Q.S. Homework 4

library(tidyverse)

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library(dplyr)
library(ggplot2)
library(pastecs)

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##     extract

traffic_data <- read.csv("traffic_data.csv")

viol_fine <- traffic_data %>%
  select(ViolFine) %>%
  filter(!is.na(ViolFine))

stat.desc(viol_fine)

##                  ViolFine
## nbr.val      9.548000e+03
## nbr.null     2.916000e+03
## nbr.na       0.000000e+00
## min          0.000000e+00
## max          5.200000e+02
## range        5.200000e+02
## sum          9.099470e+05
## median       9.800000e+01
## mean         9.530237e+01
## SE.mean      8.284820e-01
## CI.mean.0.95 1.624001e+00
## var          6.553580e+03
## std.dev      8.095418e+01
## coef.var     8.494457e-01

ggplot(viol_fine, aes(x = ViolFine)) +
  geom_histogram(bins = 30, fill = "blue", alpha = 0.7, color = "black") +
  ggtitle("Histogram of ViolFine (Original)") +
  xlab("Fine Amount ($)") +
  ylab("Frequency") +
  theme_minimal()

viol_fine <- viol_fine %>%
  mutate(ViolFine_transformed = sqrt(ViolFine))

ggplot(viol_fine, aes(x = ViolFine_transformed)) +
  geom_histogram(bins = 30, fill = "red", alpha = 0.7, color = "black") +
  ggtitle("Histogram of ViolFine (Square Root Transformed)") +
  xlab("Square Root Fine Amount") +
  ylab("Frequency") +
  theme_minimal()