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()
Hint: Insert a new code chunk below and type in the code, using the tq_get() function above. Replace the ticker symbol. Find ticker symbols from Yahoo Finance.
## Import data
stocks <- tq_get("NFLX", get = "stock.prices", from = "2017-01-01")
stocks
An example of Character data would be “TRUE!” or “char” or just any text. An example of logical data could be echo= “FALSE”
## Visualize
stocks %>%
ggplot(aes(x = date, y = close)) +
geom_line()
``` For more information on the ggplot() function, refer to Ch2 Introduction to ggplot2 in one of our e-textbooks, Data Visualization with R.
Since the beginning of 2019, Netflix steadily rose until about June of 2019.From about June 2019 to December 2019, the closing price dropped heavily and fast.
Hint: Insert a new code chunk below and type in the code, using the tq_get() function above. You may refer to the manual of the tidyquant r package. Or, simply Google the tq_get function and see examples of the function’s usage.
## Import data
stocks <- tq_get("NFLX", get = "stock.prices", from = "2017-01-01")
stocks
## # A tibble: 781 x 7
## date open high low close volume adjusted
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2017-01-03 125. 128. 124. 127. 9437900 127.
## 2 2017-01-04 127. 130. 127. 129. 7843600 129.
## 3 2017-01-05 129. 133. 129. 132. 10185500 132.
## 4 2017-01-06 132. 134. 130. 131. 10657900 131.
## 5 2017-01-09 131. 132. 130. 131. 5771800 131.
## 6 2017-01-10 131. 132. 129. 130. 5985800 130.
## 7 2017-01-11 131. 132. 129. 130. 5615100 130.
## 8 2017-01-12 131. 131. 128. 129. 5388900 129.
## 9 2017-01-13 131. 134. 131. 134. 10515000 134.
## 10 2017-01-17 135. 135. 132. 133. 12220200 133.
## # … with 771 more rows
## Import data
stocks <- tq_get("AMZN", get = "stock.prices", from = "2017-01-01")
stocks
## # A tibble: 781 x 7
## date open high low close volume adjusted
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2017-01-03 758. 759. 748. 754. 3521100 754.
## 2 2017-01-04 758. 760. 754. 757. 2510500 757.
## 3 2017-01-05 762. 782. 760. 780. 5830100 780.
## 4 2017-01-06 782. 799. 778. 796. 5986200 796.
## 5 2017-01-09 798 802. 792. 797. 3446100 797.
## 6 2017-01-10 797. 798 790. 796. 2558400 796.
## 7 2017-01-11 794. 800. 790. 799. 2992800 799.
## 8 2017-01-12 800. 814. 800. 814. 4873900 814.
## 9 2017-01-13 814. 822. 811. 817. 3791900 817.
## 10 2017-01-17 816. 816 803. 810. 3670500 810.
## # … with 771 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.