This Shiny web application provides a user interface to explore, visualize, and analyze demographic data for the city of Barcelona. The app is divided into three main tabs: Data Selection, Data Visualization, and Regression Analysis. This application also allows the user to upload their datasets (including geojson file) to explore their own data.
shiny: Provides the framework for building the Shiny web application.shinydashboard: Creates a dashboard layout for the Shiny app.plotly: Enables interactive and dynamic plotting.tidyverse: A collection of packages for data manipulation and visualization.DT: Renders interactive data tables.forecast: Used for time series forecasting.gganimate: Adds animation to ggplot2 visualizations.leaflet: Creates interactive maps.corrplot: Generates correlation plots.caret: Implements machine learning tools.stargazer: Produces statistical tables.shinycssloaders: Provides loading animations.shinythemes: Adds additional themes for Shiny apps.datadigest: Creates a digest of data for exploration. Download the package through this: https://cran.r-project.org/src/contrib/Archive/datadigest/
The application loads the dataset from the "Reduced_Data_Demographic.csv" file, excluding the first column (X). It also loads geographical data for Barcelona districts from "districtes.geojson."
df <- read.csv("Reduced_Data_Demographic.csv") %>% select(-X)
barcelona <- rjson::fromJSON(file = "districtes.geojson")Users can select age groups and columns for analysis. Buttons for downloading the selected data and updating the dataset are provided.
Users can select a specific year using a slider. A dynamic plot using Plotly shows the chosen variable’s distribution across Barcelona districts for the selected year.
Users can select variables for linear regression analysis. Options for choosing X and Y variables, setting a train/test split percentage, and viewing model summaries are available. Multiple panels for exploring data, summary statistics, correlation plots, and model results are included.
Reactive function filtereddata filters the dataset based on user inputs. Download button (input$downloadData) exports the selected data to “Barcelona_write_csv.csv.”
Reactive function output$time_dynamic_plot generates a choropleth map using Plotly based on the selected variable and year.
Reactive functions handle data splitting, model training, and prediction. Various outputs provide information on data summary, correlation plots, linear regression model details, variable importance, and prediction plots.
Linear regression is performed on the selected X and Y variables. Model results, including coefficients and statistics, are displayed. Variable importance is computed, and the top variables are presented.
The app includes a plot showing the best fit line between actual and predicted values. Residual plots are displayed for diagnostic purposes.
The app includes loading spinners (withSpinner) to indicate when data is being processed. The datadigest package is used to create a data digest for exploration in the “Data Structure” tab (currently commented out in the code).