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
## # A tibble: 3 x 2
## symbol mean_returns
## <chr> <dbl>
## 1 AAPL 0.366
## 2 IBM 0.141
## 3 MSFT 0.283
IBM has the highest chance of losing big when things o wrong because they have the highest density on the yearly returns. Their stock has a intense increase over a short period of time. They also have a drastic decrease over a short period of time as well.
I would choose Apple or Microsoft based off their yearly returns. They have a chance to either ein big or lose big. They have much higher peaks than IBM, meaning they can have higher and lower returns based on their yearly performaces.
Hint: Use message
, echo
and results
in the global chunk options. Refer to the RMarkdown Reference Guide.