The purpose of this project is to present an interactive R Shiny App that is designed to let individuals explore house prices and patterns related to house prices based on the user’s preferences. The app allows the user to explore house prices depending on the preferences they select. The user is able to select a preferred square footage, number of bedrooms and bathrooms, renovation status, condition, and price range. The app then generates a customized, sorted housing table which is sorted based on price from high price to lowest price depending on all of the preferences they selected. On the second analysis path, users can select a square footage range and select a categorical variable to visualize price distributions through box plots. The app also conducts the statistical analysis (t-test or ANOVA) to calculate p- values which offers an insight into the differences in house prices across the specific category. This can show the user if specific preferences they selected have a statistically significant affect on house prices. The housing dataset used was sourced from Kaggle. The data focuses on various attributes related to house prices. The expected user experience includes interactive visualization and analysis of house prices based on their individual preferences and enables the users to filter data and compare statistical significance based on their desires.
The housing price dataset used in the project was sourced from Kaggle. This data set contains detailed property listing information and provides a wide range of features related to housing prices, which offers an insight into key factors that influence property value. The data set includes various property arrtributes such as the number of bedroom, bathrooms, square footage of living area, lot size, location details, property condition, renovation status, and structural features. These attributes are critical for identifying patterns and conducting statistical alalysis on housing price distributions. This dataset provides insight into the relationship between quantitative variables such as square footage and price range and categorical variable such as the number of bedrooms and property condition. This allows for analysis of how these features interact with house prices and how they can influence market trends. By integrating this dataset into the R Shiny App, users can explore relationships between their preferences and house prices, visualize statistical distributions, and conduct statistical significat testing to evaluate how these features such as size, condition, and renovation status impact affordability.
This R Shiny App is designed to help users explore housing preferences and analyze factors influencing housing prices. The app consists of two main interactive modules: Finding Your Ideal House and Influecne on House Prices. Below are detailed instructions on how to use each feature of the app:
The inputs include: Price Range slider: set your ideal price range Square Feet Slider: filter by living area square footage range Bedrooms & Bathrooms Select Boxes: select your ideal number of bedrooms and bathrooms Renovated Option: select whether you prefer if the house is renovated or not Condition Slider: Set a range from 1 (poor condition) to 5 (excellent condition) Search Button: click search to apply the selected filters
The Output: The filtered housing data table will appear in the main panel displaying only the house prices based on the input criteria sorted by price. The house prices will be listed by house id. The methods used to do this was through filtering using dplyr.
The Inputs Include: Square Footage Slider: set a range of living area square footage for analysis Categorical Variable Selector: choose a feature such as the number of bedrooms, bathrooms, condition, or renovation status to analyze housing prices.
The Outputs: A boxplot displaying housing price distributions across difference categories defined by the selected variable. The methods used for this module was the ggboxplot function to generate boxplots showing price distributions and a t-test for comparison between two groups, or ANOVA when there are three or more groups.