14 Vis: Data Visualization Basics

Purpose: The most powerful way for us to learn about a dataset is to visualize the data. Throughout this class we will make extensive use of the grammar of graphics, a powerful graphical programming grammar that will allow us to create just about any graph you can imagine!

Reading: Data Visualization Basics. Note: In RStudio use Ctrl + Click (Mac Command + Click) to follow the link. Topics: Welcome, A code template, Aesthetic mappings. Reading Time: ~ 30 minutes

14.0.1 q1 Inspect the `diamonds` dataset. What do the `cut`, `color`, and `clarity` variables mean?

Hint: We learned how to inspect a dataset in e-data-00-basics!

?diamonds

14.0.2 q2 Use your “standard checks” to determine what variables the dataset has.

glimpse(diamonds)

## Rows: 53,940
## Columns: 10
## $ carat   <dbl> 0.23, 0.21, 0.23, 0.29, 0.31, 0.24, 0.24, 0.26, 0.22, 0.23, 0.…
## $ cut     <ord> Ideal, Premium, Good, Premium, Good, Very Good, Very Good, Ver…
## $ color   <ord> E, E, E, I, J, J, I, H, E, H, J, J, F, J, E, E, I, J, J, J, I,…
## $ clarity <ord> SI2, SI1, VS1, VS2, SI2, VVS2, VVS1, SI1, VS2, VS1, SI1, VS1, …
## $ depth   <dbl> 61.5, 59.8, 56.9, 62.4, 63.3, 62.8, 62.3, 61.9, 65.1, 59.4, 64…
## $ table   <dbl> 55, 61, 65, 58, 58, 57, 57, 55, 61, 61, 55, 56, 61, 54, 62, 58…
## $ price   <int> 326, 326, 327, 334, 335, 336, 336, 337, 337, 338, 339, 340, 34…
## $ x       <dbl> 3.95, 3.89, 4.05, 4.20, 4.34, 3.94, 3.95, 4.07, 3.87, 4.00, 4.…
## $ y       <dbl> 3.98, 3.84, 4.07, 4.23, 4.35, 3.96, 3.98, 4.11, 3.78, 4.05, 4.…
## $ z       <dbl> 2.43, 2.31, 2.31, 2.63, 2.75, 2.48, 2.47, 2.53, 2.49, 2.39, 2.…

The diamonds dataset has variables carat, cut, color, clarity, depth, table, price, x, y, z.

Now that we have the list of variables in the dataset, we know what we can visualize!

14.0.3 q3 Using `ggplot`, visualize `price` vs `carat` with points. What trend do

you observe?

Hint: Usually the language y vs x refers to the vertical axis vs horizontal axis. This is the opposite order from the way we often specify x, y pairs. Language is hard!

## TODO: Complete this code
ggplot(diamonds) +
  geom_point(aes(x = carat, y = price))

Observations:

price generally increases with carat
The trend is not ‘clean’; there is no single curve in the relationship

14.1 A note on aesthetics

The function aes() is short for aesthetics. Aesthetics in ggplot are the mapping of variables in a dataframe to visual elements in the graph. For instance, in the plot above you assigned carat to the x aesthetic, and price to the y aesthetic. But there are many more aesthetics you can set, some of which vary based on the geom_ you are using to visualize. The next question will explore this idea more.

14.1.1 q4 Create a new graph to visualize `price`, `carat`, and `cut`

simultaneously.

Hint: Remember that you can add additional aesthetic mappings in aes(). Some options include size, color, and shape.

## TODO: Complete this code
ggplot(diamonds) +
  geom_point(aes(x = carat, y = price, color = cut))