Homework_4
Anungoo Erdenebat
2025-10-21
Introduction
This document demonstrates how to reshape stock price data from wide
format to long format using the tidyverse package in R. The
dataset contains weekly closing prices for five US-listed companies
throughout 2019.
Load Libraries
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 errorslibrary(dplyr)Import Data
stock_df <- read_csv("C:/Users/dell/Downloads/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## # A tibble: 5 × 106
## company `2019_week1` `2019_week2` `2019_week3` `2019_week4` `2019_week5`
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Amazon 1848. 1641. 1696. 1671. 1626.
## 2 Apple 73.4 38.1 39.2 39.4 41.6
## 3 Facebook 205. 144. 150. 149. 166.
## 4 Google 1337. 1057. 1098. 1091. 1111.
## 5 Microsoft 158. 103. 108. 107. 103.
## # ℹ 100 more variables: `2019_week6` <dbl>, `2019_week7` <dbl>,
## # `2019_week8` <dbl>, `2019_week9` <dbl>, `2019_week10` <dbl>,
## # `2019_week11` <dbl>, `2019_week12` <dbl>, `2019_week13` <dbl>,
## # `2019_week14` <dbl>, `2019_week15` <dbl>, `2019_week16` <dbl>,
## # `2019_week17` <dbl>, `2019_week18` <dbl>, `2019_week19` <dbl>,
## # `2019_week20` <dbl>, `2019_week21` <dbl>, `2019_week22` <dbl>,
## # `2019_week23` <dbl>, `2019_week24` <dbl>, `2019_week25` <dbl>, …Data Description
The dataset contains:
- 5 companies: Amazon, Apple, Facebook, Google, and Microsoft
- 105 weeks of data from 2019
- Wide format: Each week is a separate column named
as
2019_week1,2019_week2, etc.
Reshape Data: Wide to Long Format
stock_df_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_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 rowsResult Summary
The transformed dataset now has:
- 525 rows (5 companies × 105 weeks)
- 4 columns:
company,year,week, andprice
summary(stock_df_long)## company year week price
## Length:525 Min. :2019 Min. : 1.00 Min. : 38.07
## Class :character 1st Qu.:2019 1st Qu.:14.00 1st Qu.: 136.62
## Mode :character Median :2020 Median :27.00 Median : 212.48
## Mean :2020 Mean :26.75 Mean : 804.30
## 3rd Qu.:2020 3rd Qu.:40.00 3rd Qu.:1479.23
## Max. :2020 Max. :53.00 Max. :3401.80dim(stock_df_long)## [1] 525 4head(stock_df_long, 10)## # A tibble: 10 × 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.Visualization
ggplot(stock_df_long, aes(x = week, y = price, color = company)) +
geom_line(linewidth = 1) +
labs(
title = "Weekly Stock Prices for Five US Companies in 2019",
x = "Week",
y = "Price (USD)",
color = "Company"
) +
theme_minimal() +
theme(
plot.title = element_text(hjust = 0.5, face = "bold"),
legend.position = "bottom"
)
Explanation
The pivot_longer() function transforms the data by:
- cols = !company - Selects all columns except “company”
- **names_sep = “_week”** - Splits column names at “_week”
- names_to = c(“year”, “week”) - Creates two new columns from split names
- names_transform - Converts year and week to integers
- values_to = “price” - Stores stock prices in a new column
Conclusion
We successfully transformed the stock price data from wide format (105 columns) to long format (4 columns), making it easier to analyze and visualize.
sessionInfo()## R version 4.5.1 (2025-06-13 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows 11 x64 (build 22631)
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## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] lubridate_1.9.4 forcats_1.0.1 stringr_1.5.2 dplyr_1.1.4
## [5] purrr_1.1.0 readr_2.1.5 tidyr_1.3.1 tibble_3.3.0
## [9] ggplot2_4.0.0 tidyverse_2.0.0
##
## loaded via a namespace (and not attached):
## [1] bit_4.6.0 gtable_0.3.6 jsonlite_2.0.0 crayon_1.5.3
## [5] compiler_4.5.1 tidyselect_1.2.1 parallel_4.5.1 jquerylib_0.1.4
## [9] scales_1.4.0 yaml_2.3.10 fastmap_1.2.0 R6_2.6.1
## [13] labeling_0.4.3 generics_0.1.4 knitr_1.50 bslib_0.9.0
## [17] pillar_1.11.0 RColorBrewer_1.1-3 tzdb_0.5.0 rlang_1.1.6
## [21] utf8_1.2.6 stringi_1.8.7 cachem_1.1.0 xfun_0.53
## [25] sass_0.4.10 S7_0.2.0 bit64_4.6.0-1 timechange_0.3.0
## [29] cli_3.6.5 withr_3.0.2 magrittr_2.0.3 digest_0.6.37
## [33] grid_4.5.1 vroom_1.6.6 rstudioapi_0.17.1 hms_1.1.3
## [37] lifecycle_1.0.4 vctrs_0.6.5 evaluate_1.0.5 glue_1.8.0
## [41] farver_2.1.2 rmarkdown_2.29 tools_4.5.1 pkgconfig_2.0.3
## [45] htmltools_0.5.8.1