API 1

This is an R Markdown Notebook. When you execute code within the notebook, the results appear beneath the code.

library(tidyverse)
library(pageviews)        # This package gets data on Wikipedia viewing
library(DT)               # DT stands for datatable, and creates interactive tables

To start out with, I made a graph that would represent the number of people who search “Gun Control” on wikipedia, from seven days before the Las Vegas shooting, to 14 days after.

nevada <- article_pageviews(article = "Gun_control",
                           start = as.Date("2017-9-24"),
                           end = as.Date("2017-10-15"))

In order to make the day of the shootig day zero, our graph will have to go from -7 days to 14 days.

nevada <- nevada %>% 
  mutate(day = -7:14) %>% 
  mutate(event = "Las Vegas")

Finally, I ran the code in order to create a visual of the views around this time. As you can see, views of the Gun Control article spiked the day of the shooting and continued to increase for about 2-3 days after before it began to decline in views.

nevada %>% 
  ggplot(aes(x = date, y = views)) +
  geom_line() +
  labs(x = "Date", y = "Wikipedia Views", title = "Views of Wikipedia's Gun Control Article")

Next, I did the exact same process as above, but instead of using the Las Vegas incident as the focal point, I used the Parkland school shooting in Florida. This will give an opportunity to compare the two events.

Much lke before, Gun Control article views spiked from day zero to about day two.

 florida <- article_pageviews(article = "Gun_control",
                           start = as.Date("2018-2-7"),
                           end = as.Date("2018-2-28"))

florida <- florida %>% 
  mutate(day = -7:14) %>% 
  mutate(event = "Florida")

florida %>% 
  ggplot(aes(x = day, y = views)) +
  geom_line()

Lastly, we created a graph to show the amount of views over a week before the first shooting in Nevada, to two weeks after the Florida shooting.

shootings <- article_pageviews(article = "Gun_control", start = as.Date("2017-9-24"), end = as.Date("2018-2-28"))

glimpse(flu)

Observations: 1,097
Variables: 8
$ project     [3m[90m<chr>[39m[23m "wikipedia", "wikipedia", "wikipedia", "wikipedia", "wikipedia", "wikipedia", "wikipedia", "wikipedia", "wikipedia"…
$ language    [3m[90m<chr>[39m[23m "en", "en", "en", "en", "en", "en", "en", "en", "en", "en", "en", "en", "en", "en", "en", "en", "en", "en", "en", "…
$ article     [3m[90m<chr>[39m[23m "Influenza", "Influenza", "Influenza", "Influenza", "Influenza", "Influenza", "Influenza", "Influenza", "Influenza"…
$ access      [3m[90m<chr>[39m[23m "all-access", "all-access", "all-access", "all-access", "all-access", "all-access", "all-access", "all-access", "al…
$ agent       [3m[90m<chr>[39m[23m "all-agents", "all-agents", "all-agents", "all-agents", "all-agents", "all-agents", "all-agents", "all-agents", "al…
$ granularity [3m[90m<chr>[39m[23m "daily", "daily", "daily", "daily", "daily", "daily", "daily", "daily", "daily", "daily", "daily", "daily", "daily"…
$ date        [3m[90m<dttm>[39m[23m 2015-07-01, 2015-07-02, 2015-07-03, 2015-07-04, 2015-07-05, 2015-07-06, 2015-07-07, 2015-07-08, 2015-07-09, 2015-0…
$ views       [3m[90m<dbl>[39m[23m 2693, 3025, 2185, 1944, 2609, 2893, 3288, 2762, 2753, 2336, 1931, 2119, 2635, 2639, 2677, 2348, 2112, 1788, 2129, 2…

shootings %>% 
  ggplot(aes(x = date, y = views)) +
  geom_line()

The second step was to create a table that would show the days with the most views of the Gun Control article relative to the two events.

shootings %>%
  arrange(-views)

Next, we want to look at the top articles search the day after each shooting in order to see if people are searching for information on the event. As you can see, the Las Vegas Strip shooting was the 4th most popuar

top_articles(start = as.Date("2017-10-2"))


top %>% 
  select(article, views) %>%
  filter(!article == "Main_Page", !article == "Special:Search")


top %>% 
  select(article, views) %>%
  filter(!article == "Main_Page", !article == "Special:Search") %>% 
  top_n(10, views)

NA

As you can see, when we search for the most searched articles after the shooting in Florida, there is a great deal of searches related to mass shootings, including past shootings and AR-15’s.

top_articles(start = as.Date("2018-2-15"))


top <- top_articles(start = as.Date("2018-2-15")) 

top %>%
  select(article, views)


top %>% 
  select(article, views) %>%
  filter(!article == "Main_Page", !article == "Special:Search")

NA

For the last step, we want to compare the two shootings together. Searches for Gun Control peak about day 0 to 3 on both graphs.

shootings <- bind_rows(nevada, florida)

shootings %>% 
  ggplot(aes(x = day, y = views, color = event)) +
  geom_line() +
  theme_minimal() +
  labs(x = "Days before/after Shooting", 
       y = "Wikipedia Views", 
       color = "Event", 
       title = "Views of the Wikipedia Gun Control Article before and after Two Mass Shootings")

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