R Notebook

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 

getSymbols(c("GOOG","AMZN","TSLA","GE"), src="yahoo")

[1] "GOOG" "AMZN" "TSLA" "GE"  

getSymbols("MSFT")

[1] "MSFT"

Which of the following exactly return(s) the daily stock prices for Microsoft from 2023-10-01 to 2024-01-31?

getSymbols("MSFT", src="yahoo",from = "2023-10-01", to = "2024-01-31")

[1] "MSFT"

n_days <- nrow(MSFT)
print(n_days)

[1] 83

Could you find out in which time period did Microsoft’s Open prices reach the highest?

plot(MSFT$MSFT.Open, main = "Microsoft Open Prices", 
     xlab = "Date", ylab = "Open Price", type = "l", 
     col = "blue", lwd = 2)

Now you will write the R code to extract the google trends data for Microsoft during the same date range from 2023-10-01 to 2024-01-31. Based on the google trends data you obtained for Microsoft, on which date does the number of search hits reached the highest?

install.packages("gtrendsR")

trying URL 'https://cran.rstudio.com/bin/macosx/big-sur-arm64/contrib/4.4/gtrendsR_1.5.1.tgz'
Content type 'application/x-gzip' length 1986574 bytes (1.9 MB)
==================================================
downloaded 1.9 MB

The downloaded binary packages are in
    /var/folders/hs/pzrbrc311xx9fwc7pk63t_2h0000gn/T//RtmpYH0NWs/downloaded_packages

library(gtrendsR)

trends_data <- read.csv(multiTimeline.csv)

Error: object 'multiTimeline.csv' not found

Based on our class demos and your coding experience obtained from the above questions (extract data via quantmod or gtrendsR), try writing the R code to get another online data source of Wikipedia views data for Microsoft in English language from 2023-10-01 to 2024-01-31? Once you successfully extract this data, how many observations and variables did you get?

Based on your extracted data above, how many days in total are with a Wikipedia views over 30,000? (Hint: You may use the subset() function)

# Filter for days with views over 30,000
high_views <- subset(microsoft_pageviews, views > 30000)

# Check the number of days with views over 30,000
num_high_view_days <- nrow(high_views)
num_high_view_days

[1] 1

Given the Stock prices and the Wikipedia views data you extracted above for Microsoft from 2023-10-01 to 2024-01-31, is it ok to directly combine these two data objects as one dataframe?

# Check the dimensions of each data frame
pageviews_dimensions <- dim(microsoft_pageviews)
stock_prices_dimensions <- dim(microsoft_stock_prices)

Error: object 'microsoft_stock_prices' not found