HW5

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.1     ✔ stringr   1.5.2
## ✔ ggplot2   4.0.0     ✔ tibble    3.3.0
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.1.0     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(dplyr)
stock_df <- read_csv("stock_df.csv")

## Rows: 5 Columns: 106
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr   (1): company
## dbl (105): 2019_week1, 2019_week2, 2019_week3, 2019_week4, 2019_week5, 2019_...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

stock_df_long <- stock_df %>%
  pivot_longer(
    cols = starts_with("2019_week"),     # select all columns starting with "2019_week"
    names_to = c("year", "week"),        # create two new columns
    names_pattern = "(\\d{4})_week(\\d+)", # extract year and week number using regex
    values_to = "price"                  # name for the numeric values
  ) %>%
  mutate(
    year = as.integer(year),
    week = as.integer(week)
  )

stock_df_long <- stock_df %>%
  pivot_longer(
    cols = matches("\\d{4}_week\\d+"),   # match all columns like "2019_week1", "2020_week3", etc.
    names_to = c("year", "week"),
    names_pattern = "(\\d{4})_week(\\d+)",
    values_to = "price"
  ) %>%
  mutate(
    year = as.integer(year),
    week = as.integer(week)
  )
stock_df_long

## # A tibble: 525 × 4
##    company  year  week price
##    <chr>   <int> <int> <dbl>
##  1 Amazon   2019     1 1848.
##  2 Amazon   2019     2 1641.
##  3 Amazon   2019     3 1696.
##  4 Amazon   2019     4 1671.
##  5 Amazon   2019     5 1626.
##  6 Amazon   2019     6 1588.
##  7 Amazon   2019     7 1608.
##  8 Amazon   2019     8 1632.
##  9 Amazon   2019     9 1672.
## 10 Amazon   2019    10 1621.
## # ℹ 515 more rows