Track weight, BMI and other metrics. Parse through the Ideal protein metrics, visualize and analyze.
Python3 for data wrangling step and then R Shiny for the web application. The R packages used are Shiny, Flexdashboard, tidyr, tidyverse, dplyr for data analysis, ggplot2 and dygraph for visualization and Prophet for timeseries forecasting.
- Get new data from the ideal protein website, this is done just by copy pasting from their web page. Save the copy pasted data in a file in the raw_data directory. Run the pre-processing step using the following (pandas is required, see requirements.txt). The output file (CSV) will be created in the data directory.
python preprocess.py --raw-data-filepath raw_data/raw_data_nidhi.txt --output-filename Nidhi.csv
python preprocess.py --raw-data-filepath raw_data/raw_data_amit.txt --output-filename Amit.csv
- Run the dashboard.Rmd in RStudio, this will create the timeseries forecasts and store the results as CSV file in the data directory. Publish the dashboard_no_prophet.Rmd and dashboard_mobile.Rmd as Shiny applications to shinyapps.io (requires sign-up).
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Desktop Version: https://amit-arora.shinyapps.io/BiometTracker/
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Mobile Version: https://amit-arora.shinyapps.io/BiometTrackerMobile/
