Deploying the front end
In my previous post I described how to create a file upload feature for images, using HTML and JavaScript in the frontend, posting a FormData object to the Ruby on Rails backend and using ActiveStorage to associate it with records in the SQL database
In my Monastery Gazetteer application (Github frontend, Github backend, I already provide users the opportunity to create and update records on monasteries and figures via forms. These send the information in JSON hashes, which can then be parsed by Rails (operating in API mode). What I wanted to do was additionally give users the opportunity to upload an image of the monastery or figure, and
Up until now in my technical training, especially with Flatiron School’s Online Software Engineering bootcamp, I have focused mostly on web development. But my interests in data driven projects, including my American Ancestries project which allows users to collect data on American ancestry groups and explore patterns and trends, has led me to explore tools more generally associated with data science. I do not see a hard and fast distinction between web development and data science, as many web applications rely on the rigorous analysis of data, and data science applications need to have a front end interface to be accessible to non-specialists. Data science techniques, and languages like R, could potentially enhance projects such as American Ancestries, allowing more sophisticated management and manipulation of the data that is displayed on the front end. The analysis of data is an increasingly important application of computation, and digital humanists in particular are using data science tools more and more to record information about data sets (textual corpora, historical maps, and so forth) and identify patterns and trends within them, using the power of computers and algorithms to analyze a far greater amount of data than could be done by hand, and solidify their intuitions with specific evidence and mathematical precision. University libraries and research departments rely on the R language as a scripting language for software applications that involve the analysis of statistical data, in fields including economics, public health, biology, and engineering.
My project was called American Ancestries. The app is based on ancestry and ethnicity data provided by the US Census American Community Survey, and allows users to explore the cultural diversity of the United States and see how it varies across different. Users are able to input data on geographic places, including overall population and the population of individual ancestry groups. They can then browse place and see the percentages of the different ancestry groups, as well as how they compare to the national average. They can also browse ancestry groups and see where the group is most prominently represented. (See https://en.wikipedia.org/wiki/Race_and_ethnicity_in_the_United_States#Ancestry for more background on how ancestry groups are categorized, https://data.census.gov/cedsci/table?q=Minneapolis%20city,%20Minnesota%20B04006&g=1600000US2743000&tid=ACSDT1Y2019.B04006&hidePreview=false for an example of the data in question).