Import stock prices
stocks <- tq_get(c("CAT", "NVDA", "TSM"),
get = "stock.prices",
from = "2016-01-01")
stocks
## # A tibble: 8,070 × 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 CAT 2016-01-04 66.9 68.1 65.7 68.0 8586900 52.9
## 2 CAT 2016-01-05 68.4 68.4 66.4 67.3 6139100 52.3
## 3 CAT 2016-01-06 66.0 66.8 65.6 66.2 6639300 51.5
## 4 CAT 2016-01-07 65.1 65.5 63.7 63.9 8601500 49.7
## 5 CAT 2016-01-08 64.3 64.5 62.9 63.3 8278500 49.2
## 6 CAT 2016-01-11 63.7 63.7 60.8 61.5 9669400 47.8
## 7 CAT 2016-01-12 62.4 62.5 60.4 61.6 8593100 47.9
## 8 CAT 2016-01-13 62.1 62.8 60.4 60.9 7237300 47.4
## 9 CAT 2016-01-14 61.2 63.0 60.4 62.3 9241800 48.4
## 10 CAT 2016-01-15 59.5 60.1 58.8 59.9 12674500 47.2
## # ℹ 8,060 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(open > 85, close > 90 )
## # A tibble: 4,318 × 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 CAT 2016-11-09 89.8 93.2 89.4 91.2 19281900 73.9
## 2 CAT 2016-11-10 92.0 94.9 91.8 93.4 14174400 75.7
## 3 CAT 2016-11-11 93.4 94.3 91.8 93.0 7369900 75.3
## 4 CAT 2016-11-14 93.2 95.5 93.0 94.2 7552800 76.3
## 5 CAT 2016-11-15 93.5 94.5 92.6 94.4 4645400 76.5
## 6 CAT 2016-11-16 93.8 94.4 92.8 93.3 3297200 75.6
## 7 CAT 2016-11-17 93.5 93.5 92.4 92.8 3942200 75.2
## 8 CAT 2016-11-18 93.0 93.4 91.9 92.3 4715900 74.8
## 9 CAT 2016-11-21 93.1 93.8 92.8 92.9 3783200 75.3
## 10 CAT 2016-11-22 93.4 93.7 92.8 93.6 3172800 75.8
## # ℹ 4,308 more rows
Arrange Rows
stocks %>% arrange(desc(high), desc(low))
## # A tibble: 8,070 × 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 CAT 2026-06-30 1045 1073. 1037. 1065. 3287500 1063.
## 2 CAT 2026-06-25 1025. 1057. 1015 1057. 4642100 1055.
## 3 CAT 2026-07-01 1034. 1041. 985. 991. 3914100 990.
## 4 CAT 2026-06-29 998. 1039. 992. 1033. 3146900 1031.
## 5 CAT 2026-06-26 1032. 1032. 990. 997. 13948500 996.
## 6 CAT 2026-06-22 999. 1023. 999 1022. 4033500 1020.
## 7 CAT 2026-06-24 982. 1005. 970. 994. 3526000 993.
## 8 CAT 2026-07-02 1001. 1001. 949. 964. 3568600 962.
## 9 CAT 2026-06-23 985. 995. 973. 984. 4286900 982.
## 10 CAT 2026-06-18 979 994. 976. 986. 6175000 984.
## # ℹ 8,060 more rows
Select Columns
select(stocks, symbol, open:close)
## # A tibble: 8,070 × 5
## symbol open high low close
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 CAT 66.9 68.1 65.7 68.0
## 2 CAT 68.4 68.4 66.4 67.3
## 3 CAT 66.0 66.8 65.6 66.2
## 4 CAT 65.1 65.5 63.7 63.9
## 5 CAT 64.3 64.5 62.9 63.3
## 6 CAT 63.7 63.7 60.8 61.5
## 7 CAT 62.4 62.5 60.4 61.6
## 8 CAT 62.1 62.8 60.4 60.9
## 9 CAT 61.2 63.0 60.4 62.3
## 10 CAT 59.5 60.1 58.8 59.9
## # ℹ 8,060 more rows
Add Columns
stocks %>% mutate(gain = close - open) %>% select(symbol, open:close, gain)
## # A tibble: 8,070 × 6
## symbol open high low close gain
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 CAT 66.9 68.1 65.7 68.0 1.11
## 2 CAT 68.4 68.4 66.4 67.3 -1.10
## 3 CAT 66.0 66.8 65.6 66.2 0.240
## 4 CAT 65.1 65.5 63.7 63.9 -1.14
## 5 CAT 64.3 64.5 62.9 63.3 -1.04
## 6 CAT 63.7 63.7 60.8 61.5 -2.20
## 7 CAT 62.4 62.5 60.4 61.6 -0.840
## 8 CAT 62.1 62.8 60.4 60.9 -1.18
## 9 CAT 61.2 63.0 60.4 62.3 1.03
## 10 CAT 59.5 60.1 58.8 59.9 0.350
## # ℹ 8,060 more rows
transmute(stocks, gain = close - open)
## # A tibble: 8,070 × 1
## gain
## <dbl>
## 1 1.11
## 2 -1.10
## 3 0.240
## 4 -1.14
## 5 -1.04
## 6 -2.20
## 7 -0.840
## 8 -1.18
## 9 1.03
## 10 0.350
## # ℹ 8,060 more rows
Summarise with groups
stocks %>% summarise(avg_open = mean(open))
## # A tibble: 1 × 1
## avg_open
## <dbl>
## 1 134.
stocks %>%
#Grouo by ticker symbol
group_by(symbol) %>%
# Calculate average open price
summarise(avg_open = mean(open)) %>%
# Sort it
arrange(avg_open)
## # A tibble: 3 × 2
## symbol avg_open
## <chr> <dbl>
## 1 NVDA 46.8
## 2 TSM 110.
## 3 CAT 244.