Prior to streaming, make sure to install and load rtweet. This
vignette assumes users have already setup app access tokens (see: the
vignette("auth", package = "rtweet")).
## Load rtweet library(rtweet)
In addition to accessing Twitter’s REST API (e.g.,
get_timeline), rtweet makes it
possible to capture live streams of Twitter data1. This requires an app
vignette("auth", package = "rtweet").
There are two ways of having a stream:
stream collecting data from a set of rules, which can be collected
stream of a 1% of tweets published, which can be collected via
In either case we need to choose how long should the streaming connection hold, and in which file it should be saved to.
## Stream time in seconds so for one minute set timeout = 60 ## For larger chunks of time, I recommend multiplying 60 by the number ## of desired minutes. This method scales up to hours as well ## (x * 60 = x mins, x * 60 * 60 = x hours) ## Stream for 5 seconds <- 5 streamtime ## Filename to save json data (backup) <- "rstats.json"filename
The filtered stream collects tweets for all rules that are currently active, not just one rule or query.
Streaming rules in rtweet need a value and a tag. The value is the query to be performed, and the tag is the name to identify tweets that match a query. You can use multiple words and hashtags as value, please read the official documentation. Multiple rules can match to a single tweet.
## Stream rules used to filter tweets <- stream_add_rule(list(value = "#rstats", tag = "rstats"))new_rule
To know current rules you can use
know if any rule is currently active:
<- stream_add_rule(NULL) rules rules#> sent result_count #> 1 2022-12-19 23:24:51 1 rules(rules) #> id value tag #> 1 1604981106868211713 #rstats rstats
With the help of
rules() the id, value and tag of each
rule is provided.
To remove rules use
# Not evaluated now stream_rm_rule(ids(new_rule))
Note, if the rules are not used for some time, Twitter warns you that
they will be removed. But given that
collects tweets for all rules, it is advisable to keep the rules list
short and clean.
Once these parameters are specified, initiate the stream. Note: Barring any disconnection or disruption of the API, streaming will occupy your current instance of R until the specified time has elapsed. It is possible to start a new instance or R —streaming itself usually isn’t very memory intensive— but operations may drag a bit during the parsing process which takes place immediately after streaming ends.
## Stream election tweets <- filtered_stream(timeout = streamtime, file = filename, parse = FALSE) stream_rstats #> Warning: No matching tweets with streaming rules were found in the time provided.
If no tweet matching the rules is detected a warning will be issued.
Parsing larger streams can take quite a bit of time (in addition to time spent streaming) due to a somewhat time-consuming simplifying process used to convert a json file into an R object.
Don’t forget to clean the streaming rules:
stream_rm_rule(ids(new_rule)) #> sent deleted not_deleted #> 1 2022-12-19 23:25:07 1 0
sample_stream() function doesn’t need rules or
<- sample_stream(timeout = streamtime, file = filename, parse = FALSE) stream_random #> 289 records... Found 289 records. Simplifying... Imported length(stream_random) #>  289
Given a lengthy parsing process, users may want to stream tweets into
json files upfront and parse those files later on. To do this, simply
parse = FALSE and make sure you provide a path (file
name) to a location you can find later.
You can also use
append = TRUE to continue recording a
stream into an already existing file.
Currently parsing the streaming data is not functional. However, you
can read it back in with
The parsed object should be the same whether a user parses up-front or from a json file in a later session.
Currently the returned object is a raw conversion of the feed into a nested list depending on the fields and extensions requested.
Till November 2022 it was possible with API v1.1, currently this is no longer possible and uses API v2.↩︎