APratt dot_maps Assignment

This is an R Markdown Notebook.

shootings %>% 
  leaflet() %>% 
  addTiles() %>% 
  setView(lat = 40, lng = -98.5, zoom = 4) %>% 
  addCircleMarkers(stroke = F, 
                   fillOpacity = .6, 
                   radius = ~total_victims/10,
                   label = ~case)

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

Middle of the US is at the Kansas-Nebraska border. I heavily rounded. The biggest dot is the Las Vegas Shooting.

median_total_victims <- median(shootings$total_victims)

median_total_victims

[1] 10.5

Median number of victims since the 80’s.

median_fatalities <- median(shootings$fatalities)

median_fatalities

[1] 6

Median number of fatalities.

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

Wow, it’s almost like we have unprecedented gun crime in the last three years. Crazy.

shootings %>% 
  mutate(gender = as_factor(gender)) %>% 
  mutate(gender = fct_collapse(gender, 
                               Female = c("Female", "F"), 
                               Male = c("Male", "M"),
                               Unknown = c("unclear", "-"))) %>% 
  mutate(race = as_factor(race)) %>% 
  mutate(race = fct_collapse(race, 
                             White = c("White", "White"), 
                             Black = c("Black", "Black"),
                             Other = c("-", "unclear"))) %>%
  plot_ly(x = ~gender, y = ~race) %>% 
  add_histogram2dcontour() %>% 
  layout(title = "Shootings by Gender and Race",
         xaxis = list(title = "Gender"),
         yaxis = list(title = "Race"))

Unknown levels in `f`: unclear

Can we collectively agree to keep guns away from white dudes, though?

shootings %>% 
  plot_ly(x = ~fatalities, y = ~injured,
          hoverinfo = "text",
          text = ~paste("Injuries:", injured , "<br>", "Fatalities:", fatalities)) %>% 
  add_markers() %>% 
  layout(title = "Shootings Injuries vs Fatalities",
         xaxis = list(title = "Fatalities"),
         yaxis = list(title = "Injuries"))

Scatterplot of injuries vs fatalities. The major outlier (again) is the Vegas shooting.

num_per_year <- shootings %>% 
  filter(fatalities > 3) %>% 
  count(year) %>% 
  filter(year < 2019)

num_per_year

NA

Mass shootings since ’82.

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

tidy(num_per_year_model)

NA

glance(num_per_year_model)

NA

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

1982 to current with a regression line. 2018 was a bad year.