Credit and Wage Dataset
Marimuthu S
2024-11-04
LIBRARIES USED
ISLR
ggplot2
dplyr
tidyr
CREDIT DATASET
ID Income Limit Rating
Min. : 1.0 Min. : 10.35 Min. : 855 Min. : 93.0
1st Qu.:100.8 1st Qu.: 21.01 1st Qu.: 3088 1st Qu.:247.2
Median :200.5 Median : 33.12 Median : 4622 Median :344.0
Mean :200.5 Mean : 45.22 Mean : 4736 Mean :354.9
3rd Qu.:300.2 3rd Qu.: 57.47 3rd Qu.: 5873 3rd Qu.:437.2
Max. :400.0 Max. :186.63 Max. :13913 Max. :982.0
Cards Age Education Gender Student
Min. :1.000 Min. :23.00 Min. : 5.00 Male :193 No :360
1st Qu.:2.000 1st Qu.:41.75 1st Qu.:11.00 Female:207 Yes: 40
Median :3.000 Median :56.00 Median :14.00
Mean :2.958 Mean :55.67 Mean :13.45
3rd Qu.:4.000 3rd Qu.:70.00 3rd Qu.:16.00
Max. :9.000 Max. :98.00 Max. :20.00
Married Ethnicity Balance
No :155 African American: 99 Min. : 0.00
Yes:245 Asian :102 1st Qu.: 68.75
Caucasian :199 Median : 459.50
Mean : 520.01
3rd Qu.: 863.00
Max. :1999.00 Plots used for Credit Dataset
Histogram
Credit Score Distribution: What is the overall distribution of credit scores in the dataset?
ggplot(data1, aes(x = Rating)) +
geom_histogram(color = "black", fill = "lightgreen", bins = 20) + # Set the number of bins if needed
labs(title = "Distribution of Credit Ratings", x = "Credit Rating", y = "Frequency") +
theme_minimal() # Optional: Adds a minimalistic theme
Box plot
Credit Score by Age Group: How do credit scores vary across different age groups within the dataset?
data1 <- data1 %>%
mutate(Age = case_when(
Age >= 18 & Age < 30 ~ "18-30",
Age >= 30 & Age < 40 ~ "31-39",
Age >= 40 & Age < 50 ~ "40-49",
Age >= 50 & Age < 60 ~ "50-59",
Age >= 60 & Age < 65 ~ "60-65",
Age >= 65 ~ "65+",
TRUE ~ NA_character_ ))
ggplot(data1, aes(x = Age, y = Rating)) +
geom_boxplot(fill = "white") +
labs(title = "Credit Rating by Age Group", x = "Age Group", y = "Credit Rating")
Barplot
Impact of Payment History: How does payment history affect the average credit score among individuals?
data <- data1 %>%
mutate(Balance = case_when(
Balance < 500 ~ "<500",
Balance >= 500 & Balance < 1000 ~ "500-999",
Balance >= 1000 & Balance < 1500 ~ "1000-1499",
Balance >= 1500 ~ "1500+"
))
average_credit_rating <- data %>%
group_by(Balance) %>%
summarize(Rating = mean(Rating, na.rm = TRUE))
ggplot(average_credit_rating, aes(x = Balance, y = Rating, fill = Balance)) +
geom_bar(stat = "identity", color = "black") +
labs(title = "Average Credit Score by Balance Group",
x = "Balance Group",
y = "Average Credit Score") +
scale_fill_manual(values = c("red", "blue", "yellow", "green"))
Scatter Plot
Credit Utilization Ratio: What is the relationship between credit utilization ratios and credit scores in the dataset?
data1$credit_utilization <- data1$Balance
suppressMessages(
ggplot(data1, aes(x = credit_utilization, y = Rating)) +
geom_point(alpha = 0.9, color = "blue") +
geom_smooth(method = "lm", color = "black", se = FALSE) +
labs(title = "Credit Utilization Ratio vs. Credit Rating",
x = "Credit Utilization Ratio",
y = "Credit Rating")
)## `geom_smooth()` using formula = 'y ~ x'
Line plot
Credit Score Trends: How have credit scores changed over the past few years? Are there noticeable trends?
trend_data <- data1 %>%
group_by(Cards) %>%
summarize(Average_Rating = mean(Rating, na.rm = TRUE))
# Plot
ggplot(trend_data, aes(x = Cards, y = Average_Rating)) +
geom_line(color = "black", linewidth = 0.8) +
geom_point(color = "red", size = 3, shape = 16) +
labs(title = "Average Credit Rating by Number of Cards",
x = "Number of Cards",
y = "Average Credit Rating") +
theme_minimal()
WAGE DATASET
year age maritl race
Min. :2003 Min. :18.00 1. Never Married: 648 1. White:2480
1st Qu.:2004 1st Qu.:33.75 2. Married :2074 2. Black: 293
Median :2006 Median :42.00 3. Widowed : 19 3. Asian: 190
Mean :2006 Mean :42.41 4. Divorced : 204 4. Other: 37
3rd Qu.:2008 3rd Qu.:51.00 5. Separated : 55
Max. :2009 Max. :80.00
education region jobclass
1. < HS Grad :268 2. Middle Atlantic :3000 1. Industrial :1544
2. HS Grad :971 1. New England : 0 2. Information:1456
3. Some College :650 3. East North Central: 0
4. College Grad :685 4. West North Central: 0
5. Advanced Degree:426 5. South Atlantic : 0
6. East South Central: 0
(Other) : 0
health health_ins logwage wage
1. <=Good : 858 1. Yes:2083 Min. :3.000 Min. : 20.09
2. >=Very Good:2142 2. No : 917 1st Qu.:4.447 1st Qu.: 85.38
Median :4.653 Median :104.92
Mean :4.654 Mean :111.70
3rd Qu.:4.857 3rd Qu.:128.68
Max. :5.763 Max. :318.34
Plots used for Wage Dataset
Histogram
Wage Distribution: What is the distribution of wages in the dataset, and how does it compare to national averages?
ggplot(data1, aes(x = wage)) +
geom_histogram(binwidth = 8, fill = "yellow", color = "black") +
labs(title = "Wage Distribution", x = "Wage", y = "Frequency") +
theme_minimal()
national_average <- 50
ggplot(data1, aes(x = wage)) +
geom_histogram(binwidth = 8, fill = "darkgreen", color = "black", alpha = 0.6) +
geom_vline(xintercept = national_average, color = "red", linetype = "dashed", linewidth = 1) + # Use linewidth
labs(
title = "Wage Distribution with National Average",
x = "Wage",
y = "Frequency"
) +
theme_minimal() +
annotate(
"text",
x = national_average + 5, # Position text slightly further for better visibility
y = 200,
label = "National Avg",
color = "navyblue",
fontface = "bold",
size = 4
) +
theme()
Bar plot
Average Wage by Industry: What are the average wages across different industries represented in the dataset?
average_wage_by_industry <- data1 %>%
group_by(jobclass) %>%
summarize(Average_Wage = mean(wage, na.rm = TRUE))
ggplot(average_wage_by_industry, aes(x = order(jobclass, Average_Wage), y = Average_Wage, fill = jobclass)) + geom_bar(stat = "identity") +
labs(title = "Average Wage by jobclass ", x = "jobclass", y = "Average Wage") +
theme_minimal() +
coord_flip()
Line plot
Wage Growth Over Time: How have wages changed over time within the dataset? Are there specific periods of growth or decline?
average_wage_over_time <- data1 %>%
group_by(year) %>%
summarize(Average_Wage = mean(wage, na.rm = TRUE))
ggplot(average_wage_over_time, aes(x = year, y = Average_Wage)) +
geom_line(color = "blue") +
geom_point() +
labs(title = "Wage Growth Over Time", x = "Year", y = "Average Wage") +
theme_minimal()
Box plot
Wage vs Education Level: How do wages differ by education level, and is there a significant correlation between education and wage?
ggplot(data1, aes(x = education, y = wage, fill = education)) +
geom_boxplot() +
labs(title = "Wage Distribution by Education Level", x = "Education Level", y = "Wage") +
theme_minimal() +
scale_fill_brewer(palette = "Set3")
Density
Impact of Employment Type: How do wages vary between full-time, part-time, and contract workers in the dataset?
ggplot(data1, aes(x = wage, fill = jobclass, color = jobclass)) +
geom_density(alpha = 0.4) +
labs(title = "Density Plot of Wages by Employment Type",
x = "Wage", y = "Density") +
theme_minimal() +
theme(legend.position = "right") +
scale_fill_brewer(palette = "Set2") +
scale_color_brewer(palette = "Set2")