Import stock prices
stocks <- tq_get(c("WMT", "GM", "MSFT"),
get = "stock.prices",
from = "2016-01-01")
stocks
## # A tibble: 7,329 × 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WMT 2016-01-04 20.2 20.5 20.1 20.5 35967600 17.1
## 2 WMT 2016-01-05 20.7 21.0 20.6 21.0 39978000 17.5
## 3 WMT 2016-01-06 20.8 21.3 20.8 21.2 49693800 17.7
## 4 WMT 2016-01-07 21.0 21.7 21.0 21.7 79290000 18.1
## 5 WMT 2016-01-08 21.7 21.8 21.1 21.2 53303700 17.7
## 6 WMT 2016-01-11 21.3 21.5 21.2 21.4 37961400 17.9
## 7 WMT 2016-01-12 21.5 21.6 21.1 21.2 36587700 17.7
## 8 WMT 2016-01-13 21.2 21.2 20.6 20.6 41177100 17.2
## 9 WMT 2016-01-14 20.7 21.2 20.6 21.0 38804700 17.6
## 10 WMT 2016-01-15 20.5 20.8 20.4 20.6 45523200 17.2
## # ℹ 7,319 more rows
Plot stock prices
stocks %>%
ggplot(aes(x = date, y = adjusted, color = symbol)) +
geom_line()
Apply the following dplyr verbs to your stock data
Filter rows
stocks %>% filter(adjusted > 24)
## # A tibble: 6,788 × 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WMT 2017-10-10 27.6 28.3 27.5 28.0 75372000 24.6
## 2 WMT 2017-10-11 28.2 28.8 28.0 28.6 55683000 25.1
## 3 WMT 2017-10-12 28.6 28.8 28.4 28.7 38631000 25.2
## 4 WMT 2017-10-13 28.7 29.0 28.7 28.9 28167000 25.3
## 5 WMT 2017-10-16 28.9 28.9 28.3 28.6 27900900 25.1
## 6 WMT 2017-10-17 28.5 28.8 28.4 28.7 17554500 25.1
## 7 WMT 2017-10-18 28.7 28.8 28.7 28.7 16416300 25.2
## 8 WMT 2017-10-19 28.7 28.9 28.6 28.8 21087000 25.3
## 9 WMT 2017-10-20 28.9 29.1 28.8 29.1 22853400 25.6
## 10 WMT 2017-10-23 29.1 29.6 29.1 29.5 31758000 25.9
## # ℹ 6,778 more rows
Arrange rows
Select columns
Add columns
Summarize by groups