\(\text{Fatalities}=\beta_0+\beta_1\cdot\text{Year}+\varepsilon; \hspace{1cm}\varepsilon\sim\mathcal{N}(0;\sigma^2)\)

# Load ggplot2 package
library(ggplot2)

shootings <- 
  read.csv("~/Desktop/US gun violence/shooting_1982-2023_cleaned.csv")


g <- ggplot(data = shootings, aes(x = year, y = fatalities, 
                                  color = gender)) + 
  geom_point() +
  geom_smooth(aes(group=1), method = "lm", se = FALSE) +
 
  labs(x = "Year", y = "Fatalities") +
  ggtitle("Fatalities in Shootings by Year and Gender")

This graph shows almost all mass shooters have been men. It also shows there was an increase in the number of fatalities from ~2007-2022

There is a lot of debate on whether we have a gun problem or a mental health problem

$x
[1] "Mental Health Issues"

$y
[1] "Count"

$fill
[1] "Gender"

attr(,"class")
[1] "labels"

library(plotly)

plot1 <- plot_ly(data = shootings, x = ~year, y = ~location, 
                 color = ~weapons_obtained_legally,
        type = "scatter", mode = "markers") %>%
  layout(title = "Weapons Obtained Legally by Year and Type",
         xaxis = list(title = "Year", dtick = 10), 
         yaxis = list(title = "Location"))

\(\text{Percentage Obtained Legally} =\frac{95}{141}\times{100} =\text{67.4%}\)
\(\text{Percentage Obtained Illegally} =\frac{16}{141}\times{100} =\text{11.3%}\)
\(\text{Percentage TBD/Unknown} =\frac{30}{141}\times{100} =\text{21.3%}\)

library(ggplot2)

shootings <- 
  read.csv("~/Desktop/US gun violence/shooting_1982-2023_cleaned.csv")

shootings_clean <- na.omit(shootings)

g3 <- ggplot(shootings_clean, aes(x = age_group, fill = age_group)) +
  geom_bar(stat = "count", color = "black") +
  labs(title = 
         "Frequency of Shootings by Age Group",
       x = "Age Group", y = "Count")