LDAvis in r doesn't work: errors and empty output - r

Question

I am a beginner in R and text mining. I have already performed the LDA and now I want to visualise my results with the LDAvis package. I have followed every step from the github example (https://ldavis.cpsievert.me/reviews/reviews.html) starting from the 'visualizing' chapter. However, I either get error notifications or empty pages.
I have tried the following:
RedditResults <- list(phi = phi,
theta = theta,
doc.length = doc.length,
vocab = vocab,
term.frequency = term.frequency)
json <- createJSON(phi = RedditResults$phi,
theta = RedditResults$theta,
doc.length = RedditResults$doc.length,
vocab = RedditResults$vocab,
term.frequency = RedditResults$term.frequency)
serVis(json, out.dir = "vis", open.browser = FALSE)
However, this gives me an error display saying:
Error in cat(list(...), file, sep, fill, labels, append) :
argument 1 (type 'closure') cannot be handled by 'cat'
I reasoned this might have happened because the 'json' object is of class 'function' rather than a character string, which I read the object has to be in to perform serVis. Therefore I tried to convert it before using serVis by means of
RedditResults <- sapply(RedditResults, toJSON)
Resulting in the following error:
Error in run(timeoutMs) :
Evaluation error: argument must be a character vector of length 1.
I feel like I'm making a very obvious mistake somewhere, but after days of trial and error I haven't been able to spot what I should do differently.
The weirdest thing to me is that sometimes it does work, but then when I try to open the html file I only see a blank page. I have tried opening it in multiple browsers as well as opening up those browsers to display local files. I have also tried opening it using the servr package, but this gives me the same result, which is either an error notification (character vector length is not equal to 1) or an empty page.
Hope anyone can spot what I'm doing wrong. Thanks!
EDIT: objects/code underlying the code above:
Convenient to know:
I cleaned the data in corpus form (reddit_data_textcleaned) before converting it to my document-term matrix (tdm3).
After converting it to tdm3, I eliminated any 'empty' documents by excluding those with less than 2 words. Thus, 'reddit_data_textcleaned' contains more documents than relevant and 'tdm3' contains the data I want to work with.
'fit3' is the fitted model resulting from doing LDA on tdm3
'DTM' is the term-document matrix with exactly the same data as tdm3, but with transposed rows/columns.
I am aware that it makes very little sense to call your term-document matrix 'DTM' whilst naming your document-term matrix 'tdm', seeing the abbreviations. Sorry about that.
phi <- as.matrix(posterior(fit3)$terms)
theta <- as.matrix(posterior(fit3)$topics)
dp <- dim(phi) # should be K x W
dt <- dim(theta) # should be D x K
D <- length(as.matrix(tdm3[, 1])) # number of documents (2812)
doc.length <- colSums(as.matrix(DTM)) #number of tokens in each document
N <- sum(doc.length) # total number of tokens in the data (54,136)
vocab <- colnames(phi)# all terms in the vocab
W <- length(vocab) # number of terms in the vocab (6470)
temp_frequency <- inspect(tdm3)
freq_matrix <- data.frame(ST = colnames(temp_frequency),
Freq = colSums(temp_frequency))
rm(temp_frequency)
term.frequency <- freq_matrix$Freq
doc.list <- as.list(reddit_data_textcleaned, "[[:space:]]+")
get.terms <- function(x) {
index <- match(x, vocab)
index <- index[!is.na(index)]
rbind(as.integer(index - 1), as.integer(rep(1, length(index))))
}
documents <- lapply(doc.list, get.terms)
I presume something goes wrong in the creation of the 'get.terms' and 'documents' objects, as I don't exactly know what happens there. I used these methods based on answers to similar questions I read on this platform. Also, the 'doc.list' object still contains the empty documents I removed from the data after converting 'reddit_data_textcleaned' to 'tdm3'. However, the code above doesn't work with a document-term matrix object so that's why I used 'reddit_data_textcleaned' instead of 'tdm3'. I figured I would fix that issue later.