#loading neccesary packages
library(MASS)

## Warning: package 'MASS' was built under R version 4.4.3

View(Boston)
data("Boston")
library(ggplot2)

# crime rate pattern 
#What is the distribution of per capita crime rate (crim) across suburbs in 

# Histogram for per capita crime rate
hist(Boston$crim, 
     main = "Distribution of Per Capita Crime Rate (crim)", 
     xlab = "Crime Rate", 
     col = "green", 
     breaks = 30)

# Check summary for potential outliers
summary(Boston$crim)

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##  0.00632  0.08204  0.25651  3.61352  3.67708 88.97620

boxplot(Boston$crim, main = "Boxplot of Crime Rate")

# Histogram for median home value
hist(Boston$medv, 
     main = "Distribution of Median Home Values (medv)", 
     xlab = "Median Value (in $1000s)", 
     col = "lightgreen", 
     breaks = 30)

summary(Boston$medv)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    5.00   17.02   21.20   22.53   25.00   50.00

hist(Boston$tax,
     main = "distribution of property taxes(tax)",
     xlab = "tax rate",
     col = "red",
     )

summary(Boston$tax)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   187.0   279.0   330.0   408.2   666.0   711.0

boxplot(Boston$tax, main = "Boxplot of Property Tax Rates")

#5

hist(Boston$crim,breaks = 10,  
     main = "Crime Rate with 10 Bins", col = "lightcoral")

hist(Boston$crim, breaks = 50,
     main = "crime rate with 50 bins",col = "red")

hist(Boston$medv,breaks = 10,
     main = "median house valuse ")

?Skewed