In this exercise you will learn to plot data using the ggplot2 package. To answer the questions below, use Chapter 4.3 Categorical vs. Quantitative Data Visualization with R.
## # A tibble: 80 x 9
## # Groups: symbol [2]
## symbol date open high low close volume adjusted daily.returns
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AAPL 2020-01-02 296. 301. 295. 300. 33870100 300. 0
## 2 AAPL 2020-01-03 297. 301. 296. 297. 36580700 297. -0.00972
## 3 AAPL 2020-01-06 294. 300. 293. 300. 29596800 299. 0.00797
## 4 AAPL 2020-01-07 300. 301. 297. 298. 27218000 298. -0.00470
## 5 AAPL 2020-01-08 297. 304. 297. 303. 33019800 302. 0.0161
## 6 AAPL 2020-01-09 307. 310. 306. 310. 42527100 309. 0.0212
## 7 AAPL 2020-01-10 311. 313. 308. 310. 35161200 310. 0.00226
## 8 AAPL 2020-01-13 312. 317. 311. 317. 30383000 316. 0.0214
## 9 AAPL 2020-01-14 317. 318. 312. 313. 40488600 312. -0.0135
## 10 AAPL 2020-01-15 312. 316. 310. 311. 30480900 311. -0.00429
## # … with 70 more rows
Hint: See the code in 4.3.2 Grouped kernel density plots.
Hint: See the code in 4.3.3 Box plots. Use the same title as in the density plot.
Hint: Discuss your answer based on median, the middle 50%, roughly 99% of the data, and outliers.
I would invest in microsoft because it has the bettern median and outliers of the two.
Hint: See the code in 4.3.1 Bar chart (on summary statistics).
Hint: See the code in 4.3.1 Bar chart (on summary statistics).
Hint: See the code in 4.3.1 Bar chart (on summary statistics).
Hint: See the code in 4.3.1 Bar chart (on summary statistics).
Hint: Use message, echo and results in the chunk options. Refer to the RMarkdown Reference Guide.