Google trends data full#
I’ll now re-run this piece of analysis, using the shorter term 榮光, as the hypothesis is that people are more likely to search for that instead of the full song name. This finding above is surprising, because you would expect that Hong Kong people are more likely to search for the Chinese term rather than the English term, as the original piece was written in Cantonese. $interest_over_time %>% ggplot( aes( x = date, y = hits)) + geom_line( colour = "darkblue", size = 1.5) + facet_wrap( ~keyword) + ggthemes :: theme_economist() -> plot I really like the Economist theme from ggthemes, so I’ll use that: Let us plot this in ggplot2, just to try and replicate what we normally see on the Google Trends site - i.e. visualising the search trends over time. The resulting numbers are then scaled on a range of 0 to 100 based on a topic’s proportion to all searches on all topics. Otherwise, places with the most search volume would always be ranked highest. Search results are normalized to the time and location of a query by the following process:Įach data point is divided by the total searches of the geography and time range it represents to compare relative popularity. Google Trends normalizes search data to make comparisons between terms easier. This is what the hits variable represents, according to Google’s FAQ documentation: # $ keyword "Glory to Hong Kong", "Glory to Hong Kong", "Glory to. $interest_over_time %>% glimpse() # Observations: 104