Google Trends data

Arkadiusz Wiśniowski
2025-09-17

Installing gtrendsR package

install.packages("gtrendsR")
# If not installed, the below packages may also need to be installed
install.packages("tidyr")
install.packages("dplyr")
install.packages("ggplot2")
install.packages("tibble")
install.packages("lubridate")

Loading packages needed for manipulation and plotting

library(gtrendsR)
library(tidyr)
library(dplyr)
library(ggplot2)
library(tibble)
library(lubridate)

Define search details

chosen keyword(s)

keyword = c("jobs in UK")

chosen geographic area

RO = Romania, PL = Poland, DE = Germany, FR = France, IT = Italy (large EU countries)

geo_area = c('RO','PL','DE','FR','IT')

chosen time period: 2012 - 2025

time_period = ("2012-01-01 2025-08-31")

preferred channel: web

channel='web'

Use gtrendsR to collect the data

trend_data = gtrends(keyword, gprop = channel, geo = geo_area, time = time_period)

Results

data over time

trend_over_time = trend_data$interest_over_time
head(trend_over_time)
        date hits    keyword geo                  time gprop category
1 2012-01-01   61 jobs in UK  RO 2012-01-01 2025-08-31   web        0
2 2012-02-01   46 jobs in UK  RO 2012-01-01 2025-08-31   web        0
3 2012-03-01   37 jobs in UK  RO 2012-01-01 2025-08-31   web        0
4 2012-04-01   44 jobs in UK  RO 2012-01-01 2025-08-31   web        0
5 2012-05-01   38 jobs in UK  RO 2012-01-01 2025-08-31   web        0
6 2012-06-01   38 jobs in UK  RO 2012-01-01 2025-08-31   web        0

plotting

plot <- ggplot(data = trend_over_time, aes(x = date, y = hits, group = keyword, col = keyword)) +
          geom_smooth(span = 0.2,se = FALSE) + xlab('Time') + ylab('Relative Interest') + 
          theme_bw() + theme(legend.title = element_blank(), legend.position = "bottom",
            legend.text = element_text(size = 12)) + ggtitle("Google Search Volume")
plot

convert to quarterly data

trend_quarterly <- trend_over_time %>%
  mutate(quarter = ceiling_date(date, "quarter")) %>%   # create quarter variable
  group_by(geo, keyword, quarter) %>%                 # group by quarter
  summarise(hits = mean(hits), .groups = "drop") %>%      # average interest in a quarter
  mutate(quarter=quarter+months(2))                  # add two months to match the migration data

Immigration from the EU to UK data

Source: ONS website, Data for Figure 3

immigration_df <- tribble(
  ~quarter,       ~Non_EU, ~EU,   ~British, ~Total,
  ymd("2012-06-01"), 238000, 327000,  74000,  639000,
  ymd("2012-09-01"), 232000, 322000,  76000,  630000,
  ymd("2012-12-01"), 230000, 334000,  79000,  643000,
  ymd("2013-03-01"), 224000, 350000,  75000,  649000,
  ymd("2013-06-01"), 222000, 373000,  75000,  669000,
  ymd("2013-09-01"), 233000, 410000,  78000,  720000,
  ymd("2013-12-01"), 234000, 402000,  76000,  713000,
  ymd("2014-03-01"), 236000, 414000,  80000,  731000,
  ymd("2014-06-01"), 236000, 420000,  82000,  737000,
  ymd("2014-09-01"), 239000, 441000,  81000,  761000,
  ymd("2014-12-01"), 241000, 457000,  81000,  778000,
  ymd("2015-03-01"), 240000, 457000,  83000,  780000,
  ymd("2015-06-01"), 241000, 454000,  83000,  778000,
  ymd("2015-09-01"), 240000, 453000,  81000,  774000,
  ymd("2015-12-01"), 238000, 481000,  78000,  797000,
  ymd("2016-03-01"), 235000, 490000,  78000,  803000,
  ymd("2016-06-01"), 237000, 521000,  74000,  832000,
  ymd("2016-09-01"), 238000, 496000,  70000,  804000,
  ymd("2016-12-01"), 237000, 465000,  70000,  772000,
  ymd("2017-03-01"), 239000, 447000,  72000,  759000,
  ymd("2017-06-01"), 241000, 399000,  78000,  718000,
  ymd("2017-09-01"), 251000, 425000,  78000,  754000,
  ymd("2017-12-01"), 257000, 411000,  84000,  752000,
  ymd("2018-03-01"), 261000, 399000,  76000,  736000,
  ymd("2018-06-01"), 269000, 407000,  81000,  757000,
  ymd("2018-09-01"), 284000, 397000,  85000,  766000,
  ymd("2018-12-01"), 330000, 421000,  74000,  825000,
  ymd("2019-03-01"), 346000, 429000,  74000,  849000,
  ymd("2019-06-01"), 347000, 413000,  61000,  821000,
  ymd("2019-09-01"), 351000, 376000,  66000,  793000,
  ymd("2019-12-01"), 368000, 349000,  71000,  788000,
  ymd("2020-03-01"), 388000, 347000,  80000,  815000,
  ymd("2020-06-01"), 331000, 333000,  72000,  736000,
  ymd("2020-09-01"), 260000, 293000,  53000,  606000,
  ymd("2020-12-01"), 294000, 316000,  52000,  662000,
  ymd("2021-03-01"), 297000, 287000,  44000,  629000,
  ymd("2021-06-01"), 368000, 279000,  62000,  709000,
  ymd("2021-09-01"), 534000, 249000,  96000,  878000,
  ymd("2021-12-01"), 611000, 172000, 108000,  891000,
  ymd("2022-03-01"), 721000, 138000, 110000,  968000,
  ymd("2022-06-01"), 848000, 121000, 108000, 1078000,
  ymd("2022-09-01"), 990000, 114000,  94000, 1198000,
  ymd("2022-12-01"),1053000, 116000,  88000, 1257000,
  ymd("2023-03-01"),1033000, 128000,  87000, 1248000,
  ymd("2023-06-01"),1025000, 129000,  84000, 1238000,
  ymd("2023-09-01"),1038000, 117000,  64000, 1219000,
  ymd("2023-12-01"),1031000, 126000,  61000, 1218000
)
trend_joint <- trend_quarterly %>%
  group_by(quarter) %>%
  summarise(hits=sum(hits)) %>%     # sum all interest across all five countries
  ungroup() %>%
  left_join(immigration_df) %>%     # join with ONS immigration data 
  mutate(hits=scale(hits),
         immi_EU=scale(EU)) %>%
  select(quarter,hits,immi_EU) %>%
  pivot_longer(cols = hits:immi_EU, names_to = c("series"), values_to = "value") 
plot <- ggplot(data = trend_joint, aes(x = quarter, y = value, group = series, col = series)) +
          geom_smooth(span = 0.1,se = FALSE) + xlab('Time') + ylab('Relative Interest') + 
          theme_bw() + theme(legend.title = element_blank(), legend.position = "bottom",
            legend.text = element_text(size = 12)) + ggtitle("Google Search Volume")
plot