Give a bit more direction
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EDA.qmd
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EDA.qmd
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@ -490,8 +490,9 @@ ggplot(mpg, aes(x = hwy, y = fct_reorder(class, hwy, median))) +
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What do you learn?
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What do you learn?
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How do you interpret the plots?
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How do you interpret the plots?
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5. Compare and contrast `geom_violin()` with a faceted `geom_histogram()`, or a colored `geom_freqpoly()`.
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5. Create a visualization of diamond prices vs. a categorical variable from the `diamonds` dataset using `geom_violin()`, then a faceted `geom_histogram()`, and then a colored `geom_freqpoly()`.
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What are the pros and cons of each method?
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Compare and contrast the three plots.
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What are the pros and cons of each method of visualizing the distribution of a numerical variable based on the levels of a categorical variable?
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6. If you have a small dataset, it's sometimes useful to use `geom_jitter()` to see the relationship between a continuous and categorical variable.
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6. If you have a small dataset, it's sometimes useful to use `geom_jitter()` to see the relationship between a continuous and categorical variable.
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The ggbeeswarm package provides a number of methods similar to `geom_jitter()`.
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The ggbeeswarm package provides a number of methods similar to `geom_jitter()`.
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