In this exercise you will learn to plot data using the ggplot2
package. To answer the questions below, use 4.1 Categorical vs. Categorical from Data Visualization with R.
## # A tibble: 22,230 x 8
## # Groups: symbol [3]
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AAPL 1990-01-02 1.26 1.34 1.25 1.33 45799600 1.08
## 2 AAPL 1990-01-03 1.36 1.36 1.34 1.34 51998800 1.09
## 3 AAPL 1990-01-04 1.37 1.38 1.33 1.34 55378400 1.10
## 4 AAPL 1990-01-05 1.35 1.37 1.32 1.35 30828000 1.10
## 5 AAPL 1990-01-08 1.34 1.36 1.32 1.36 25393200 1.11
## 6 AAPL 1990-01-09 1.36 1.36 1.32 1.34 21534800 1.10
## 7 AAPL 1990-01-10 1.34 1.34 1.28 1.29 49929600 1.05
## 8 AAPL 1990-01-11 1.29 1.29 1.23 1.23 52763200 1.00
## 9 AAPL 1990-01-12 1.22 1.24 1.21 1.23 42974400 1.00
## 10 AAPL 1990-01-15 1.23 1.28 1.22 1.22 40434800 0.997
## # … with 22,220 more rows
## # A tibble: 90 x 3
## # Groups: symbol [3]
## symbol yearly.returns year
## <chr> <dbl> <dbl>
## 1 AAPL 0.169 1990
## 2 AAPL 0.323 1991
## 3 AAPL 0.0691 1992
## 4 AAPL -0.504 1993
## 5 AAPL 0.352 1994
## 6 AAPL -0.173 1995
## 7 AAPL -0.345 1996
## 8 AAPL -0.371 1997
## 9 AAPL 2.12 1998
## 10 AAPL 1.51 1999
## # … with 80 more rows
Hint: See the code in 4.3.1 Bar chart (on summary statistics).
## # A tibble: 3 x 2
## symbol mean_returns
## <chr> <dbl>
## 1 AAPL 0.366
## 2 IBM 0.141
## 3 MSFT 0.283
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 4.3.2 Grouped kernel density plots.
Hint: Google how to interpret density plots.
IBM has the biggest chance of loosing big becasue they have the most to loose with their density of yearly return being passed 1.2.
Hint: See the code in 4.3.3 Box plots.
I would invest in IBM and Microsoft because they have the highest density for yearly returns so they would have the most risk when investing because then you’d have the most to loose.
Hint: Use message
, echo
and results
in the global chunk options. Refer to the RMarkdown Reference Guide.