Housing Affordability Data
Dataset Description
For this lab exercise, I selected a dataset from Data.gov, which provides free public datasets. The chosen dataset is called 2019 Housing Affordability Data. It contains information about housing costs, income levels, and affordability indices across U.S. counties. The file was available in a .CSV format, making it compatible for analysis in R.
Examples include county names, median household income, median housing costs, and affordability ratios. These variables are essential for analyzing trends in housing affordability. With this dataset, researchers can explore relationships between income levels and housing challenges. Such analysis supports decision-making in housing policies and community planning.
Check out our Essay writing services
Housing Affordability Data
Data Structure and Summary
After loading the file into R with the function, I assigned it to the BAN6040_DATA data frame. I first used the command, which displayed the structure of the dataset. This provided an overview of variable types and dataset size.
Next, I used the command to view the first few rows. The preview revealed sample county names along with their corresponding affordability data. This gave me a quick look at how information was organized in the dataset.
The results included minimum, maximum, mean, and median values for numerical columns.
By combining structural details, sample previews, and statistical summaries, I gained a clear understanding of the dataset’s content. This process demonstrated the usefulness of R for organizing and analyzing complex data.
For this lab exercise, I selected a dataset from Data.gov, which provides free public datasets. The chosen dataset is called 2019 Housing Affordability Data. It contains information about housing costs, income levels, and affordability indices across U.S. counties. The file was available in a .CSV format, making it compatible for analysis in R.
Examples include county names, median household income, median housing costs, and affordability ratios. These variables are essential for analyzing trends in housing affordability. With this dataset, researchers can explore relationships between income levels and housing challenges. Such analysis supports decision-making in housing policies and community planning.
