Assignment2_LibbyCamardella

library(quantmod)

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getSymbols("MSFT", src="yahoo", from = "2024-10-01", to = "2025-02-01")

## [1] "MSFT"

nrow(MSFT)

## [1] 84

plot(MSFT$MSFT.Open)

library(gtrendsR)
library(dplyr)

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library(ggplot2)
library(lubridate)

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Explored the Google Trends using the R package gtrendsR which gives access to Google Trends API. I chose the streaming services topic to see how platforms such as Netflix, Diseney Plus, and HBO Max compete, specifically within the U.S. and this timeframe. These are the key words I specified in my code.

keywords <- c("Netflix", "Disney Plus", "HBO Max")
geo <- "US"      
timewin <- "2024-01-01 2025-09-21"  
gprop <- "web"     

gt <- gtrends(keyword = c("Netflix","Disney Plus","HBO Max"),
              geo = "US",
              time = "2024-01-01 2025-09-21")
iot <- gt$interest_over_time

The summary_tbl output pinpoints each service’s peak date and average interest score.

summary_tbl <- iot %>%
  group_by(keyword) %>%
  summarise(
    points = n(),
    peak_hits = max(hits, na.rm = TRUE),
    peak_date = date[which.max(hits)],
    avg_hits  = mean(hits, na.rm = TRUE),
    .groups = "drop"
)
print(summary_tbl)

## # A tibble: 3 × 5
##   keyword     points peak_hits peak_date           avg_hits
##   <chr>        <int>     <int> <dttm>                 <dbl>
## 1 Disney Plus     91        12 2024-11-24 00:00:00     7.49
## 2 HBO Max         91         5 2023-12-31 00:00:00     3.43
## 3 Netflix         91       100 2025-07-06 00:00:00    51.1

I plotted the results with ggplot.

ggplot(iot, aes(date, hits, color = keyword)) +
  geom_line(linewidth = 1) +    # <-- replace size with linewidth
  labs(
    title = "Google Trends: Streaming Wars",
    subtitle = paste0("Geo: ", geo, " | Time: ", timewin),
    x = "Date", y = "Interest (0–100, scaled)"
  ) +
  theme_minimal()

The plot shows Netflix consistently leading in search interest, Disney Plus peaks around new movie or show releases, and HBO Max remains lower overall but spikes during major series premieres