Tips for effective data visualization

Data Science in a Box

datasciencebox.org

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Designing effective visualizations

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Keep it simple

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Use color to draw attention

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Tell a story

Credit: Angela Zoss and Eric Monson, Duke DVS

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Principles for effective visualizations

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Principles for effective visualizations

Order matters
Put long categories on the y-axis
Keep scales consistent
Select meaningful colors
Use meaningful and nonredundant labels

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Data

In September 2019, YouGov survey asked 1,639 GB adults the following question:

In hindsight, do you think Britain was right/wrong to vote to leave EU?

Right to leave

Wrong to leave

Don't know

Source: YouGov Survey Results, retrieved Oct 7, 2019

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Order matters

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Alphabetical order is rarely ideal

ggplot(brexit, aes(x = opinion)) +
  geom_bar()

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Order by frequency

fct_infreq: Reorder factors' levels by frequency

ggplot(brexit, aes(x = fct_infreq(opinion))) +
  geom_bar()

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Clean up labels

ggplot(brexit, aes(x = opinion)) +
  geom_bar() +
  labs(
    x = "Opinion",
    y = "Count"
  )

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Alphabetical order is rarely ideal

ggplot(brexit, aes(x = region)) +
  geom_bar()

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Use inherent level order

fct_relevel: Reorder factor levels using a custom order

brexit <- brexit %>%
  mutate(
    region = fct_relevel(
      region,
      "london", "rest_of_south", "midlands_wales", "north", "scot"
    )
  )

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Clean up labels

fct_recode: Change factor levels by hand

brexit <- brexit %>%
  mutate(
    region = fct_recode(
      region,
      London = "london",
      `Rest of South` = "rest_of_south",
      `Midlands / Wales` = "midlands_wales",
      North = "north",
      Scotland = "scot"
    )
  )

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Put long categories on the y-axis

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Long categories can be hard to read

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Move them to the y-axis

ggplot(brexit, aes(y = region)) +
  geom_bar()

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And reverse the order of levels

fct_rev: Reverse order of factor levels

ggplot(brexit, aes(y = fct_rev(region))) +
  geom_bar()

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Clean up labels

ggplot(brexit, aes(y = fct_rev(region))) +
  geom_bar() +
  labs(
    x = "Count",
    y = "Region"
  )

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Pick a purpose

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Segmented bar plots can be hard to read

ggplot(brexit, aes(y = region, fill = opinion)) +
  geom_bar()

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ggplot(brexit, aes(y = opinion, fill = region)) +
  geom_bar() +
  facet_wrap(~region, nrow = 1)

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Avoid redundancy?

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Redundancy can help tell a story

ggplot(brexit, aes(y = opinion, fill = opinion)) +
  geom_bar() +
  facet_wrap(~region, nrow = 1)

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Be selective with redundancy

ggplot(brexit, aes(y = opinion, fill = opinion)) +
  geom_bar() +
  facet_wrap(~region, nrow = 1) +
  guides(fill = "none")

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Use informative labels

ggplot(brexit, aes(y = opinion, fill = opinion)) +
  geom_bar() +
  facet_wrap(~region, nrow = 1) +
  guides(fill = "none") +
  labs(
    title = "Was Britain right/wrong to vote to leave EU?",
    x = NULL, y = NULL
  )

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A bit more info

ggplot(brexit, aes(y = opinion, fill = opinion)) +
  geom_bar() +
  facet_wrap(~region, nrow = 1) +
  guides(fill = "none") +
  labs(
    title = "Was Britain right/wrong to vote to leave EU?",
    subtitle = "YouGov Survey Results, 2-3 September 2019",
    caption = "Source: https://d25d2506sfb94s.cloudfront.net/cumulus_uploads/document/x0msmggx08/YouGov%20-%20Brexit%20and%202019%20election.pdf",
    x = NULL, y = NULL
  )

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Let's do better

ggplot(brexit, aes(y = opinion, fill = opinion)) +
  geom_bar() +
  facet_wrap(~region, nrow = 1) +
  guides(fill = "none") +
  labs(
    title = "Was Britain right/wrong to vote to leave EU?",
    subtitle = "YouGov Survey Results, 2-3 September 2019",
    caption = "Source: bit.ly/2lCJZVg",
    x = NULL, y = NULL
  )

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ggplot(brexit, aes(y = opinion, fill = opinion)) +
  geom_bar() +
  facet_wrap(~region,
    nrow = 1,
    labeller = label_wrap_gen(width = 12)
  ) + 
  guides(fill = "none") +
  labs(
    title = "Was Britain right/wrong to vote to leave EU?",
    subtitle = "YouGov Survey Results, 2-3 September 2019",
    caption = "Source: bit.ly/2lCJZVg",
    x = NULL, y = NULL
  )

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Select meaningful colors

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Rainbow colors not always the right choice

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Manually choose colors when needed

ggplot(brexit, aes(y = opinion, fill = opinion)) +
  geom_bar() +
  facet_wrap(~region, nrow = 1, labeller = label_wrap_gen(width = 12)) +
  guides(fill = "none") +
  labs(title = "Was Britain right/wrong to vote to leave EU?",
       subtitle = "YouGov Survey Results, 2-3 September 2019",
       caption = "Source: bit.ly/2lCJZVg",
       x = NULL, y = NULL) +
  scale_fill_manual(values = c(
    "Wrong" = "red",
    "Right" = "green",
    "Don't know" = "gray"
  ))

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Choosing better colors

colorbrewer2.org

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Use better colors

ggplot(brexit, aes(y = opinion, fill = opinion)) +
  geom_bar() +
  facet_wrap(~region, nrow = 1, labeller = label_wrap_gen(width = 12)) +
  guides(fill = "none") +
  labs(title = "Was Britain right/wrong to vote to leave EU?",
       subtitle = "YouGov Survey Results, 2-3 September 2019",
       caption = "Source: bit.ly/2lCJZVg",
       x = NULL, y = NULL) +
  scale_fill_manual(values = c(
    "Wrong" = "#ef8a62",
    "Right" = "#67a9cf",
    "Don't know" = "gray"
  ))

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Select theme

ggplot(brexit, aes(y = opinion, fill = opinion)) +
  geom_bar() +
  facet_wrap(~region, nrow = 1, labeller = label_wrap_gen(width = 12)) +
  guides(fill = "none") +
  labs(title = "Was Britain right/wrong to vote to leave EU?",
       subtitle = "YouGov Survey Results, 2-3 September 2019",
       caption = "Source: bit.ly/2lCJZVg",
       x = NULL, y = NULL) +
  scale_fill_manual(values = c("Wrong" = "#ef8a62",
                               "Right" = "#67a9cf",
                               "Don't know" = "gray")) +
  theme_minimal()

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Your turn!

RStudio Cloud > AE 07 - Brexit + Telling stories with dataviz > brexit.Rmd.
Change the visualisation in three different ways to tell slightly different stories with it each time.

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Tips for effective data visualization

Data Science in a Box

datasciencebox.org

Designing effective visualizations

Keep it simple

Use color to draw attention

Tell a story

Principles for effective visualizations

Principles for effective visualizations

Data

Order matters

Alphabetical order is rarely ideal

Order by frequency

Clean up labels

Alphabetical order is rarely ideal

Use inherent level order

Clean up labels

Put long categories on the y-axis

Long categories can be hard to read

Move them to the y-axis

And reverse the order of levels

Clean up labels

Pick a purpose

Segmented bar plots can be hard to read

Use facets

Avoid redundancy?

Redundancy can help tell a story

Be selective with redundancy

Use informative labels

A bit more info

Let's do better

Fix up facet labels

Select meaningful colors

Rainbow colors not always the right choice

Manually choose colors when needed

Choosing better colors

Use better colors

Select theme

Your turn!

Designing effective visualizations

Help