05-viz

Professor Shannon Ellis

UC San Diego
COGS 137 - Fall 2024

2024-10-17

Effective Data Visualization

Slides modified from datascienceinabox.org

Q&A

Q: Does ggplot2 allow access to the same color schemes that we use in Python? I’m familiar with using Matplot.lib, but it seems slightly different for ggplot2.
A: You can directly control color in R. We’ll discuss that, but you can also use a ton of available color palletes. See here.You’ll find what you largely have in matplotlib but also additional palettes.

Q: What are some common aesthetics options for graphs?
A: Discussing this today!

Q: Why in the world anyone would think that %>% is an acceptable operator
A: Hadley Wickham (the developer) has made SO many incredible updates to the consistency and syntax in R. This was just NOT one of them.

Course Announcements

Due Dates:

Lab 02 due tonight (11:59 PM)
- ~~Group re-coded as with WB/OF~~ <- removed from instructions
Lecture Participation survey “due” after class

Notes:

Lab03 now available
Lab01 scores posted; Scores on Canvas; feedback as “issue” on GH
Case Study Groups will be sent tomorrow; will discuss case study in class on Tues.

Keep it simple

Use color to draw attention

Tell a story

Principles for effective visualizations

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

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

The Data: Code

brexit <- tibble(
  opinion = c(
    rep("Right", 664), rep("Wrong", 787), rep("Don't know", 188)
  ),
  region = c(
    rep("london", 63), rep("rest_of_south", 241), rep("midlands_wales", 145), rep("north", 176), rep("scot", 39),
    rep("london", 110), rep("rest_of_south", 257), rep("midlands_wales", 152), rep("north", 176), rep("scot", 92),
    rep("london", 24), rep("rest_of_south", 49), rep("midlands_wales", 57), rep("north", 48), rep("scot", 10)
  )
)

Order matters

Alphabetical is rarely ideal

Plot
Code

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

fct_infreq: Reorder factors’ levels by frequency

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

Clean up labels

Plot
Code

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

Avoiding Alphabetical Order

Plot
Code

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

Use inherent level order

Relevel
Plot

fct_relevel: Reorder factor levels using a custom order

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

Clean up labels

Recode
Plot

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"
    )
  )

Put long categories on the y-axis

Long categories can be hard to read

Move them to the y-axis

Plot
Code

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

And reverse the order of levels

Plot
Code

fct_rev: Reverse order of factor levels

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

Clean up labels

Plot
Code

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

Pick a purpose

Segmented bar plots can be hard to read

Plot
Code

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

Avoid redundancy?

Redundancy can help tell a story

Plot
Code

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

Be selective with redundancy

Plot
Code

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

Use informative labels

Plot
Code

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
  )

A bit more info

Plot
Code

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
  )

Let’s do better

Plot
Code

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
  )

Fix up facet labels

Plot
Code

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
  )

Select meaningful colors

Rainbow colors not always best

Manually choose colors when needed

Plot
Code

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" 
  ))

Choosing better colors

colorbrewer2.org

Use better colors

Plot
Code

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" 
  ))

Select theme

Plot
Code

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()

Customize theme

Plot
Code

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(base_size = 16) + 
  theme(plot.title.position = "plot", 
        panel.grid.major.y = element_blank())

Your Turn

Read in the data (Data)
Think of at least three different ways to tell slightly different stories with these data
Try to implement at least one of these ideas!

Recap

Can you determine what needs to be done to improve the effectiveness of your visualizations?
Can you execute said improvements using ggplot2?
Can you tell a story with data?

05-viz

Effective Data Visualization

Q&A

Course Announcements

Suggested Reading

Keep it simple

Use color to draw attention

Tell a story

Principles for effective visualizations

Principles for effective visualizations

Data

The Data: Code

Order matters

Order by frequency

Clean up labels

Avoiding Alphabetical Order

Use inherent level order

Clean up labels

Put long categories on the y-axis

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 best

Manually choose colors when needed

Choosing better colors

Use better colors

Select theme

Customize theme

Your Turn

Recap