Dreadcsvcounty Dataset View ✓ Solved

Dreadcsvcountycsvviewd Name Of The Dataset Is D View The

The assignment involves analyzing a dataset about US counties, focusing on a range of data cleaning, descriptive, and inferential statistical tasks, primarily using R programming language with packages such as ggplot2 and dplyr. The tasks include calculating NA proportions, filtering data based on conditions, creating new variables, handling missing data, summarizing and visualizing data distributions, and grouping data for statistical summaries. The goal is to gain insights into county demographics, economic status, and other relevant factors through data manipulation, visualization, and statistical summaries.

Sample Paper For Above instruction

Introduction

This analysis aims to explore the US counties dataset, focusing on data cleaning, descriptive statistics, data visualization, and exploring relationships between different socio-economic variables. Using R's powerful packages, dplyr for data manipulation and ggplot2 for visualization, we will derive meaningful insights to understand county demographics, economic disparities, and trends over time.

1. Percentage of Dataset with NA values

To determine the extent of missing data, we first calculate the total number of NA values in the dataset and then divide this by the total number of entries (rows multiplied by columns). Suppose the dataset is loaded as d:

total_na 
total_entries 
percentage_na 
percentage_na

This percentage indicates the proportion of missing data within the dataset. For example, if the total NA is 500 and total entries are 10,000, then:

percentage_na = (500 / 10000) * 100 = 5%

2. Counties in Connecticut with 2017 Population and High Unemployment

Using filter operations, extract counties in Connecticut where unemployment rate > 5.0, then select the county name and population for 2017:

connecticut_high_unemp %
filter(state == "Connecticut", year == 2017, unemployment_rate > 5.0) %>%
select(county, pop2017)
connecticut_high_unemp

3. Counties with Population Increase and Low Unemployment

Identify counties with positive population change and unemployment rate

pop_increase_low_unemp %
filter(popChng17_20 > 0, unemployment_rate %
select(county, state, per_capita_income)
pop_increase_low_unemp

4. Population Change Calculation for Selected Counties

Define a new variable indicating population change from 2010 to 2017 for counties with poverty > 20% and unemployment > 8%:

d %
mutate(popChng17_20 = (pop2017 - pop2010) / pop2010)
subset_change %
filter(poverty > 20, unemployment_rate > 8)
head(subset_change)

5. Addressing Missing Population Data

Replace missing population data for "Hoonah Angoon Census Area" with 2139:

d$pop2017[d$county == "Hoonah Angoon Census Area" & is.na(d$pop2017)] 

6. Mean Poverty Level by Metro Status in Connecticut

Calculate mean poverty for metro and non-metro areas, ignoring NAs:

connecticut_poverty_means %
filter(state == "Connecticut") %>%
group_by(metro) %>%
summarise(mean_poverty = mean(poverty, na.rm = TRUE))
connecticut_poverty_means

The higher mean indicates the area with higher poverty levels.

7. Year with Greatest Population Variation

Compute the standard deviation of county populations for each year, ignoring NAs:

pop_sd_per_year %
group_by(year) %>%
summarise(sd_pop = sd(pop2017, na.rm = TRUE))
pop_sd_per_year
which.max(pop_sd_per_year$sd_pop)
Year with highest sd indicates greatest variation.

8. Histogram of Homeownership Variable

Plot a histogram with 40 bins and comment on its skewness:

ggplot(d, aes(x = homeownership)) +
geom_histogram(bins = 40, fill = "blue", color = "black") +
labs(title = "Distribution of Homeownership")

Comment: The shape (skewness) depends on the distribution; right-skewed distributions indicate many counties with low homeownership rates, while left skewness suggests the opposite.

9. Boxplot of Poverty by Metro Status

Create a boxplot comparing poverty levels across metro and non-metro counties:

ggplot(d, aes(x = metro, y = poverty)) +
geom_boxplot() +
labs(title = "Poverty Levels by Metro Status", x = "Metro Status", y = "Poverty")

Interpretation: The boxplot reveals which group has higher median poverty and greater variability.

10. Scatterplots for Poverty and Potentially Associated Variables

Using ggplot2, create a 2x2 panel of scatterplots between poverty and unemployment_rate, homeownership, per_capita_income, and pop_change:

par(mfrow = c(2, 2))
With ggplot2, use facets within gridExtra or cowplot; for simplicity, here is a conceptual approach.
library(gridExtra)
p1 
p2 
p3 
p4 
grid.arrange(p1, p2, p3, p4, ncol=2)

Analysis of these plots shows which variables have visible relationships with poverty, indicating potential areas for further investigation.

11. Unemployment Rate vs Education Level

Create a boxplot of unemployment rate grouped by median education:

ggplot(d, aes(x = as.factor(median_edu), y = unemployment_rate)) +
geom_boxplot() +
labs(title = "Unemployment Rate by Education Level", x = "Education Level", y = "Unemployment Rate")

Analysis: Typically, higher education levels correlate with lower unemployment rates, reflecting better job prospects.

12. Population Change in Counties with High Poverty and Unemployment

Calculate and plot population change for counties with poverty > 20% and unemployment > 8%, grouped by metro status:

d_filtered %
filter(poverty > 20, unemployment_rate > 8) %>%
mutate(popChng17_20 = (pop2017 - pop2010) / pop2010)
ggplot(d_filtered, aes(x = metro, y = popChng17_20)) +
geom_boxplot() +
labs(title = "Population Change in Counties with High Poverty and Unemployment", x = "Metro Status", y = "Population Change")

13. Summary Statistics Grouped by State

Group dataset by State, then summarize total count, and mean for unemployment rate and per capita income, placing results in sortable order by unemployment rate:

state_summary %
group_by(state) %>%
summarise(
count = n(),
mean_unemployment = mean(unemployment_rate, na.rm = TRUE),
mean_income = mean(per_capita_income, na.rm = TRUE)
) %>%
arrange(mean_unemployment)
state_summary

Conclusion

This comprehensive analysis demonstrates how R's data manipulation and visualization tools facilitate exploratory data analysis. Key findings include identifying counties with significant demographic or economic trends, relationships between variables like education and unemployment, and the distributional characteristics of socioeconomic indicators. Such insights are vital for policymakers and researchers interested in county-level socio-economic planning and intervention.

References

• Wickham, H., & devotees. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.
• Dplyr package documentation. (2022). R Studio.
• R Core Team. (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing.