Twitter dashboard app how to#
Twitter dashboard app pdf#
Twitter dashboard app code#

on_status(): Called when a new tweet is present in the stream.

There are two methods of the StreamListener class that we will overwrite in our child class TweetListener: The StreamListener class is used to define how each incoming tweet should be handled. The latter simply handles API authentication and requires the unique keys from the creation of your Twitter app. Streaming with Tweepy comprises of three objects Stream, StreamListener, OAuthHandler. YouвЂ™ll need to set up a twitter application at to attain a set of authentication keys to use with the API. Tweepy is a Python library to access the Twitter API. This article will work through each point above and show how they interact. This is surprisingly simple and can be done within a single python file!

Streamlit: Once we have our data stream working weвЂ™ll need to set up our web app using Streamlit.

Once these have been obtained we can set up a stream with keyword filters.

Tweepy: Credentials are required to interact with the Tweepy API.Database set-up: This can be done directly in the RDBMS of your choice, however we choose to use SQLAlchemy in this example.We can break down what needs to be done into three distinct areas: However itвЂ™s possible to use any combination of keywords to analyse political figures, companies etc. I decided to build a dashboard that combines the two questions using the example of UK political leaders Boris Johnson and Keir Starmer. Twitter can be used as a data source for various data science projects, including Geo-spatial analysis (where are users tweeting about certain subjects?) and sentiment analysis (how do users feel about certain subjects?). Other useful resources: Zev Ross created an easy-to-follow Shiny tutorial with examples and a cheat sheet, and Hadley Wickham has a Mastering Shiny book in progress thatвЂ™s available free online.Streamlit makes it easy to turn Data Science projects into web apps and dashboards (Image by Author) To learn more about Shiny, RStudio has some written and video tutorials at. You could keep other columns from the initial data set to filter for variables such as language and geography. Or, you could add a check box to filter for tweets that contain URLs - potentially helpful if youвЂ™re looking for conference tweets that might include links to presentations and other resources. You might add sliders to set a minimum number of retweets or likes, and then do additional table filtering on that subset of tweets. There is so much more you could do to add capabilities to this app. # Configure variables: number of tweets to download and hashtag search query num_tweets_to_download % select(user_id, status_id, created_at, screen_name, text, favorite_count, retweet_count, urls_expanded_url) %>% mutate( Tweet = glue::glue(') More enhancements

Twitter dashboard app code#

This first code block from that article searches for tweets and formats results to display in a table.

Twitter dashboard app how to#

Original R code to search tweetsДo More With R episode #41 demonstrated how to search for tweets with the rtweet package and create a sortable, filterable table with reactable.

Twitter dashboard app pdf#

If you donвЂ™t want to go through the whole Twitter R tutorial, you can download the Shiny and R app code plus this article in R Markdown and PDF format as a free Insider extra.