rtweet<\/code> to grab the timeline, count the characters per-tweet and look up the results. I posted the results of said process to — of course — Twitter and some folks asked me for the code.<\/p>\nOthers used it and there were some discussions as to why timelines looked similar (distribution-wise) with not many Tweets going over 140 characters. One posit I had was that it might be due to client-side limitations since I noted that Twitter for macOS — a terrible client they haven’t updated in ages (but there really aren’t any good ones) — still caps tweets at 140 characters. Others, like Buffer on the web, do have support for 280, so I modified the code a bit to look at the distribution by client.<\/p>\n
Rather than bore you with my own timeline analysis, and to help the results be a tad more reproducible (which was another discussion that spawned from the tweet-length tweet), here’s a bit of code that tries to grab the last 3,000 tweets with the #rstats<\/code> hashtag and plots the distribution by Twitter client:<\/p>\nlibrary(rtweet)\r\nlibrary(ggalt)\r\nlibrary(rprojroot)\r\nlibrary(hrbrthemes)\r\nlibrary(tidyverse)\r\n\r\nrt <- find_rstudio_root_file()\r\nrstats_tweet_data_file <- file.path(rt, "data", "2017-11-13-rstats-tweet-search-results.rds")\r\n\r\nif (!file.exists(rstats_tweet_data_file)) {\r\n rstats <- search_tweets("#rstats", 3000) # setting up rtweet is an exercise left to the reader\r\n write_rds(rstats, rstats_tweet_data_file)\r\n} else {\r\n rstats <- read_rds(rstats_tweet_data_file)\r\n}\r\n\r\nrstats <- mutate(rstats, tweet_length=map_int(text, nchar)) # get the tweet length for each tweet\r\n\r\ncount(rstats, source) %>%\r\n filter(n > 5) -> usable_sources # need data for density + I wanted a nice grid :-)\r\n\r\n# We want max tweet length & total # of tweets for sorting & labeling facets\r\nfilter(rstats, source %in% usable_sources$source) %>%\r\n group_by(source) %>%\r\n summarise(max=max(tweet_length), n=n()) %>%\r\n arrange(desc(max)) -> ordr\r\n\r\n# four breaks per panel regardless of the scales (we're using free-y scales)\r\nthere_are_FOUR_breaks <- function(limits) { seq(limits[1], limits[2], length.out=4) }\r\n\r\nmutate(rstats) %>%\r\n filter(source %in% usable_sources$source) %>%\r\n mutate(source = factor(source, levels=ordr$source,\r\n labels=sprintf("%s (n=%s)", ordr$source, ordr$n))) %>%\r\n ggplot(aes(tweet_length)) +\r\n geom_bkde(aes(color=source, fill=source), bandwidth=5, alpha=2\/3) +\r\n geom_vline(xintercept=140, linetype="dashed", size=0.25) +\r\n scale_x_comma(breaks=seq(0,280,70), limits=c(0,280)) +\r\n scale_y_continuous(breaks=there_are_FOUR_breaks, expand=c(0,0)) +\r\n facet_wrap(~source, scales="free", ncol=5) +\r\n labs(x="Tweet length", y="Density",\r\n title="Tweet length distributions by Twitter client (4.5 days #rstats)",\r\n subtitle="Twitter client facets in decreasing order of ones with >140 length tweets",\r\n caption="NOTE free Y axis scales\\nBrought to you by #rstats, rtweet & ggalt") +\r\n theme_ipsum_rc(grid="XY", strip_text_face="bold", strip_text_size=8, axis_text_size=7) +\r\n theme(panel.spacing.x=unit(5, "pt")) +\r\n theme(panel.spacing.y=unit(5, "pt")) +\r\n theme(axis.text.x=element_text(hjust=c(0, 0.5, 0.5, 0.5, 1))) +\r\n theme(axis.text.y=element_blank()) +\r\n theme(legend.position="none")<\/code><\/pre>\n
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