dplyr::filter(!is.na(bigram))%>%
dplyr::distinct(!! categories_col,bigram, .keep_all = TRUE)%>% #dedup before getting top 10 by categories_col
dplyr::group_by(!! categories_col) %>%
dplyr::top_n(n = number_of_words_to_plot, wt = bigram_total_by_this_category)%>%
dplyr::ungroup()
# count the number of words so that each word bar is coloured differently from each other
number_of_top_words <- top_terms_by_category %>% dplyr::summarise(count_all = dplyr::n_distinct(bigram))
number_of_top_words <- number_of_top_words$count_all
# create BT colour pallet for each word to be plotted on y-axis
my_palette <- rep(c("#6400AA","#E60050","#00A0D6","#E60014","#14AA37","#FFDC00","#333333","#666666",
"#DDDDDD"), length.out = number_of_top_words)
# plot the top n terms
p <- top_terms_by_category %>%
dplyr::mutate(term = drlib::reorder_within(bigram, bigram_total_by_this_category, !! categories_col)) %>% #order group within group
ggplot2::ggplot(ggplot2::aes(term, bigram_prop_of_total_comments_by_this_category, fill = factor(bigram))) + # plot beta by theme
ggplot2::geom_col(show.legend = FALSE) + # as a bar plot
drlib::scale_x_reordered()+
ggplot2::scale_y_continuous(labels = scales::percent_format()) +
ggplot2::facet_wrap(ggplot2::vars(!!categories_col), scales = "free") + # which each group_var in a seperate plot
theme_BT()+
ggplot2::scale_fill_manual(values = my_palette)+
ggplot2::labs(title = paste("Top",number_of_words_to_plot,"terms in",rlang::quo_name(categories_col)),x = NULL, y = "% of verbatim containing the bigram") + # no x label, change y label
ggplot2::coord_flip() # turn bars sideways
print(p)
}
}
if (number_of_words == 3) {
all_trigrams <- df %>%
tidytext::unnest_tokens(trigram, !! text_col,token = "ngrams", n = 3, drop = FALSE) %>%
dplyr::filter(!is.na(trigram))
if (clean_text == TRUE) {
all_trigram_seps <- all_trigrams %>%
tidyr::separate(trigram, c("word1","word2","word3"),sep = " ")
all_trigram_seps$word1 <- all_trigram_seps$word1 %>%
stringr::str_replace_all('[:space:]',' ')%>%
stringr::str_trim() %>%
#pluralize::singularize()%>%
textstem::lemmatize_words()
all_trigram_seps$word2 <- all_trigram_seps$word2 %>%
stringr::str_replace_all('[:space:]',' ')%>%
stringr::str_trim() %>%
# pluralize::singularize()%>%
textstem::lemmatize_words()
all_trigram_seps$word3 <- all_trigram_seps$word3 %>%
stringr::str_replace_all('[:space:]',' ')%>%
stringr::str_trim() %>%
#pluralize::singularize()%>%
textstem::lemmatize_words()
all_trigrams <- all_trigram_seps %>%
tidyr::unite(trigram, word1, word2,word3, sep=" ")
}
all_trigrams <- all_trigrams%>%
dplyr::distinct(!! categories_col,line_no, trigram)%>%
dplyr::group_by(!! categories_col,trigram) %>%
dplyr::mutate(trigram_total_by_this_category = n()) %>%
dplyr::ungroup()%>%
dplyr::group_by(!! categories_col)%>%
dplyr::mutate(total_comments_by_this_category = max(line_no))%>%
dplyr::mutate(trigram_prop_of_total_comments_by_this_category = trigram_total_by_this_category /
total_comments_by_this_category)
if (plot == FALSE) {
return(all_trigrams)
} else{
#calculate and get top n words
top_terms_by_category <- all_trigrams %>%
dplyr::filter(!is.na(trigram))%>%
dplyr::distinct(!! categories_col,trigram, .keep_all = TRUE)%>% #dedup before getting top 10 by categories_col
dplyr::group_by(!! categories_col) %>%
dplyr::top_n(n = number_of_words_to_plot, wt = trigram_total_by_this_category)%>%
dplyr::ungroup()
# count the number of words so that each word bar is coloured differently from each other
number_of_top_words <- top_terms_by_category %>% dplyr::summarise(count_all = dplyr::n_distinct(trigram))
number_of_top_words <- number_of_top_words$count_all
# create BT colour pallet for each word to be plotted on y-axis
my_palette <- rep(c("#6400AA","#E60050","#00A0D6","#E60014","#14AA37","#FFDC00","#333333","#666666",
"#DDDDDD"), length.out = number_of_top_words)
# plot the top n terms
p <- top_terms_by_category %>%
dplyr::mutate(term = drlib::reorder_within(trigram, trigram_total_by_this_category, !! categories_col)) %>% #order group within group
ggplot2::ggplot(ggplot2::aes(term, trigram_prop_of_total_comments_by_this_category, fill = factor(trigram))) + # plot beta by theme
ggplot2::geom_col(show.legend = FALSE) + # as a bar plot
drlib::scale_x_reordered()+
ggplot2::scale_y_continuous(labels = scales::percent_format()) +
theme_BT()+
ggplot2::scale_fill_manual(values = my_palette)+
ggplot2::facet_wrap(ggplot2::vars(!!categories_col), scales = "free") + # which each categories_col in a seperate plot
ggplot2::labs(title = paste("Top",number_of_words_to_plot,"terms in",rlang::quo_name(categories_col)),x = NULL, y = "% of verbatim containing the trigram") + # no x label, change y label
ggplot2::coord_flip() # turn bars sideways
print(p)
}
}
} else
stop("you can ask for single, 2-words and 3-words together only, therefore enter a number between 1-3")
}
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.