-### COVID-19 Digital Observatory
-### 2020-03-28
-###
-### Minimal example analysis file using trending search data
-
-library(tidyverse)
-
-### Import and cleanup data
-
-
-related.searches.top = read_csv("https://github.com/CommunityDataScienceCollective/COVID-19_Digital_Observatory/raw/master/keywords/output/intermediate/related_searches_top.csv")
-
-
-## Plot how often the top 10 queries appear in the top 10 suggested list each day
-
-plot <- related.searches.top %>%
- group_by(term, date) %>% # Group by term and date
- arrange(-value) %>% # Sort by value (this should already be done anyway)
- top_n(10) %>% # Get the top 10 queries for each term-day pair
- group_by(query) %>% # Group by again, this time for each query
- summarize(appearances = n()) %>% # Count how often this query appears in the top 10 (which is how many Google displays)
- arrange(-appearances) %>% # Sort by appearances
- top_n(10) %>% # And get the top 10 queries
- ggplot(aes(x=reorder(query, appearances), y=appearances)) + # Plot the number of appearances, ordered by appearances
- geom_bar(stat = 'identity') + # Tell R that we want to use the values of `appearances` as the counts
- coord_flip() + # Flip the plot
- xlab("Query") +
- ylab("Number of appearances in top 10 suggested queries") +
- theme_minimal() # And make it minimal
-
-ggsave('./output/top_queries_plot.png', plot)
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