Step 1: Download your Twitter archive

Like the Python tutorial, you have to first download your Twitter archive. Go to https://twitter.com/settings/your_twitter_data, enter your password, and “Download Archive”. You will get an email when you can download your data – for me, it took a day.

Once downloaded, open the ZIP file. Save the like.js file in your project’s data folder.

Step 2: Load the JS file and convert it to a data frame

Now, start an R script (I called mine code.R). Install (if you haven’t already) and load all the packages.

library(tidyverse)
library(rjson)
library(rtweet)

The like.js file is a Javascript file. To convert it to a data frame, first convert to a JSON file by removing the text that makes it Javascript, read in the JSON, and then flatten into a data frame using purrr::flatten_df.

js <- read_file(here::here("data", "like.js"))
json <- sub("window.YTD.like.part0 = ", "", js)
 
likes_raw <-
  fromJSON(json)

likes_df <-
  likes_raw %>% 
  flatten_df()

Now your likes, their status IDs, and URLs are stored in likes_df.

Step 3: Authenticate {rtweet} and get tweet info

To get the Tweet info, you have to authenticate your Twitter account and get the API keys. Go to your Twitter Developer Portal to do this. The {rtweet} vignette here covers what to do pretty well, but one thing I noticed is now you have to enable “3-legged OAuth” and there, you can add the Callback URL of http://127.0.0.1:1410 (otherwise you will get a 401 error when running the code below).

token <-
  create_token(
    consumer_key = "PUT API KEY IN QUOTES HERE",
    consumer_secret = "PUT API KEY SECRET IN QUOTES HERE"
  )

Once your token is saved, you can use rtweet::lookup_statuses to get the Tweet info.

likes_dat <-
  lookup_statuses(likes_df$tweetId)

There you have it! All your likes and their information are saved in the likes_dat data frame. Here, you can do all of your usual data manipulations - lookup_statuses() results in a LOT of data, so I recommend narrowing it down to the columns you care about using select().

Step 4: Save to a CSV

Saving to a CSV is a bit tricky because lookup_statuses() creates list-columns, which cannot be exported to a CSV.

Here, I’ve selected only the columns status_id, text, and hashtags to look at. hashtags is a list-column because one Tweet may have multiple hashtags, which is then saved as a vector within the column hashtags. To convert it to a data frame, I use unnest(). This creates a data frame with multiple entries for each Tweet, one for each hashtag.

That is too long for me since I want to quickly look through my likes. I then create a hashtag ID for each tweet by grouping by the status_id and assigning each of its hashtags an ID using row_number() and then using pivot_wider() to move each hashtag into its own column.

You will see that some tweets have blank entries while some tweets have many!

likes_csv <-
  likes_dat %>% 
  select(status_id, text, hashtags) %>% 
  unnest() %>% 
  group_by(status_id) %>% 
  mutate(id = row_number()) %>% 
  pivot_wider(names_prefix = "hashtag_",
              names_from = id,
              values_from = hashtags)

Now, this is savable in a CSV. You can run the code below if your project has an output folder.

write_csv(likes_csv, here::here("output", "likes_csv.csv"))

Thank you for reading this quick entry in looking through your Twitter likes. I hope this reduces the amount of annoying scrolling you need to do to look through your Tweets! I think there are a lot of opportunities to make this an even easier process - maybe a Shiny app? - but hopefully, this is a good starting point.