R Notebook

library(leaflet)
library(tidyverse)
library(lubridate)
library(htmltools)
library(rtweet)
library(readxl)
library(broom)
library(plotly)
library(DT)

1. The first step I took was to read in the data about the mass shootings.

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

58/60

[1] 0.9666667

## [1] 0.9666667
2/(60*60)

[1] 0.0005555556

## [1] 0.0005555556
46/60

[1] 0.7666667

## [1] 0.7666667
18/(60*60)

[1] 0.005

## [1] 0.005

2. After finding the correct coordinate points (above), you are able to view the whole country.

leaflet() %>% 
  addTiles() %>% 
  setView(lat= 44.967, lng = -103.771, zoom = 4)

3. Ass the addCircleMarkers() line in order to see locations of the US shootings.

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

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

4. Next, gather a bit more info about the shootings. I found the median for total number of victims, and the median of fatalities. I also found the mean number of total victims as well as the mean number of fatalities.

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

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

shootings %>% 
  summarize(mean_total_victims = mean(total_victims)) 

shootings %>% 
  summarize(mean_fatalities = mean(fatalities)) 

5. Plot the data by year.

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

6. Next, I created a thermal graph to show gender and race. The second thermal graph is based on age and gender.

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

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

7. Next, combine the data.

shootings %>% 
  mutate(gender = as_factor(gender)) %>% 
  mutate(gender = fct_collapse(gender, Male = c("Male", "M"), Female = c("Female", "F"))) %>% 
  count(gender)

8. Here I created a scatterplot of the number of victims injured, and the number of fatalities in the shooting.

shootings %>% 
  plot_ly(x = ~fatalities, 
          y = ~injured) %>% 
  add_markers()

9. Here, I input the data in order to calculate how many shootings there have been for each year.

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

num_per_year

10. Finally, I did a regression model for the data, and added a bit more info below for clarity.

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

num_per_year_model

Call:
lm(formula = n ~ year, data = num_per_year)

Coefficients:
(Intercept)         year  
  -218.8464       0.1107  

tidy(num_per_year_model)

glance(num_per_year_model)