Principles for Effective Visualizations

Angela Zoss
October 15, 2016

Principle 1: Order matters

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Order by semantics

data$answer <- 
    factor(data$answer,
           levels=c("None","A little", "Some", "A lot"),
           ordered = TRUE)

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Order by value

data$academic_field <- 
    factor(data$academic_field,
           levels=names(
               sort(
                   table(
                       data$academic_field),decreasing=TRUE)))

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Principle 2: Put long categories on y-axis

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coord_flip()

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Oops!

data$academic_field <- 
    factor(data$academic_field,
           levels=names(
               sort(
                   table(data$academic_field),
                   decreasing=TRUE)))

data$academic_field <- 
    factor(data$academic_field,
           levels=names(
               sort(
                   table(data$academic_field))))

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Principle 3: Pick a purpose

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Different placement helps with different comparisons.

fill=highest_degree

facet_grid(.~highest_degree)

Principle 4: Keep scales consistent

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Keep all categories, manually set axes

scale_x_discrete(drop=FALSE)

scale_y_continuous(limits=c(0,40),breaks=c(0,10,20,30,40),minor_breaks=NULL)

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Principle 5: Select meaningful colors

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Select colors manually, or use alternate palette

scale_fill_manual(
    values=c("snow4","snow3",
             "tan3","tan1",
             "turquoise2","turquoise4"))

scale_fill_manual(
    values=c("#fee391","#fe9929", "#cc4c02"))

# Also see package RColorBrewer
scale_fill_brewer(palette="BrBG")

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Questions?

angela.zoss@duke.edu

slides: URL