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 0.116
## 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 does because when it rises up high in the chart it means when it falls it could be a huge failure.
Hint: See the code in 4.3.3 Box plots.
Apple would be my choice because they fluncuate very often you can never know when they are going to skip up of die hard.
Hint: Use message, echo and results in the global chunk options. Refer to the RMarkdown Reference Guide.