This is where you can change the way that you are classifying your data. For example, you could change "linear" to "unclassed" if you are wanting to look at your data in an unclassed way

This function takes your data and orders it from low to high, or the full extent or domain of your data, and then takes your color scheme and creates a continuous color scheme for the values. In this example we can see that some of the values/squares are the same color, which means that there are squares that have the same or very similar values.

The range function symbolizes that the two colors are going to make up the full range of values and that the first color is the minimum of the range and that the second one is the high end of the range and all of the colors in between represent the values in the middle of the range.

"Chart" takes the data and the classification method to then create your visualization

manually putting in values for where you want the breaks to be

The number of breaks that you put in the domain line is how many classes we will have

Creates the breaks for you, in the drop-down from "array(3)" for where the breaks are

This is also where we are generating classes, different from our unclassed because we created classes now

These breaks are generated based on natural gaps, or breaks, in the data. Recognizes the little clumps of data and groups the data based on these.

The number of classes is determined by the number of colors you specify

This is a more compact/neat way to find the min/max of your dataset

You just enter how many visual variables you want under "range" instead of specifying how many intervals you want

This method rounds and groups the observations into classes based on the number of colors we provide. This method may lead to more observations in each class than others because it does involve using rounding.

Equal number of observations for each color

When using the domain function with scale quantile you can just add your full dataset.

The number of classes is determined by how many colors you specify

Percent population white in Washington Counties

I chose the breaks to be where they are because of the min and max of our dataset. Because the range is very small, the values/breaks had to be quite close to each other.

Within this histogram we can see overall that it is skewed to the left, with there being more counties that have a higher percentage of white population. This makes sense with the rest of the classifications that we made later in this exercise because some of them are mostly the last color/ the highest value in the classifications. It also helps me to better understand why even in our unclassed set there are still multiple squares with the same value, because a lot of these counties have similar values on the right side of the histogram. Even as you increase the number of bins there are still multiple counties that share the same or very similar values.