Assignment 1: Data Analysis In R - Income Dataset

Assignment 1 Data Analysis In R1readthe Income Dataset Zipincomeas

Read the income dataset, “zipIncomeAssignment.csv”, into R. Change the column names so that zcta becomes zipCode and meanhouseholdincome becomes income. Analyze the summary of the data to determine the mean and median average incomes. Create a scatter plot of the data and identify any outliers. Create a subset of the data with income between $7,000 and $200,000 and calculate the new mean income. Generate a box plot of income by zip code with appropriate title and axis labels, then create a log-scaled box plot. Using the ggplot2 library, produce a scatter plot with jitter, colored by zip code, and overlaid with a box plot, both with specified transparency settings and labels. Finally, interpret the analysis and visualizations to draw meaningful conclusions about income distribution among ZIP codes.

Paper For Above instruction

The analysis of income distribution across different ZIP codes presents a nuanced understanding of economic disparities and regional income variation. This investigation uses R programming to manipulate, analyze, and visualize income data, which is vital for policymakers and economists interested in regional economic health and income inequality. The dataset, “zipIncomeAssignment.csv,” offers an opportunity to explore various statistical measures and visual storytelling techniques to interpret income data effectively.

Initially, importing the dataset into R lays the foundation for subsequent operations. The `read.csv()` function facilitates data importation, after which the column names are renamed for clearer interpretability. Changing 'zcta' to 'zipCode' and 'meanhouseholdincome' to 'income' enhances readability and aligns with standard naming conventions, allowing for more intuitive analysis. This renaming underscores the importance of data cleaning in preparing datasets for comprehensive analysis.

Following data preparation, a summary statistics analysis reveals key measures such as the mean and median income levels. The `summary()` function delivers insights into the dataset’s central tendency and variability; quantifying the average income and assessing its median supports understanding income distribution skewness, especially considering potential outliers. Typically, income data tend to be right-skewed, with a few high-income outliers pulling the mean upward relative to the median.

To visualize the data, a scatter plot is generated using R’s base plotting system. Despite its simplicity, this visualization aids in identifying extreme outlier values that deviate markedly from the bulk of the data points. These outliers could represent ZIP codes with extraordinarily high incomes, perhaps due to affluent neighborhoods or data entry anomalies. Recognizing such outliers is crucial for subsequent more refined analysis.

Subsequently, outliers are omitted to better analyze typical income levels. A subset of data is created where income values are constrained between $7,000 and $200,000, filtering extreme high and low incomes. Calculating the mean income of this subset provides a more representative measure of typical income without the distortion caused by outliers. This step emphasizes the importance of data cleaning and the impact outliers have on statistical measures.

The creation of a box plot visualizes the distribution of incomes across ZIP codes. A standard box plot displays median, quartiles, and potential outliers, providing a succinct summary of income spread per ZIP code. Given the skewness of income data, transforming the y-axis to a logarithmic scale better spreads the data and reveals variations masked on a linear scale. This log-scale visualization enhances interpretability, especially when incomes span multiple orders of magnitude.

Moving to advanced visualization with ggplot2, a scatter plot with jittering is generated to show individual data points across ZIP codes, colored distinctly for each ZIP code. Jittering prevents point overlap, revealing the density and distribution nuances. Applying `log10` transformation to the y-axis adjusts for skewness, making patterns more discernible.

Expanding upon the scatter plot, a combined plot overlays a box plot with the jittered scatter points. The box plot is semi-transparent and featureless for outliers to focus attention on the central distribution. Different colors for each ZIP code enable easy comparison of income variability across regions. Such layered visualizations support a comprehensive understanding of income dispersion and regional disparities.

Interpreting these analyses uncovers insights into income inequality among ZIP codes. The presence of outliers indicates pockets of affluence or data anomalies. The skewed distribution highlights that most ZIP codes tend toward lower-income levels, with a minority of high-income areas extending the upper bound. The combined plots illustrate that income variations within ZIP codes are significant, with some regions exhibiting substantial heterogeneity. These visual insights inform targeted policy interventions aimed at reducing regional income gaps and fostering economic equity.

References

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