I am using Google Trends to compare internet search trends on ten terms. Since Google Trends allows a maximum of five terms per query, I thought I would run two queries (each with five terms), and then merge all the results into a single dataset. However, this method would be incorrect, as the results for each term are normalized to the term with the highest search volume in each query. To get around this, I intend to:
Identify which of my ten terms has the highest search volume;
Perform three queries, including the most searched term in all of them, to control the other terms.
In this way, it would be possible to unite all the results in the same dataset, since they would all be properly normalized. In R, my commands would look like this (using the gtrendsR package):
library(gtrendsR)
# Setting the search terms:
keywords_1 <- c("Term with the highest peak value", "Term_2", "Term_3", "Term_4", "Term_5")
keywords_2 <- c("Term with the highest peak value", "Term_6", "Term_7", "Term_8", "Term_9")
keywords_3 <- c("Term with the highest peak value", "Term_10")
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)
data3 <- gtrends(keywords_3, gprop = channel, geo = country, time = time, category = 0)
Then, I would merge the three datasets above, excluding Term with the highest peak value from data2 and data3. My question is: Is there any way to identify in R which of the ten terms has, comparatively, the highest peak value?