library(quantmod)
## Loading required package: xts
## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
## Loading required package: TTR
## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo
library(gtrendsR)
library(ggplot2)
library(lubridate)
##
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union
getSymbols('LNC', src = 'yahoo',
from = Sys.Date() - years(5), to = Sys.Date())
## [1] "LNC"
LNC <- data.frame(
LNC,
date = as.Date(rownames(data.frame(LNC)))
)
# View the data
head(LNC)
## LNC.Open LNC.High LNC.Low LNC.Close LNC.Volume LNC.Adjusted
## 2020-10-30 33.64 35.16 33.50 35.10 2721300 27.47414
## 2020-11-02 35.79 36.59 34.77 36.37 1870700 28.46821
## 2020-11-03 37.14 38.28 37.03 37.39 2133000 29.26661
## 2020-11-04 36.01 36.90 34.63 35.03 2770700 27.41934
## 2020-11-05 36.00 36.80 35.10 35.90 3318600 28.10033
## 2020-11-06 36.27 36.69 33.75 34.07 3075200 26.66792
## date
## 2020-10-30 2020-10-30
## 2020-11-02 2020-11-02
## 2020-11-03 2020-11-03
## 2020-11-04 2020-11-04
## 2020-11-05 2020-11-05
## 2020-11-06 2020-11-06
tail(LNC)
## LNC.Open LNC.High LNC.Low LNC.Close LNC.Volume LNC.Adjusted
## 2025-10-22 39.72 39.9500 38.940 39.42 1209800 39.42
## 2025-10-23 39.32 39.7400 39.270 39.35 893600 39.35
## 2025-10-24 39.65 40.3300 39.650 39.93 834900 39.93
## 2025-10-27 40.34 40.6200 39.760 40.32 1226700 40.32
## 2025-10-28 40.36 40.3600 39.670 40.12 1329400 40.12
## 2025-10-29 39.95 40.6612 39.595 40.03 1425187 40.03
## date
## 2025-10-22 2025-10-22
## 2025-10-23 2025-10-23
## 2025-10-24 2025-10-24
## 2025-10-27 2025-10-27
## 2025-10-28 2025-10-28
## 2025-10-29 2025-10-29
ggplot(LNC, aes(date)) +
geom_line(aes(y = LNC.Adjusted))
ggplot(LNC, aes(date)) +
geom_line(aes(y = LNC.Close))
#now let's predict Lincoln Financials stock price with Google Trends search queries
library(gtrendsR)
trends.dat <- gtrends('AI', 'US')$interest_over_time
# View the data
head(trends.dat)
## date hits keyword geo time gprop category
## 1 2020-10-25 2 AI US today+5-y web 0
## 2 2020-11-01 2 AI US today+5-y web 0
## 3 2020-11-08 2 AI US today+5-y web 0
## 4 2020-11-15 2 AI US today+5-y web 0
## 5 2020-11-22 2 AI US today+5-y web 0
## 6 2020-11-29 3 AI US today+5-y web 0
tail(trends.dat)
## date hits keyword geo time gprop category
## 257 2025-09-21 83 AI US today+5-y web 0
## 258 2025-09-28 81 AI US today+5-y web 0
## 259 2025-10-05 77 AI US today+5-y web 0
## 260 2025-10-12 72 AI US today+5-y web 0
## 261 2025-10-19 67 AI US today+5-y web 0
## 262 2025-10-26 70 AI US today+5-y web 0
p <- ggplot(trends.dat, aes(date)) +
geom_line(aes(y = hits))
library(plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
ggplotly(p)