Use Package Dplyr To Explore West Roxbury Real Estate Datase

Use Package Dplyr To Explore Westroxbury Real Estate Datasetd

Below are the core instructions for analyzing the Westroxbury real estate dataset using R and the dplyr package. The analysis involves filtering, sorting, selecting specific columns, grouping, summarizing, and viewing the data, with an emphasis on understanding property attributes and applying data manipulation techniques in R.

Specifically, you are instructed to:

1. Filter the dataset to include only houses with a total value greater than 350,000, less than 20 years of age as of 2020, and with taxes less than 5,000. Use the mutate function to compute the age of each house.
2. Sort the filtered dataset by age and total value, then select only the columns for age, total value, lot square footage, and number of rooms. Assign this sorted data to a variable called sortedD. Show only the first 10 rows of sortedD using base R.
3. Create a subset of houses suitable for a potential buyer: houses with at least 8 rooms, exactly one floor, a fireplace, and built between 1970 and 1990. Arrange these houses by total value and assign the result to custList. View custList.
4. Group the dataset by age, then calculate the count, mean, minimum, and maximum of total value for each age group. Order this summary in descending order by count, and assign it to groupedD. View groupedD.

Paper For Above instruction

This analysis employs R programming and the dplyr package to explore and manipulate the Westroxbury real estate dataset. The goal is to extract meaningful insights by filtering, sorting, selecting, grouping, and summarizing data based on property attributes such as age, value, size, and features. These techniques facilitate a detailed understanding of the dataset, aiding potential buyers or real estate stakeholders in making informed decisions.

The first step involves reading the dataset from a CSV file and examining the data structure with the View() function. The primary focus is on properties valued over 350,000, less than 20 years old (calculated as of 2020), and with tax obligations under 5,000. The age of each house is calculated using the mutate() function, which creates a new variable representing the property's age by subtracting the year built from 2020.

Subsequently, the filtered dataset is sorted by age and total value to identify properties in a particular order, possibly indicating their relative age and investment value. The sorted dataset is then trimmed to show only key columns such as age, total value, lot square footage, and number of rooms. The result is stored in sortedD, and the initial ten records are displayed using base R's head() function.

The third analysis segment targets identifying properties ideal for a specific buyer profile. Conditions include houses with at least 8 rooms, a single-floor layout, a fireplace, and construction dates between 1970 and 1990. These criteria are used to filter the dataset, with the resulting subset sorted by total value to prioritize more expensive properties. This subset is assigned to custList and is displayed with the View() function.

Finally, the dataset is grouped based on age, and summary statistics are computed. The group_by() function clusters properties by age, and summarise() calculates the count, mean, minimum, and maximum of total value within each group. The resulting summarized data is ordered in descending order by the count of properties in each age group, providing insight into the distribution of property ages in the dataset. This final summary is stored in groupedD and displayed for review.

These data manipulation steps demonstrate the practical use of dplyr functions for real estate analysis, enabling stakeholders to derive actionable insights from complex datasets efficiently. The approach supports data-driven decision-making in real estate investments, property management, and market analysis.

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