Dot Map

library(leaflet)
library(tidyverse)
library(lubridate)
library(broom)
library(plotly)
library(DT)

1. Here is a map of the shootings data.

shootings %>% 
  leaflet() %>% 
  addTiles() %>%
  addCircleMarkers(stroke = F, fillOpacity = .6, radius = ~log(total_victims))

Assuming "longitude" and "latitude" are longitude and latitude, respectively

I find it interesting that most of the area around Montana and the Dakotas is dot free.

2. Additional statistics A. Here is a median number of total victims.

shootings %>% 
  drop_na(total_victims) %>% 
  summarize(median_total_victims = median(total_victims))

Median total victims is 10.

A. Here is median fatalities

shootings %>% 
  drop_na(fatalities) %>% 
  summarize(median_fatalities = median(fatalities))

Median fatalities were 6.

B.This is a histogram of the number of shootings per year.

shootings %>% 
  drop_na(year) %>%
  plot_ly(x = ~year) %>%
  add_histogram(nbinsx = 40)

NA

2018-2019 had the most shootings with 22.

C. This is a heatmap with gender and race of the shooter.

shootings %>% 
  mutate(race = as_factor(race)) %>% 
    mutate(race = fct_collapse(race,
                             White = c( "white", "White"),
                             Black = c("black", "Black"),
                             Other = c ("unclear", "Other"))) %>%
  mutate(gender = as_factor(gender)) %>% 
    mutate(gender = fct_collapse(gender,
                             Female = c( "F", "Female"),
                             Male = c("M", "Male"))) %>%
    plot_ly(x = ~gender, y = ~race) %>%
  add_histogram2dcontour() 

NA
NA

Most shooters where white males.

C. This is a heatmap with gender and age of shooter

shootings %>% 
   mutate(gender = as_factor(gender)) %>% 
    mutate(gender = fct_collapse(gender,
                             Female = c( "F", "Female"),
                             Male = c("M", "Male"))) %>%
  plot_ly(x = ~gender, y = ~age_of_shooter) %>%
  add_histogram2dcontour() 

NA
NA
NA
NA

Most shooters were males around the age of 25.

D. Here is a scatterplot with the number injured by the number of fatalities in the shooting.

shootings %>% 
  plot_ly(x = ~injured, 
          y = ~fatalities,
          hoverinfo = "text", text = ~paste("Fatalities per shooting: ", fatalities, "<br>", "Year: ", year)) %>% 
  add_markers(showlegend = F) %>%
  layout(title = "Number of fatalities per shooting by year",
   xaxis = list(title = "Year"),
   yaxis = list(title = "Number of fatalities per shooting")) 

NA

There is a cluster with 2 extreme out lairs.

3. This is a regression analysis testing the hypothesis that the number of shootings has increased over the years.

num_per_year <- shootings %>% 
  filter(fatalities > 3) %>% 
  count(year)

num_per_year

Based on our data from the regression analysis and the graph, I would say within the past 2 year shootings have increased.