R Notebook

Assignment: Use the Las Vegas shooting of 2017 and the Florida high school shooting of early 2018. Look up the dates when those happened, and do the following:

library(tidyverse)
library(pageviews)       
library(DT)

Create a graph of views of the Gun control article on wikipedia over a several year period, like we did for Influenza.

gun_control_data <- article_pageviews(article= "Gun control", start = as.Date("2014-1-1"), end= as.Date("2018-12-31"))

gun_control_data %>% 
  ggplot(aes(x=date, y=views))+
  geom_line()+
  labs(x= "Date", y= "Article Views", title= "'Gun control' on Wikipedia views")

Here is a graph of gun control article hits on Wikipedia form January 1st, 2014, to December 31st, 2018. There are notable spikes at various points throughout the graph with the largest spike coming in early 2018, presumably following the Parkland high school shooting in early 2018.

Create a table showing the highest days for viewing the Gun control article.

gun_control_data %>%
  select(article, views, date) %>% 
  datatable(class = "cell-border stripe") %>%
  formatStyle("views", backgroundColor = "lightgoldenrodyellow")

NA
NA

Here’s a datatable showing the highest days that the “gun control” article was viewed. The highest day (with nearly 7,000 views) was February 16th, 2018, two days following the Parkland shooting.

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

Here’s a table showing the highest days that the “gun control” article was viewed. The highest day (with nearly 7,000 views) was February 16th, 2018, two days following the Parkland shooting.

Look at top_articles the next day after the Vegas and Florida shootings to see if people are searching for information about those events. Create two tables using datatable() of the top articles for those two days.

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

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

Florida_shooting %>% 
select(article, views) %>% 
  datatable(class = "cell-border stripe") %>% 
  formatStyle("views", backgroundColor = "lightgoldenrodyellow")

Here is the datatable for the Parkland, Florida shooting on Febrary 14th, 2018. Although the Black Panther film had the highest views, three of the top ten page views for the next day were about mass shootings or guns.

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

Vegas_shooting <- Vegas_shooting %>% 
  select(article, views) %>% 
  filter(!article=="Main_Page", !article=="Special:Search", !article=="Special:Book")

Vegas_shooting %>% 
select(article, views) %>% 
  datatable(class = "cell-border stripe") %>% 
  formatStyle("views", backgroundColor = "lightgoldenrodyellow")

Here is the datatable for the Las Vegas shooting on October 1st, 2017. The next day, only one of the top ten searches was directly related to the shooting, but another top search (Deaths_in_2017) is somewhat related. Tom Petty’s death seemed to overshadow the horrific events in Las Vegas.

Compare Wikipedia views of the Gun control article 1 week before and 2 weeks after the Vegas & Florida shootings with a ggplot.

Florida <- article_pageviews(article= "Gun_control", start = as.Date("2017-2-7"), end = as.Date("2017-2-28"))

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

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

Here is the Wikipedia article data following the Florida shooting. It seems to fluctuate up and down every few days following the event, and reached it’s highest point two weeks after the shooting.

Vegas <- article_pageviews(article= "Gun_control", start = as.Date("2018-9-24"), end = as.Date("2018-10-15"))

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

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

NA

Here is the Wikipedia article data following the Las Vegas shooting. It too seems to fluctuate up and down following the event. Out of these two weeks, it’s highest peak was about eight days after the shooting, but it’s lowest point was in the days leading up to the event.

V_F_shootings <- bind_rows(Vegas, Florida)

V_F_shootings %>% 
  ggplot(aes(x=day, y=views, color= event))+
  geom_line()+
  labs(x = "Days Before and After the Shooting", 
       y = "Wikipedia Article Views", 
       color = "Event", 
       title = "Views of Wikipedia articles before and after two mass shootings")

Here is a comparison of Wikipedia article views from both the Florida and Las Vegas shootings. Interestingly, they seem to fluctuate in a nearly identical pattern in terms of views over time. I would be willing to bet it has something to do with the news cycle and reporting as new information becomes available to the public, but that’s only speculation.

Publish the results to rpubs.com, and make sure you annotate the document to explain what you did.

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