Assignment 5

Download Microsoft’s daily stock prices

getSymbols("MSFT",
           src = "yahoo",
           from = "2025-10-01",
           to = "2026-02-01")

## [1] "MSFT"

Display the stock data

head(MSFT)

##            MSFT.Open MSFT.High MSFT.Low MSFT.Close MSFT.Volume MSFT.Adjusted
## 2025-10-01    514.80    520.51   511.69     519.71    22632300      515.4689
## 2025-10-02    517.64    521.60   510.68     515.74    21222900      511.5312
## 2025-10-03    517.10    520.49   515.00     517.35    15112300      513.1281
## 2025-10-06    518.61    531.03   518.20     528.57    21388600      524.2566
## 2025-10-07    528.29    529.80   521.44     523.98    14615200      519.7040
## 2025-10-08    523.28    526.95   523.09     524.85    13363400      520.5669

tail(MSFT)

##            MSFT.Open MSFT.High MSFT.Low MSFT.Close MSFT.Volume MSFT.Adjusted
## 2026-01-23    451.87    471.10   450.53     465.95    38000200      463.0126
## 2026-01-26    465.31    474.25   462.00     470.28    29291200      467.3153
## 2026-01-27    473.70    482.87   473.16     480.58    29213900      477.5503
## 2026-01-28    483.21    483.74   478.00     481.63    36875400      478.5937
## 2026-01-29    439.99    442.50   421.02     433.50   128855300      430.7671
## 2026-01-30    439.17    439.60   426.45     430.29    58566800      427.5774

Count the number of stock price observations

nrow(MSFT)

## [1] 84

Find the highest opening price

max(MSFT$MSFT.Open, na.rm = TRUE)

## [1] 550

Find the date when the highest opening price occurred

which.max(MSFT$MSFT.Open)

## [1] 20

Display the exact date and opening price

MSFT[which.max(MSFT$MSFT.Open), "MSFT.Open"]

##            MSFT.Open
## 2025-10-28       550

Download U.S. GDP data

library(wbstats)
gdp <- wb_data(
  indicator = "NY.GDP.MKTP.CD",
  country = "USA",
  start_date = 2015,
  end_date = 2025
)

Display the first rows

head(gdp)

## Key: <iso3c>
##     iso2c  iso3c       country  date NY.GDP.MKTP.CD   unit obs_status footnote
##    <char> <char>        <char> <num>          <num> <char>     <char>   <char>
## 1:     US    USA United States  2015   1.829502e+13   <NA>       <NA>     <NA>
## 2:     US    USA United States  2016   1.880491e+13   <NA>       <NA>     <NA>
## 3:     US    USA United States  2017   1.961210e+13   <NA>       <NA>     <NA>
## 4:     US    USA United States  2018   2.065652e+13   <NA>       <NA>     <NA>
## 5:     US    USA United States  2019   2.153998e+13   <NA>       <NA>     <NA>
## 6:     US    USA United States  2020   2.137528e+13   <NA>       <NA>     <NA>
##    last_updated
##          <char>
## 1:   2026-10-08
## 2:   2026-10-08
## 3:   2026-10-08
## 4:   2026-10-08
## 5:   2026-10-08
## 6:   2026-10-08

install.packages(“wbstats”)