X-Git-Url: https://code.communitydata.science/covid19.git/blobdiff_plain/ff96d52cb92966dd0c487e6452aeab70773cf3e6:/keywords/analysis/related_searches_tidyverse_example.R..f770ade87a6e06828f015147f28c1a8334878731:/keywords/example_analysis/static/git-logo.png

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-### 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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