Riesen Shootings Assignment

Assignment: Shootings.

For your notebook, do some further analyses on the shootings data.

1. Create a map of the shootings. There are columns called latitude and longitude in the data that can generate dots on a map of where the shootings took place.

A. Because there was a shooting in Hawaii, the map is spread out a lot by default. Center on the continental US by looking up the center of the continental US, translating it to computer, finding a good zoom level, and using setView(lng = x, lat = y, zoom = z).

B. Change the size of the dots so that the more victims, the larger the dot on the map. You can do this by setting radius = ~fatalities or radius = ~total_victims inside of addCircleMarkers(). If the dots are too big, you can do something like radius = ~total_victims/10, or radius = ~log(total_victims). I like that last one because larger numbers are reduced more than smaller ones.

2. Report some additional statistics, including:
   A. Median number of total_victims and median number of fatalities.
   B. A histogram of the number of shootings per year. You will want to set nbinsx = 40 inside add_histogram(), because that is the approx. number of years covered by the data.
   C. Heatmaps with add_histogram2dcontour() of the gender and race of the shooter, and the gender and age of the shooter. You’ll notice some problems with the data that you’ll need to fix with fct_collapse().
   D. A scatterplot with plotly of the number injured by the number of fatalities in the shooting.

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

To create the regression model, y = the number of shootings and x = year, you need to set up the data like this:

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

num_per_year

shootings %>%
  leaflet() %>%
  addTiles() %>%
  addCircleMarkers

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

39.828175, -98.5795

shootings %>%
  leaflet() %>%
  addTiles() %>%
  addCircleMarkers %>%
setView(lat = 39.828175, lng = -98.5795, zoom = 3) %>%
  addMarkers(lat = 39.828175, lng = -98.5795)

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

shootings %>% 
  leaflet() %>% 
  addTiles() %>%
addCircleMarkers(radius = ~fatalities)

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

shootings %>% 
  leaflet() %>% 
  addTiles() %>%
addCircleMarkers(radius = ~log(total_victims))

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

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

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

A histogram of the number of shootings per year. You will want to set nbinsx = 40 inside add_histogram(), because that is the approx. number of years covered by the data.

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

Heatmaps with add_histogram2dcontour() of the gender and race of the shooter, and the gender and age of the shooter. You’ll notice some problems with the data that you’ll need to fix with fct_collapse()

shootings %>% 
  plot_ly(x = ~age_of_shooter, y = ~gender) %>% 
  add_histogram2dcontour()

NA

shootings %>% 
  plot_ly(x = ~age_of_shooter, y = ~race) %>% 
  add_histogram2dcontour()

D. A scatterplot with plotly of the number injured by the number of fatalities in the shooting.

fatalities_data <- shootings %>%                                       # start with the shootings data
  group_by(injured) %>%                                                   # we're going to count by year
  summarize(count = n(), fatalities = sum(fatalities)) %>%             # get the total number of fatalities per year
  mutate(fatalities_per_incident = fatalities/count)                   # divide by the number of shooting incidents

fatalities_data

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

fatalities_data

fatalities_per_incident_model <- lm(fatalities_per_incident ~ injured, data = fatalities_data)

fatalities_per_incident_model

Call:
lm(formula = fatalities_per_incident ~ injured, data = fatalities_data)

Coefficients:
(Intercept)      injured  
    9.64036      0.09381  

fatalities_data %>% 
  plot_ly(x = ~injured, 
          y = ~fatalities_per_incident) %>% 
  add_markers() %>%
add_lines(y = ~fitted(fatalities_per_incident_model))

NA

Conduct a regression analysis testing the hypothesis that the number of shootings has increased over the years.

To create the regression model, y = the number of shootings and x = year, you need to set up the data like this:

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

num_per_year

fatalities_data <- shootings %>%                                       # start with the shootings data
  group_by(year) %>%                                                   # we're going to count by year
  summarize(count = n(), fatalities = sum(fatalities)) %>%             # get the total number of fatalities per year
  mutate(fatalities_per_incident = fatalities/count)                   # divide by the number of shooting incidents

fatalities_data

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

Unknown or uninitialised column: 'fitted'.Unknown or uninitialised column: 'na.action'.Error: All columns in a tibble must be 1d or 2d objects:
* Column `y` is NULL