black_tweets <- rtweet::get_timeline("evilbmcats")
black_tweets <- dplyr::filter(black_tweets, is.na(reply_to_user_id), !is_retweet, !is_quote)
black_tweets <- dplyr::select(black_tweets, text, created_at, status_id)
black_tweets <- dplyr::mutate(black_tweets, text = stringr::str_replace(text, "https.*", ""))
readr::write_csv(black_tweets, path = "data/2018-01-03-bubblegumpuppies_cats.csv")

library("tidytext")
library("magrittr")
bing <- get_sentiments("bing")

sentiment <- dplyr::select(black_tweets, text) %>%
  dplyr::mutate(saved_text = text) %>%
  unnest_tokens(word, text) %>%
  dplyr::inner_join(bing, by = "word") %>%
  dplyr::filter(sentiment == "negative")

dplyr::filter(black_tweets, !text %in% sentiment$saved_text) %>%
  head() %>%
  knitr::kable()

library("cleanNLP")
init_spaCy()

get_token_with_text <- function(x){
  obj <- run_annotators(x, as_strings = TRUE)
  entities <- get_token(obj)
  entities$text <- x
  entities
}

possibly_get_tokens <- purrr::possibly(get_token_with_text,
                                         otherwise = NULL)

tokens <- purrr::map_df(sentiment$saved_text, possibly_get_tokens)
head(tokens) %>%
  knitr::kable()

tokens <- dplyr::mutate(tokens, word = enc2native(word))
tokens <- dplyr::mutate(tokens, word = tolower(word))
tokens <- dplyr::left_join(sentiment, tokens, by = c("saved_text" = "text", "word"))
head(tokens) %>% knitr::kable()

modifiers <- tibble::tibble(pos = c( "NN",  "VBG", "JJ", 
                                     "VBD", "VB",  "NNS", 
                                     "VBP", "VBN", "NNP", "VBZ"),
                            modifier = c("love", "${adjective}", "${adjective}",
                                         "loved", "love", "lovers",
                                         "love", "${adjective}",
                                         "love", "loves"))
knitr::kable(modifiers)

# praise has some randomness, let's make it reproducible
set.seed(42)

modifiable_tweets <- dplyr::left_join(tokens, black_tweets, 
                                      by = c("saved_text" = "text"))

modifiable_tweets <- dplyr::left_join(modifiable_tweets, modifiers, 
                                      by = "pos")

# for easier use with map
replace_all <- function(pattern, replacement, x){
  stringr::str_replace_all(x, pattern = pattern,
                           replacement = replacement)
}

modified_tweets <- dplyr::group_by(modifiable_tweets, saved_text) %>%
  dplyr::summarize(praise_template = purrr::map2_chr(word, modifier, replace_all,
                                                  x = tolower(saved_text[1]))[1],
                   praise_template = paste("${exclamation}!", praise_template),
                   praise_template = stringr::str_replace_all(praise_template,
                                                              "hate", "love"),
                   praise_template = stringr::str_replace_all(praise_template,
                                                              "evil", "love"),
                   new_text = praise::praise(praise_template)) 

modified_tweets <- dplyr::select(modified_tweets, saved_text, new_text)

puppies_and_cats <- dplyr::left_join(modified_tweets, black_tweets, by = c("saved_text" = "text"))
head(puppies_and_cats) %>% knitr::kable()

library("rvest")
library("RSelenium")
library("magrittr")
# https://stackoverflow.com/questions/29861117/r-rvest-scraping-a-dynamic-ecommerce-page
rD <- rsDriver()
remDr <- rD[["client"]]


# open the webpage
remDr$navigate("https://www.pexels.com/search/puppies/")

# scroll down
for(i in 1:30){      
  remDr$executeScript(paste("scroll(0,",i*10000,");"),
                      args = list("dummy"))
  # be nice and wait
  Sys.sleep(1)    
}

page_content <- remDr$getPageSource() 
remDr$close()

# functiosn for getting the pic links
get_link_from_src <- function(node){
  xml2::xml_attrs(node)["src"] %>%
    as.character() %>%
    stringr::str_replace("\\?h.*", "")
  
}

xtract_pic_links <- function(source) {
  css <- '.photo-item__img'
  read_html(source[[1]]) %>%
    html_nodes(css) %>%
    purrr::map_chr(get_link_from_src)    
}

links <- xtract_pic_links(page_content)
links <- links[1:nrow(puppies_and_cats)]

# save
dir.create("data/puppies")
save_pic <- function(url, name){
  Sys.sleep(1)
  name <- paste0("puppy", name, ".png")
  
  try(magick::image_read(url) %>%
        magick::image_write(paste0("data/puppies/", name)),
      silent = TRUE)
}

purrr::walk2(links, 1:nrow(puppies_and_cats), save_pic)

resize <- function(pic){
  magick::image_read(pic) %>%
    magick::image_resize("300x300") %>%
    magick::image_write(pic)
}
purrr::walk(dir("data/puppies/", full.names = TRUE), resize)

post_a_reply <- function(df, pic){
  rtweet::post_tweet(paste("@evilbmcats", df$new_text), in_reply_to_status_id = df$status_id,
                     media = pic)
  Sys.sleep(1)
}

pics <- dir("data/puppies/", full.names = TRUE)
purrr::walk2(split(puppies_and_cats, puppies_and_cats$status_id), pics,
            post_a_reply)

save_one_tweet <- function(status_id){
  rtweet::tweet_shot(status_id) %>%
    magick::image_write(paste0("data/2018-01-04-",
                               status_id, ".png"))
}

c("948942337912328192",
  "948942293272334336",
  "948942240713531392") %>%
  purrr::walk(save_one_tweet)