Shooting Maps

1. This first code just gathers the mass shooting information from the link and shortens it to ‘shootings’.

shootings <- read_csv("https://docs.google.com/spreadsheets/d/1b9o6uDO18sLxBqPwl_Gh9bnhW-ev_dABH83M5Vb5L8o/export?format=csv", na = "-")

This next code chunk creates a map of the shootings. In order to center the map on the continental U.S., I converted the coordinates from 39°48′38″N 98°33′22″W to 39.9 and -98.6. It also adds circle markers and changes their size based on total victims.

shootings %>% 
  leaflet() %>% 
  setView(lng = -98.6, lat = 39.9, zoom = 3.4) %>%
  addTiles() %>%
  addCircleMarkers(radius = ~log(total_victims))

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

2. This code finds the median total victims of the shootings.

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

This code finds the median fatalities for the shootings.

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

This code creates a histogram of the number of shootings per year.

shootings %>% 
  drop_na(year) %>%
  plot_ly(x = ~year) %>% 
  layout(title = "Number of Shootings per Year", xaxis = list(title = "Year"), yaxis = list(title = "Number of Shootings")) %>%
  add_histogram(nbinsx = 40)

This code creates a heatmap of the gender and race of the shooters.

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

This code creates a heatmap of the gender and age of the shooters.

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

This code creates a scatter plot of the number of injuries by fatalities in each mass shooting.

injured_data <- shootings %>%                                      
  filter(fatalities > 3) %>% 
  group_by(fatalities) %>%                                                  
  summarize(count = n(), injured = sum(injured)) %>%             
  mutate(injured_per_incident = injured/count)                   

injured_per_incident_model <- lm(injured_per_incident ~ fatalities, data = injured_data)

injuries_data %>% 
  plot_ly(x = ~fatalities, y = ~injured_per_incident,  hoverinfo = "text", text = ~paste("Injured per shooting: ", injured_per_incident, "<br>", "Fatalities: ", fatalities)) %>% 
  add_markers(showlegend = F) %>%
  layout(title = "Number of Injuries per Fatalities", xaxis = list(title = "Injuries"), yaxis = list(title = "Fatalitites")) %>%
  add_lines(y = ~fitted(injured_per_incident_model))

NA

3. This code finds the number of shootings per year and displays the data in a table.

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

num_per_year

This code creates a linear regression model for the data.

shootings_per_year_model <- lm(n ~ year, data = num_per_year) 
tidy(shootings_per_year_model)

glance(shootings_per_year_model)

This code takes the shootings per year data and displays it in a scatterplot.

num_per_year %>% 
  plot_ly(x = ~year, 
          y = ~n, hoverinfo = "text", text = ~paste("Number per Year: ", num_per_year, "<br>", "Year: ", year)) %>% 
  add_markers(showlegend = F) %>%
  layout(title = "Number of Shootings per Year",
   xaxis = list(title = "Year"),
   yaxis = list(title = "Number of Shootings")) %>%
  add_lines(y = ~fitted(shootings_per_year_model))