Use the given code below to answer the questions.
## Load package
library(tidyverse) # for cleaning, plotting, etc
library(tidyquant) # for financial analysis
## Import data
stocks <- tq_get("AAPL", get = "stock.prices", from = "2016-01-01")
stocks
## Visualize
stocks %>%
ggplot(aes(x = date, y = adjusted)) +
geom_line()
## Load package
library(tidyverse) # for cleaning, plotting, etc
library(tidyquant) # for financial analysis
## Import data
stocks <- tq_get("NFLX", get = "stock.prices", from = "2017-01-01")
stocks
Character data would be like “one”, “two”, “three”. Logical data would be “TRUE” , “FALSE”
Hint: Insert a new code chunk below and type in the code, using the ggplot() function above. Revise the code so that it maps close to the y-axis, instead of adjusted.
## Visualize
stocks %>%
ggplot(aes(x = date, y = close)) +
geom_line()
At the beginning of 2019, Netflix experienced rapid growth, followed by a plateau. It then took a dip before beginning an alsmost year long period of growth to present.
## Import data
stocks <- tq_get("NFLX", "AMZN", get = "stock.prices", from = "2016-01-01")
stocks
## # A tibble: 1,033 x 7
## date open high low close volume adjusted
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2016-01-04 109 110 105. 110. 20794800 110.
## 2 2016-01-05 110. 111. 106. 108. 17664600 108.
## 3 2016-01-06 105. 118. 105. 118. 33045700 118.
## 4 2016-01-07 116. 122. 112. 115. 33636700 115.
## 5 2016-01-08 116. 118. 111. 111. 18067100 111.
## 6 2016-01-11 112. 117. 111. 115. 21920400 115.
## 7 2016-01-12 116. 118. 115. 117. 15133500 117.
## 8 2016-01-13 114. 114. 105. 107. 24921600 107.
## 9 2016-01-14 106. 109. 101. 107. 23664800 107.
## 10 2016-01-15 102. 106. 102. 104. 19775100 104.
## # … with 1,023 more rows
Hint: Use message, echo and results in the chunk options. Refer to the RMarkdown Reference Guide.
Hint: Use echo and results in the chunk option. Note that this question only applies to the individual code chunk of Q6.