My goal is to compare the interest over time regarding some animal groups, and, for this, I will utilize gtrendsR package. As I intend to include more than five groups in my research (i.e., more than five keywords in Google Trends), thus exceeding the search queries limit allowed by Google Trends, I must to perform different sets of searches, each containing five keywords, one of which needs the one with the peak value. These are my R commands, regarding the data collection (Lion got the highest peak value):
> library(gtrendsR)
> # Setting the search terms:
> keywords_1 <- c("Lion", "Butterfly", "Cockroach", "Parrot", "Ostrich")
> keywords_2 <- c("Lion", "Platypus", "Alligator", "Hyena", "Horse")
> country <- c('BR') #setting the geographic area (Brazil).
> time <- ("2011-01-01 2021-12-31") #setting the period.
> channel <- 'web' #setting the channels.
> Running the queries:
> data1 <- gtrends(keywords_1, gprop = channel, geo = country, time = time, category = 0)
> data2 <- gtrends(keywords_2, gprop = channel, geo = country, time = time, category = 0)
At this point, my question is how can I merge data1 and data2 into a single dataset. I know that all data must be normalized to the peak value, which is Lion; but how can I perform this normalization in R?