unlink(“~/Library/Caches/R/sass”, recursive = TRUE) unlink(tempdir(), recursive = TRUE) install.packages(“sass”, type = “binary”) xcode-select –install library(tidyverse) library(dplyr) — title: “Stock Data Transformation with pivot_longer()” author: “Your Name” date: “2025-10-21” output: html_document —

Data Transformation Overview

This document demonstrates how to transform wide-format stock data into long format using pivot_longer() from the tidyverse package.

Load Required Libraries

library(tidyverse)

Sample Data Structure

Before transformation, the data looks like this (wide format):

## # A tibble: 3 × 5
##   company   `2020_week1` `2020_week2` `2021_week1` `2021_week2`
##   <chr>            <dbl>        <dbl>        <dbl>        <dbl>
## 1 Apple              120          125          130          135
## 2 Google            1500         1520         1550         1580
## 3 Microsoft          180          185          190          195

Transformation Code

stock_long <- stock_df %>% 
  pivot_longer(
    cols = !company,
    names_to = c("year", "week"),
    names_sep = "_week",
    names_transform = list(year = as.integer, week = as.integer),
    values_to = "price"
  )

stock_long

## # A tibble: 12 × 4
##    company    year  week price
##    <chr>     <int> <int> <dbl>
##  1 Apple      2020     1   120
##  2 Apple      2020     2   125
##  3 Apple      2021     1   130
##  4 Apple      2021     2   135
##  5 Google     2020     1  1500
##  6 Google     2020     2  1520
##  7 Google     2021     1  1550
##  8 Google     2021     2  1580
##  9 Microsoft  2020     1   180
## 10 Microsoft  2020     2   185
## 11 Microsoft  2021     1   190
## 12 Microsoft  2021     2   195

Parameter Breakdown

• cols = !company: Select all columns except ‘company’ to pivot
• names_to = c("year", "week"): Split column names into two new columns
• names_sep = "_week": Use “_week” as the delimiter to split names
• names_transform: Convert year and week to integers
• values_to = "price": Store the cell values in a column called ‘price’

Result Summary

# Check the structure
glimpse(stock_long)

## Rows: 12
## Columns: 4
## $ company <chr> "Apple", "Apple", "Apple", "Apple", "Google", "Google", "Googl…
## $ year    <int> 2020, 2020, 2021, 2021, 2020, 2020, 2021, 2021, 2020, 2020, 20…
## $ week    <int> 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2
## $ price   <dbl> 120, 125, 130, 135, 1500, 1520, 1550, 1580, 180, 185, 190, 195

# Summary statistics
stock_long %>% 
  group_by(company, year) %>% 
  summarise(
    avg_price = mean(price),
    min_price = min(price),
    max_price = max(price),
    .groups = "drop"
  )

## # A tibble: 6 × 5
##   company    year avg_price min_price max_price
##   <chr>     <int>     <dbl>     <dbl>     <dbl>
## 1 Apple      2020      122.       120       125
## 2 Apple      2021      132.       130       135
## 3 Google     2020     1510       1500      1520
## 4 Google     2021     1565       1550      1580
## 5 Microsoft  2020      182.       180       185
## 6 Microsoft  2021      192.       190       195