Week 2: Apply 1

Import stock prices

stocks <- tq_get(c("NKE", "ADDYY"),
                 get = "stock.prices",
                 from = "2016-01-01",
                 to = "2017-01-01")
stocks

## # A tibble: 504 × 8
##    symbol date        open  high   low close   volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
##  1 NKE    2016-01-04  61.1  61.9  60.9  61.5 11626800     54.8
##  2 NKE    2016-01-05  61.7  62.6  61.7  62.4  9220600     55.5
##  3 NKE    2016-01-06  61.3  62.0  61.2  61.5  6551600     54.8
##  4 NKE    2016-01-07  60.4  61.3  59.8  59.8 10881300     53.3
##  5 NKE    2016-01-08  60.1  60.8  58.7  58.9 11191300     52.4
##  6 NKE    2016-01-11  59.0  60.0  58.5  59.5 12825000     53.0
##  7 NKE    2016-01-12  60.3  60.8  59.6  59.9  8292200     53.4
##  8 NKE    2016-01-13  60.5  60.5  58.7  58.8  9944300     52.3
##  9 NKE    2016-01-14  59    59.3  57.3  58.5  9989000     52.1
## 10 NKE    2016-01-15  56.9  58.1  56.6  57.6 12208300     51.3
## # ℹ 494 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 > 50)

## # A tibble: 341 × 8
##    symbol date        open  high   low close   volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
##  1 NKE    2016-01-04  61.1  61.9  60.9  61.5 11626800     54.8
##  2 NKE    2016-01-05  61.7  62.6  61.7  62.4  9220600     55.5
##  3 NKE    2016-01-06  61.3  62.0  61.2  61.5  6551600     54.8
##  4 NKE    2016-01-07  60.4  61.3  59.8  59.8 10881300     53.3
##  5 NKE    2016-01-08  60.1  60.8  58.7  58.9 11191300     52.4
##  6 NKE    2016-01-11  59.0  60.0  58.5  59.5 12825000     53.0
##  7 NKE    2016-01-12  60.3  60.8  59.6  59.9  8292200     53.4
##  8 NKE    2016-01-13  60.5  60.5  58.7  58.8  9944300     52.3
##  9 NKE    2016-01-14  59    59.3  57.3  58.5  9989000     52.1
## 10 NKE    2016-01-15  56.9  58.1  56.6  57.6 12208300     51.3
## # ℹ 331 more rows

Arrange rows

stocks %>% arrange(low, high)

## # A tibble: 504 × 8
##    symbol date        open  high   low close volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>  <dbl>    <dbl>
##  1 ADDYY  2016-01-08  45.9  45.9  45.1  45.1  32600     41.2
##  2 ADDYY  2016-01-15  45.5  46.1  45.4  45.5  77100     41.6
##  3 ADDYY  2016-01-07  45.8  46.1  45.7  45.8  42400     41.9
##  4 ADDYY  2016-01-13  46.2  46.5  45.8  45.9  45000     41.9
##  5 ADDYY  2016-01-14  46.0  46.6  45.8  46.3  41800     42.3
##  6 ADDYY  2016-01-06  46.0  46.4  46.0  46.3  61300     42.3
##  7 ADDYY  2016-01-11  46.7  46.7  46.2  46.7  52400     42.6
##  8 ADDYY  2016-01-12  47.0  47.1  46.6  47.1  74000     43.0
##  9 ADDYY  2016-01-05  46.9  47.1  46.7  47.1  41400     43.0
## 10 ADDYY  2016-01-04  47.6  47.8  47    47.8  76000     43.6
## # ℹ 494 more rows

Select columns

stocks %>% select(high)

## # A tibble: 504 × 1
##     high
##    <dbl>
##  1  61.9
##  2  62.6
##  3  62.0
##  4  61.3
##  5  60.8
##  6  60.0
##  7  60.8
##  8  60.5
##  9  59.3
## 10  58.1
## # ℹ 494 more rows

Add columns

mutate(stocks,
       gain = high) %>%
    select(gain)

## # A tibble: 504 × 1
##     gain
##    <dbl>
##  1  61.9
##  2  62.6
##  3  62.0
##  4  61.3
##  5  60.8
##  6  60.0
##  7  60.8
##  8  60.5
##  9  59.3
## 10  58.1
## # ℹ 494 more rows

Summarise with groups

stocks %>%
    summarise(high)

## Warning: Returning more (or less) than 1 row per `summarise()` group was deprecated in
## dplyr 1.1.0.
## ℹ Please use `reframe()` instead.
## ℹ When switching from `summarise()` to `reframe()`, remember that `reframe()`
##   always returns an ungrouped data frame and adjust accordingly.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## # A tibble: 504 × 1
##     high
##    <dbl>
##  1  61.9
##  2  62.6
##  3  62.0
##  4  61.3
##  5  60.8
##  6  60.0
##  7  60.8
##  8  60.5
##  9  59.3
## 10  58.1
## # ℹ 494 more rows