In v0.2 of the package, we include functionality to convert JSON files to various data frame formats. In order to use these features, we recommend the following workflow.
First, you should build your query using the build_query function.
require(academictwitteR)
require(tibble)
#> Loading required package: tibble
my_query <- build_query(c("#ichbinhanna", "#ichwarhanna"), place = "Berlin")
my_query
#> [1] "(#ichbinhanna OR #ichwarhanna) place:Berlin"Then, use the get_all_tweets to collect data. Make sure to specify data_path and set bind_tweets to FALSE.
get_all_tweets(
query = my_query,
start_tweets = "2021-06-01T00:00:00Z",
end_tweets = "2021-06-20T00:00:00Z",
n = Inf,
data_path = "tweetdata",
bind_tweets = FALSE
)The first format is the so-called “vanilla” format. This vanilla format is the direct output from jsonlite::read_json. It can display columns such as text just fine. But some columns such as retweet_count are nested in list-columns.
In order to extract user information, it is additionally necessary to set user = TRUE. Please also note that the data frame returned in this format is not a tibble. As such, we first need to convert it to a tibble.
bind_tweets(data_path = "tweetdata") %>% as_tibble#> ================================================================================
#> # A tibble: 25 × 14
#> public_metrics$retwee… conversation_id author_id entities$mentio… text lang
#> <int> <chr> <chr> <list> <chr> <chr>
#> 1 48 14060074051803… 58755490 <df [1 × 3]> "RT … de
#> 2 4 14056173864058… 97759337… <df [1 × 3]> "RT … de
#> 3 4 14056160479909… 13065071… <df [1 × 3]> "RT … de
#> 4 4 14056150555557… 97897581… <df [1 × 3]> "RT … de
#> 5 4 14056130649684… 114774406 <df [1 × 3]> "RT … de
#> 6 4 14056107240266… 47919307 <named list [0]> "Ihr… de
#> 7 0 14053930335589… 94052353… <df [2 × 3]> "👩💻… und
#> 8 0 14048087518576… 47919307 <df [1 × 3]> ".@j… de
#> 9 20 14044409298812… 11508518… <named list [0]> "Oka… de
#> 10 0 14043934574273… 30635588… <named list [0]> "#Ic… und
#> # … with 15 more rows, and 13 more variables: public_metrics$reply_count <int>,
#> # $like_count <int>, $quote_count <int>, entities$hashtags <list>,
#> # $urls <list>, created_at <chr>, id <chr>, possibly_sensitive <lgl>,
#> # referenced_tweets <list>, source <chr>, geo <df[,1]>,
#> # context_annotations <list>, in_reply_to_user_id <chr>The second format is the “raw” format. It is a list of data frames containing all of the data extracted in the API call. Please note that not all data frames are in Boyce-Codd 3rd Normal form, i.e. some columns are still list-column.
bind_tweets(data_path = "tweetdata", output_format = "raw") %>% names#> [1] "tweet.public_metrics.retweet_count" "tweet.public_metrics.reply_count"
#> [3] "tweet.public_metrics.like_count" "tweet.public_metrics.quote_count"
#> [5] "tweet.entities.mentions" "tweet.entities.hashtags"
#> [7] "tweet.entities.urls" "tweet.geo.place_id"
#> [9] "tweet.referenced_tweets" "tweet.context_annotations"
#> [11] "tweet.main" "user.public_metrics.followers_count"
#> [13] "user.public_metrics.following_count" "user.public_metrics.tweet_count"
#> [15] "user.public_metrics.listed_count" "user.entities.url"
#> [17] "user.entities.description" "user.main"
#> [19] "sourcetweet.main"The third format is the “tidy” format. It is an “opinionated” format, which we believe to contain all essential columns for social media research. By default, it is a tibble.
bind_tweets(data_path = "tweetdata", output_format = "tidy")#> # A tibble: 25 × 31
#> tweet_id user_username text conversation_id author_id lang created_at
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 1406007405180… Phardiga "RT … 14060074051803… 58755490 de 2021-06-1…
#> 2 1405617386405… dorothee_goe… "RT … 14056173864058… 97759337… de 2021-06-1…
#> 3 1405616047990… dejools "RT … 14056160479909… 13065071… de 2021-06-1…
#> 4 1405615055555… LenaOetzel "RT … 14056150555557… 97897581… de 2021-06-1…
#> 5 1405613064968… jenniferhenk… "RT … 14056130649684… 114774406 de 2021-06-1…
#> 6 1405610724026… Tobias_Schul… "Ihr… 14056107240266… 47919307 de 2021-06-1…
#> 7 1405393033558… HTMIBerlin "👩💻… 14053930335589… 94052353… und 2021-06-1…
#> 8 1404808751857… Tobias_Schul… ".@j… 14048087518576… 47919307 de 2021-06-1…
#> 9 1404440929881… ASattelmacher "Oka… 14044409298812… 11508518… de 2021-06-1…
#> 10 1404393457427… dr_john_aus_b "#Ic… 14043934574273… 30635588… und 2021-06-1…
#> # … with 15 more rows, and 24 more variables: possibly_sensitive <lgl>,
#> # source <chr>, in_reply_to_user_id <chr>, user_url <chr>,
#> # user_verified <lgl>, user_name <chr>, user_protected <lgl>,
#> # user_profile_image_url <chr>, user_description <chr>,
#> # user_created_at <chr>, user_pinned_tweet_id <chr>, user_location <chr>,
#> # retweet_count <int>, like_count <int>, quote_count <int>,
#> # user_tweet_count <int>, user_list_count <int>, …It has the following features / caveats:
tweet_id, author_id and sourcetweet_id respectively.text field of a retweet is truncated. However, the full-text original tweet is located in sourcetweet_text.sourcetweet_text. If you need that data, please follow the clue using the conversation_id.text by Twitter are not available in the tidy format, e.g. list of hashtags, cashtags, urls, entities, context annotations etc. If you need those columns, please consider using the “raw” format above.