Use the stock_df.csv to answer the following questions:
- This data is the weekly closing prices for five US listed companies
in 2019.
- Import data stock_df.csv and reshape it from wide to long format
(stock_df_long)
stock_df <- read_csv("stock_df.csv")
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>, …
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 rows