Import stock prices
stocks <- tq_get(c("LULU", "MSFT", "AAPL"),
get = "stock.prices",
from = "2021-01-01",
to = "2022-01-01")
stocks
## # A tibble: 756 × 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 LULU 2021-01-04 352. 357. 346. 356. 2015300 356.
## 2 LULU 2021-01-05 358. 369. 357. 366. 2122900 366.
## 3 LULU 2021-01-06 362. 368. 357. 363. 1170800 363.
## 4 LULU 2021-01-07 367. 374. 365. 367. 1103000 367.
## 5 LULU 2021-01-08 367. 371. 363. 365. 994900 365.
## 6 LULU 2021-01-11 354. 373. 352. 362. 1754000 362.
## 7 LULU 2021-01-12 362. 362. 351. 357. 1628000 357.
## 8 LULU 2021-01-13 358. 359. 350. 352. 1108700 352.
## 9 LULU 2021-01-14 352. 358. 348. 348. 1047700 348.
## 10 LULU 2021-01-15 347 347. 337. 344. 1401200 344.
## # … with 746 more rows
Plot stock prices
stocks %>%
ggplot(aes(x = date, y = adjusted, color = symbol)) +
geom_line()
Apply the dplyr verbs you learned in chapter 5
Filter rows
stocks %>% filter(adjusted > 24)
## # A tibble: 756 × 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 LULU 2021-01-04 352. 357. 346. 356. 2015300 356.
## 2 LULU 2021-01-05 358. 369. 357. 366. 2122900 366.
## 3 LULU 2021-01-06 362. 368. 357. 363. 1170800 363.
## 4 LULU 2021-01-07 367. 374. 365. 367. 1103000 367.
## 5 LULU 2021-01-08 367. 371. 363. 365. 994900 365.
## 6 LULU 2021-01-11 354. 373. 352. 362. 1754000 362.
## 7 LULU 2021-01-12 362. 362. 351. 357. 1628000 357.
## 8 LULU 2021-01-13 358. 359. 350. 352. 1108700 352.
## 9 LULU 2021-01-14 352. 358. 348. 348. 1047700 348.
## 10 LULU 2021-01-15 347 347. 337. 344. 1401200 344.
## # … with 746 more rows
Arrange rows
Select columns
Add columns
Summarise with groups