Sharla Gelfand

January 2019: Tweets

For the first post in my Me, me, me, 2019 series, I figured I’d do something that is especially topical: tweets. Spoiler alert, but in January my tweeting was at an all time high, absolutely exacerbated by RStudio conf.

As I have before, I’m using the awesome rtweet package, created by Mike Kearney. I finally met Mike IRL at the conference and didn’t even fan over him thank him for rtweet 😱.

I’ll start by reading in my tweets. I have ~3000 overall, so 2500 should be enough for what I’m trying to do.

my_tweets_raw <- get_timeline(user = "sharlagelfand",
                              n = 2500)

There is an absolute ton of information attached to the tweets, so I’m only keeping what I’ll use.

my_tweets <- my_tweets_raw %>%
  select(status_id, created_at, text, is_retweet, reply_to_screen_name, favorite_count,
         retweet_count, hashtags, mentions_screen_name)

The tweets come, by default, in UTC, so I’ll set them to my timezone, keep ones from 2018 onwards, and add some additonal date columns for use later on.

my_tweets <- my_tweets %>%
  mutate(created_at = with_tz(created_at, tzone = "America/Toronto")) %>%
  filter(created_at >= "2018-01-01") %>%
            funs(date = date, 
                 month = floor_date(., unit = "month"))) %>%
  mutate_at(vars(date, month), as.Date)

Finally, before looking at January’s tweets, I’m deriving the tweet_type variable so that information about retweets, replies, and original tweets is all in one variable. To keep it simple, if a tweet has data in the reply_to_screen_name field and it’s not tto myself, I’m counting it as a reply. I like to thread too much to assume a tweet replying to myself is actually part of a conversation with n > 0 other people 😬.


my_tweets <- my_tweets %>%
  mutate(tweet_type = case_when(is_retweet ~ "Retweet",
                                ! & reply_to_screen_name != "sharlagelfand" ~ "Reply",
                                TRUE ~ "Original"),
         tweet_type = fct_relevel(as_factor(tweet_type),
                                  c("Original", "Reply", "Retweet"))) %>%

Now it’s time to look at my tweets 🐦 for the last month! I will look at all of the rest later on, I promise.

january_tweets <- my_tweets %>%
  filter(month == "2019-01-01")

In January 2019, I tweeted a total of 396 times, including original tweets, replies, and retweets. The breakdown is as follows:


january_tweets %>%
  ggplot(aes(x = tweet_type,
             fill = tweet_type)) +
  geom_histogram(stat = "count") + 
  coord_flip() + 
  theme(legend.position = "none",
        axis.title = element_blank(),
        axis.text = element_text(size = 10))

Broken down daily, it looks a little something like this…

january_tweets %>%
  ggplot(aes(x = date,
             fill = tweet_type)) +
  geom_histogram(binwidth = 1) + 
  theme(legend.position = "bottom",
        axis.title = element_blank(),
        axis.text = element_text(size = 10),
        legend.title = element_blank())

The conference ran from January 15 - 19 (if you include workshops and the tidyverse dev day, which I did), and you can see absolutely huge increases in tweeting during that time. Aaand I guess during the work week since I’ve been back 🙈.

It wouldn’t be analyzing the self if I didn’t look at how ~popular I am, right? So let’s check how many likes and retweets I’ve gotten over the last month.


january_tweets %>%
  filter(tweet_type != "Retweet") %>%
  group_by(date) %>%
  summarise_at(vars(favorite_count, retweet_count), sum) %>%
  gather(measure, count, ends_with("count")) %>%
  mutate(measure = str_replace(measure, "_count", "")) %>%
  ggplot(aes(x = date,
             y = count,
             colour = measure)) + 
  geom_line() + 
  scale_x_discrete("Date of original tweet") + 
  theme(legend.position = "bottom",
        axis.title.y = element_blank(),
        axis.text = element_text(size = 10),
        legend.title = element_blank())

I really am not normally this hype, I promise. I know what my top-favourited tweet is and it is absolutely absurd.

january_tweets %>% 
  filter(tweet_type != "Retweet") %>%
  filter(favorite_count == max(favorite_count)) %>%
  pull(status_id) %>%
  blogdown::shortcode("tweet", .)

But, honestly, enough about me. As you can see, I replied to a lot of other people’s tweets. Especially during the conf. Especially to people who were also at the conf.

But who have I been talking to most?

mentions <- january_tweets %>% 
  filter(tweet_type != "Retweet") %>%
  select(username = mentions_screen_name) %>% 
  unnest() %>% 
  filter(! %>% 
  count(username, sort = TRUE)

mentions %>%
  head(10) %>%
  ggplot(aes(x = fct_reorder(username, n),
             y = n,
             fill = username)) + 
  labs(x = NULL, y = "Mentions") +
  geom_col() + 
  coord_flip() + 
  theme(legend.position = "none",
        axis.text = element_text(size = 10))

Yep, checks out. Demetri snuck in, but every other person on that list was at the conference!

For the top 8, let’s also get their tweets during January. You might think that 1000 each is overkill, but, uh, Mara is on the list.


friends_tweets <- mentions %>%
  filter(row_number() <= 8) %>%
  mutate(tweets = map(username, get_timeline, n = 1000)) %>%
  unnest() %>%
  filter(!is_retweet) %>%
  select(username, created_at, text)

(yes, top 10 to top 8, sorry Erin and Demetri – I want a 3x3 plot in like just a sec and I need to include myself OK sorry!)

I am going to totally be lazy about timezones here. Let’s pretend everyone is in my timezone and look at their tweets for January.

friends_tweets <- friends_tweets %>%
  mutate(created_at = with_tz(created_at, "America/Toronto")) %>%
  filter(created_at >= "2019-01-01" & created_at <= "2019-01-31")

Every single one of these people likes and uses R. But would it really be a Twitter analysis if I didn’t do some text analysis? I’m going to do tf-idf (using Julia Silge and drob’s tidytext package), 1) because I have blogged about it before and can copy my old code, but mostly 2) because it’s a fun way to look at differences in how people communicate.

I’m throwing myself back in the mix (hence keeping getting the top 8 before) to see the words that each one of us uses the most, but everyone else uses… not so much.

There’s a bunch of data cleaning here to replace quotes that aren’t quotes (i.e., ’ – Hadley was the most guilty of this), remove mentions of other people, and 😭 remove emojis. I know, I know. Another time.


tidy_words <- friends_tweets %>%
  select(username, text) %>%
    january_tweets %>%
      select(text) %>%
      mutate(username = "sharlagelfand")
  ) %>%
  mutate(text = str_replace_all(text, "’", "'")) %>% 
  unnest_tokens(word, text, token = "tweets") %>%
  filter(!str_detect(word, "^@"),
         !str_detect(word, "[\\uD83C-\\uDBFF\\uDC00-\\uDFFF]+")) %>%
  anti_join(stop_words, by = "word") %>%
  count(username, word)

total_words <- tidy_words %>%
  group_by(username) %>%
  summarize(total = sum(n))

tf_idf_words <- tidy_words %>%
  left_join(total_words, by = "username") %>%
  bind_tf_idf(word, username, n) %>%
  group_by(username) %>%
  top_n(5, wt = tf_idf) %>%

tf_idf_words %>%
  ggplot(aes(x = fct_reorder(word, tf_idf), 
             y = tf_idf, 
             fill = username)) +
  geom_col() + 
  labs(x = NULL, y = "tf-idf") +
  facet_wrap(~username, ncol = 3, scales = "free") + 
  coord_flip() + 
  theme(axis.text.x = element_blank(),
        legend.position = "none",
        axis.text = element_text(size = 10),
        strip.text = element_text(size = 10))

I absolutely love how you can see people’s personalities immediately.

Caitlin loves Texas and data science. Brooke had her epic mug thread and her #rstudioconf drawings. Mara is a mix of #SEO badness and umms.

Hadley is all business (I found out DSL = “Domain Specific Language” like, during the conference). Jen finally got a flight home after being stuck in Austin for four extra days. Malcolm just is Epi (ok, sorry Malcolm, I know there are other aspects to your personality).

Miles is, uh, Australian. I am vegan and annoying 😁. Jacqueline has unlimited T-Mobile internet ™️ ™️ ™️ and gave a talk about Tensorflow. She even got the T-Mobile colours. By default! 💅

I don’t think there’s much more I can do in this post that will have results as cool as quickly as that tf-idf analysis. So, I’ll just say that I’m excited to be 8.3% done my 2019 blogging commitment, and I’m going to try to tweet like, significantly less than 400 times in February. Because, let’s just look at the past year.

my_tweets %>%
  ggplot(aes(x = month,
             fill = tweet_type)) + 
  geom_histogram(stat = "count") + 
  theme(legend.position = "bottom",
        legend.title = element_blank(),
        axis.title = element_blank(),
        axis.text = element_text(size = 10))

Woof 🐶