Midterm exam

data <- read.table("MidtermDataTEJ.txt", header=TRUE, sep="\t", stringsAsFactors=FALSE)

data$Date <- as.Date(as.character(data$Date), format="%Y%m%d")

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

highest_prices <- data %>%
  group_by(CO_ID, CoName) %>%
  summarize(
    Highest_Price = max(Close),
    Date_of_Highest_Price = Date[which.max(Close)]
  ) %>%
  arrange(desc(Highest_Price))

## `summarise()` has grouped output by 'CO_ID'. You can override using the
## `.groups` argument.

print(highest_prices)

## # A tibble: 3 × 4
## # Groups:   CO_ID [3]
##   CO_ID CoName                Highest_Price Date_of_Highest_Price
##   <dbl> <chr>                         <dbl> <date>               
## 1    50 "Yuanta Taiwan Top50"         137.  2022-01-17           
## 2    52 "FB Technology"               124.  2023-12-28           
## 3    56 "PTD          "                34.1 2023-12-27

overall_highest <- highest_prices %>%
  slice(which.max(Highest_Price))

print("Overall highest price:")

## [1] "Overall highest price:"

print(overall_highest)

## # A tibble: 3 × 4
## # Groups:   CO_ID [3]
##   CO_ID CoName                Highest_Price Date_of_Highest_Price
##   <dbl> <chr>                         <dbl> <date>               
## 1    50 "Yuanta Taiwan Top50"         137.  2022-01-17           
## 2    52 "FB Technology"               124.  2023-12-28           
## 3    56 "PTD          "                34.1 2023-12-27