In my project, I utilized raw datasets to determine data trend within the LA Bike Share dataset. I used the languages R and Python to calculate values and visualize my data. Specifically I used plotting function in RStudio and the dplyr library to work with the .csv files which were later converted to R data frames.
I utilized a DOM model where I had my code (.R and .py files) in a separate folder contained in the HTML/CSS structure. I referenced what each code file printed out as the solution to my problem. Furthermore, in my visualizations, I used the dplyr library to filter and mutate my data frames and to calculate new values. I also used R plotting functions (like bar plot and pie chart) to craft efficient data visualizations into image files for viewing.
In the presentation of my data, I used Python to calculate and write the latitude and longitude values onto Tableau Desktop. The Tableau Desktop plotted each coordinate in a Map so we can see where each station is located (start, end).
Prior to working on this web application, I didn't know how to use R and manipulate data frames. I had only previously used Python for data structures. However, I was able to pick up the R language and use RStudio to easily manipulate my data and calculate values that would be difficult to do in Python.
My main takeaway from this project is to always understand your data before you work with it. I crafted an efficient approach towards answering the proposed questions and even made my own questions to answer.
You can find the GitHub Source Code here.