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shooting <- article_pageviews(article = "Gun Control", start = as.Date("2015-7-1"), end = as.Date("2019-7-1"))

glimpse(shooting)

Rows: 1,439
Columns: 8
$ project     <chr> "wikipedia", "wikipedia", "wikipedia", "wikipedia", "wikipedia", "wikipedia", "wikipedia", "wikipedia~
$ language    <chr> "en", "en", "en", "en", "en", "en", "en", "en", "en", "en", "en", "en", "en", "en", "en", "en", "en",~
$ article     <chr> "Gun_Control", "Gun_Control", "Gun_Control", "Gun_Control", "Gun_Control", "Gun_Control", "Gun_Contro~
$ access      <chr> "all-access", "all-access", "all-access", "all-access", "all-access", "all-access", "all-access", "al~
$ agent       <chr> "all-agents", "all-agents", "all-agents", "all-agents", "all-agents", "all-agents", "all-agents", "al~
$ granularity <chr> "daily", "daily", "daily", "daily", "daily", "daily", "daily", "daily", "daily", "daily", "daily", "d~
$ date        <dttm> 2015-07-01, 2015-07-02, 2015-07-03, 2015-07-04, 2015-07-05, 2015-07-06, 2015-07-07, 2015-07-09, 2015~
$ views       <dbl> 4, 5, 3, 2, 2, 2, 5, 3, 2, 5, 2, 3, 1, 3, 3, 1, 4, 4, 4, 4, 3, 1, 3, 1, 1, 7, 5, 1, 1, 2, 4, 3, 2, 2,~

By using this command above, I am able to see the page views of the article “Gun Control” from 2015-2019.

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

Using the command above, I converted the data of views of the “Gun Control” article into a graph. Now, I am going to add a title to differentiate which article this graph is for.

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

Now, I am going to convert the data into a table to show which days the Gun Control article had the most views

shooting %>%
  arrange(-views)

This shows that the date with the most views of the article was july 19th, 2018. Now, we are going to look at the top articles for the day after the Vegas shooting in 2017 and the Florida High school shooting of 2018.

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

This just ran the data so it can be saved for the next step. Nothing happened which is perfectly fine. Now, we will see the top articles.

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

This shows the top articles viewed the day after the Vegas shooting.

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

THis shows the data in a table so it is more organized. Next, lets do the date after the Florida High School Shooting

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

Once again, this will not show anything. Now, the data will show

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

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

Now the data for the day after the Florida shooting is more organized as well. Next, we will plot the views of the GUn Control article one week before and two weeks after each of the shootings

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

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

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

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()

shootings <- bind_rows(Vegas, 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")

After plotting the views of the Gun Control article for one week before and two weeks after each of the shootings, the above command combined them to be on the same graph and makes a more organized presentation of the views each day around the time of each mass shooting.