hw04

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

library(readr)
library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2

## ── 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

# Load the data into stock_df_1_
stock_df_1_ <- read_csv("/cloud/project/stock_df (1).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.

# Perform pivot_longer on stock_df_1_
stock_df_long <- stock_df_1_ %>%
  pivot_longer(
    cols = !company, # Select all columns except 'company'
    names_to = c("year", "week"),
    names_sep = "_week",
    names_transform = list(year = as.integer, week = as.integer),
    values_to = "price"
  )

# Print the result
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