Week 2: Apply 1

Import stock prices

stocks <- tq_get(c("JNJ", "KSS", "TDS"),
                 get = "stock.prices",
                 from = "2017-01-01",
                 to = "2019-01-01")
stocks

## # A tibble: 1,506 × 8
##    symbol date        open  high   low close  volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>   <dbl>    <dbl>
##  1 JNJ    2017-01-03  116.  116   115.  116. 5953000     93.8
##  2 JNJ    2017-01-04  116.  116.  115.  116. 5828900     93.7
##  3 JNJ    2017-01-05  116   117.  115.  117. 6217200     94.6
##  4 JNJ    2017-01-06  117.  117   116.  116. 5221400     94.2
##  5 JNJ    2017-01-09  116.  117.  116.  116. 5457500     94.2
##  6 JNJ    2017-01-10  116.  117   116.  116. 5312800     94.1
##  7 JNJ    2017-01-11  116.  116.  114.  115. 8887200     92.9
##  8 JNJ    2017-01-12  114.  115.  113.  115. 6627300     92.8
##  9 JNJ    2017-01-13  115.  115.  114.  115. 4935200     92.8
## 10 JNJ    2017-01-17  114.  115.  114.  115. 6255400     93.0
## # ℹ 1,496 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: 624 × 8
##    symbol date        open  high   low close  volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>   <dbl>    <dbl>
##  1 JNJ    2017-01-03  116.  116   115.  116. 5953000     93.8
##  2 JNJ    2017-01-04  116.  116.  115.  116. 5828900     93.7
##  3 JNJ    2017-01-05  116   117.  115.  117. 6217200     94.6
##  4 JNJ    2017-01-06  117.  117   116.  116. 5221400     94.2
##  5 JNJ    2017-01-09  116.  117.  116.  116. 5457500     94.2
##  6 JNJ    2017-01-10  116.  117   116.  116. 5312800     94.1
##  7 JNJ    2017-01-11  116.  116.  114.  115. 8887200     92.9
##  8 JNJ    2017-01-12  114.  115.  113.  115. 6627300     92.8
##  9 JNJ    2017-01-13  115.  115.  114.  115. 4935200     92.8
## 10 JNJ    2017-01-17  114.  115.  114.  115. 6255400     93.0
## # ℹ 614 more rows

Arrange Rows

stocks %>% arrange (open)

## # A tibble: 1,506 × 8
##    symbol date        open  high   low close volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>  <dbl>    <dbl>
##  1 TDS    2018-07-26  24.1  25.2  24.0  24.9 693200     19.9
##  2 TDS    2018-02-14  24.2  24.5  24.0  24.4 505700     19.2
##  3 TDS    2018-02-12  24.3  24.7  24.2  24.5 452300     19.2
##  4 TDS    2018-07-25  24.3  24.3  24    24.1 715100     19.2
##  5 TDS    2018-02-13  24.3  24.4  23.9  24.3 449200     19.1
##  6 TDS    2018-07-24  24.4  24.4  24.0  24.2 481900     19.3
##  7 TDS    2018-07-23  24.4  24.4  24.1  24.3 383800     19.4
##  8 TDS    2018-07-20  24.5  24.5  24    24.4 708500     19.4
##  9 TDS    2018-02-09  24.6  24.7  23.5  24.3 750400     19.1
## 10 TDS    2018-02-15  24.6  25.5  24.3  25.5 524300     20.0
## # ℹ 1,496 more rows

Select Columns

stocks %>% select(date,high)

## # A tibble: 1,506 × 2
##    date        high
##    <date>     <dbl>
##  1 2017-01-03  116 
##  2 2017-01-04  116.
##  3 2017-01-05  117.
##  4 2017-01-06  117 
##  5 2017-01-09  117.
##  6 2017-01-10  117 
##  7 2017-01-11  116.
##  8 2017-01-12  115.
##  9 2017-01-13  115.
## 10 2017-01-17  115.
## # ℹ 1,496 more rows

Add Columns

stocks %>% select (date, high, open)

## # A tibble: 1,506 × 3
##    date        high  open
##    <date>     <dbl> <dbl>
##  1 2017-01-03  116   116.
##  2 2017-01-04  116.  116.
##  3 2017-01-05  117.  116 
##  4 2017-01-06  117   117.
##  5 2017-01-09  117.  116.
##  6 2017-01-10  117   116.
##  7 2017-01-11  116.  116.
##  8 2017-01-12  115.  114.
##  9 2017-01-13  115.  115.
## 10 2017-01-17  115.  114.
## # ℹ 1,496 more rows

    mutate(stocks, gain =low)

## # A tibble: 1,506 × 9
##    symbol date        open  high   low close  volume adjusted  gain
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>   <dbl>    <dbl> <dbl>
##  1 JNJ    2017-01-03  116.  116   115.  116. 5953000     93.8  115.
##  2 JNJ    2017-01-04  116.  116.  115.  116. 5828900     93.7  115.
##  3 JNJ    2017-01-05  116   117.  115.  117. 6217200     94.6  115.
##  4 JNJ    2017-01-06  117.  117   116.  116. 5221400     94.2  116.
##  5 JNJ    2017-01-09  116.  117.  116.  116. 5457500     94.2  116.
##  6 JNJ    2017-01-10  116.  117   116.  116. 5312800     94.1  116.
##  7 JNJ    2017-01-11  116.  116.  114.  115. 8887200     92.9  114.
##  8 JNJ    2017-01-12  114.  115.  113.  115. 6627300     92.8  113.
##  9 JNJ    2017-01-13  115.  115.  114.  115. 4935200     92.8  114.
## 10 JNJ    2017-01-17  114.  115.  114.  115. 6255400     93.0  114.
## # ℹ 1,496 more rows

Summarize with groups

stocks %>% summarise (volume)

## 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: 1,506 × 1
##     volume
##      <dbl>
##  1 5953000
##  2 5828900
##  3 6217200
##  4 5221400
##  5 5457500
##  6 5312800
##  7 8887200
##  8 6627300
##  9 4935200
## 10 6255400
## # ℹ 1,496 more rows