Data science communication

Lecture 13

John Zito

Duke University
STA 199 Spring 2026

2026-03-02

While you wait: Participate 📱💻

Your first peer evaluation for your project is due at midnight tonight. You should have received an e-mail from TEAMMATES about it (check your junk folder!). Did you complete it yet?

Scan the QR code or go HERE. Log in with your Duke NetID.

Reminder

Project feedback returned today;
Make progress (clean, plot, summarize) by Wednesday night;
Thursday lab: continue to work and give another team feedback;
Friday: complete midsemester check-in;
Friday: Midterm 1 grades returned.

Forget this class exists during spring break. After we return, our last six weeks are about statistical modeling and inference.

Ugly plot

Honorable mention: Ben Mei-Dan

Code

mtcars |> 
  mutate(am = factor(am,
                       levels = c(1, 0),
                       labels = c("Manual", "Automatic"))) |> 
  mutate(vs = factor(vs, 
                     levels = c(0,1),
                     labels = c("V-shaped", "Straight"))) |> 
  group_by(am, vs) |> 
  summarize(mpg_total = sum(mpg), wt_total = sum(wt), .groups = "drop") |>
  pivot_longer(
    cols = c(mpg_total, wt_total),
    names_to = "metric",
    values_to = "value") |> 
  ggplot(aes(x = "", y = value, fill = metric)) + 
  geom_col(show.legend = TRUE) +
  coord_polar(theta = "y") +
  facet_grid(vs ~ am) +
  labs(
    title = "Faceted pie charts by am and vs",
    fill = "What the slice is"
  ) + 
  scale_fill_manual(values = c("hotpink", "limegreen")) +
  scale_y_reverse() +
  scale_x_discrete(labels = c("???")) + 
  theme_bw() +
  theme(
    legend.position = "bottom",
    axis.text = element_text(angle = 90, size = 16, color = "purple"),
    axis.title = element_text(size = 20, face = "bold.italic"),
    panel.grid.major = element_line(color = "red", size = 2)
  )

Honorable mention: Celina Chen

Code

ggplot(
  mtcars,
  aes(
    x = wt,
    y = mpg,
    color = factor(am, levels = c(1, 0)),
    shape = factor(vs, levels = c(1, 0))
  )
) +
  # Giant points + jitter = chaos
  geom_point(
    size = 9,
    alpha = 0.95,
    stroke = 2,
    position = position_jitter(width = 0.25, height = 1.5)
  ) +
  # Overlapping labels for maximum visual pain
  geom_text(
    aes(label = row.names(mtcars)),
    size = 3,
    angle = 45,
    vjust = -0.4,
    show.legend = FALSE
  ) +
  # Fragment into panels
  facet_wrap(~cyl, nrow = 1) +
  # Reverse + weird limits + tons of minor breaks
  scale_x_reverse(
    limits = c(5.5, 1.5),
    breaks = seq(5.5, 1.5, -0.5),
    minor_breaks = seq(5.5, 1.5, -0.1)
  ) +
  # Reverse + log transform = extra confusing
  scale_y_reverse(
    trans = "log10",
    limits = c(35, 8),
    breaks = c(35, 30, 25, 20, 15, 10, 8)
  ) +
  # Weaponized color choices + misleading labels
  scale_color_manual(
    values = c("magenta2", "chartreuse3"),
    labels = c("AUTO??", "MANUAL??"),
    guide = guide_legend(reverse = TRUE, title.position = "top")
  ) +
  # Aggressive shapes that don’t help interpretation
  scale_shape_manual(
    values = c(8, 4),
    labels = c("Vampire Engine", "Sauron Engine")
  ) +
  # Turn a scatterplot into a donut of confusion
  coord_polar(theta = "x") +
  labs(
    title = "💀 MPG vs WT (but in a cursed universe) 💀",
    subtitle = "Why read a scatterplot when you can suffer?",
    x = "WEIGHT (1000 lbs)\n(reversed & spun into a donut)",
    y = "Miles / gallon (log10, reversed, vibes only)",
    color = "Transmission (maybe)",
    shape = "Engine (definitely not)",
    caption = "Data: mtcars • Design: pure spite"
  ) +
  theme(
    # Use safe fonts so Quarto can render (no Comic Sans errors)
    text = element_text(family = "sans"),

    plot.background  = element_rect(fill = "hotpink", color = "limegreen", linewidth = 8),
    panel.background = element_rect(fill = "yellow",  color = "cyan", linewidth = 6),
    panel.grid.major = element_line(color = "red",  linewidth = 2, linetype = "dotdash"),
    panel.grid.minor = element_line(color = "blue", linewidth = 1, linetype = "longdash"),
    strip.background = element_rect(fill = "black", color = "white", linewidth = 3),

    strip.text = element_text(color = "orange", size = 18, face = "bold.italic"),
    axis.text  = element_text(color = "purple", size = 16, face = "bold", angle = 90, vjust = 0.5),
    axis.title = element_text(color = "green4", size = 22, face = "bold"),
    plot.title = element_text(color = "white", size = 28, face = "bold", hjust = 0),
    plot.subtitle = element_text(color = "black", size = 14, face = "italic", hjust = 1),

    legend.position = c(0.15, 0.85),
    legend.background = element_rect(fill = "white", color = "black", linewidth = 2),
    legend.key = element_rect(fill = "grey10"),
    legend.text  = element_text(color = "dodgerblue", size = 14),
    legend.title = element_text(color = "red", size = 16, face = "bold"),

    plot.margin = margin(5, 60, 5, 60)
  )

Honorable mention: Lachlan Black

Code

library(showtext)

showtext_auto()

set.seed(1)

bg_dots <- data.frame(
  x = runif(6000, -20, 20),
  y = runif(6000, -50, 100),
  s = runif(6000, 1, 8)
)

bg_poly <- data.frame(
  x = runif(4000, -20, 20),
  y = runif(4000, -50, 100),
  group = rep(1:400, each = 10)
)

bg_poly$fill_col <- rep(
  sample(c("red", "darkred", "firebrick"), 400, replace = TRUE),
  each = 10
)

ggplot(mtcars, aes(x = wt, y = mpg)) +

  # Background polygons
  geom_polygon(
    data = bg_poly,
    aes(x = x, y = y, group = group, fill = fill_col),
    inherit.aes = FALSE,
    alpha = 0.7,
    color = "black",
    linewidth = 0.3,
    show.legend = FALSE
  ) +

  # Background dots
  geom_point(
    data = bg_dots,
    aes(x = x, y = y, size = s),
    inherit.aes = FALSE,
    color = "darkred",
    alpha = 0.6,
    show.legend = FALSE
  ) +

  scale_fill_identity() +

  # Density layer (separate fill scale)
  ggnewscale::new_scale_fill() +

  stat_bin2d(
    bins = 25,
    aes(fill = after_stat(count)),
    alpha = 0.4,
    show.legend = FALSE
  ) +

  scale_fill_gradient(low = "red", high = "darkred") +

  # Real data
  geom_smooth(
    aes(color = factor(am, levels = c(1, 0))),
    se = FALSE,
    method = "lm",
    linetype = "dotted",
    linewidth = 3
  ) +

  geom_jitter(
    aes(
      color = factor(am, levels = c(1, 0)),
      shape = factor(vs),
      size = hp
    ),
    width = 0.7,
    height = 0.7,
    stroke = 5,
    alpha = 1
  ) +

  facet_wrap(~gear) +

  labs(
    title = "1974 Motor Trend UNITED STATES OF THE AMERICAS",
    subtitle = "visually aesthetic plot!!!!!",
    x = "WEIGHT?????????????????",
    y = "MILES per GALLLOOOOONS",
    color = "TRANSMISSION CHAOS",
    shape = "ENGINE OR SOMETHING",
    size = "HORSEPOWER???"
  ) +

  scale_color_manual(
    values = c("cyan", "#39FF14"),
    labels = c("Manual Mayhem", "Automatic Anarchy")
  ) +

  scale_x_reverse() +
  scale_y_continuous(trans = "sqrt") +

  coord_cartesian(
    xlim = c(6, 1),
    ylim = c(35, 10),
    clip = "off"
  ) +

  theme(
    legend.position = "right",
    legend.background = element_rect(
      fill = "black",
      color = "chartreuse",
      linewidth = 4
    ),
    legend.text = element_text(
      family = "mono",
      color = "white",
      size = 16
    ),
    legend.key = element_rect(fill = "orange"),

    text = element_text(
      family = "Comic Sans MS",
      size = 20,
      face = "bold"
    ),

    plot.title = element_text(
      size = 30,
      color = "green",
      face = "italic"
    ),

    plot.subtitle = element_text(
      size = 25,
      color = "yellow"
    ),

    panel.background = element_rect(fill = NA),
    plot.background  = element_rect(fill = NA),
    strip.background = element_rect(fill = NA),

    strip.text = element_text(
      size = 22,
      color = "white",
      face = "bold"
    ),

    panel.grid.major = element_line(
      color = "yellow",
      linewidth = 3,
      linetype = "dotdash"
    ),

    panel.grid.minor = element_line(
      color = "white",
      linewidth = 2
    ),

    axis.text.x = element_text(
      angle = 270,
      color = "chartreuse",
      size = 22
    ),

    axis.text.y = element_text(
      angle = 180,
      color = "gold",
      size = 24
    ),

    axis.ticks = element_line(
      linewidth = 3,
      color = "purple"
    ),

    axis.ticks.length = unit(1.5, "cm"),

    panel.spacing = unit(0.1, "cm")
  ) +

  guides(
    size = guide_legend(
      override.aes = list(
        color = "cyan",
        fill = "cyan1",
        alpha = 1,
        size = 3
      )
    )
  )

Honorable mentschen: Nicholas Wang

Code

mtcars |> 
  mutate(
    am = case_when(
      am == 0 ~"ZiȚo",
      am == 1 ~ "zĩtO"
    ),
    am = fct_relevel(am, "Manual"),
    vs = case_when(
      vs == 0 ~ "žITO",
      vs == 1 ~ "zitö"
    )
  ) |> 
  ggplot(
    aes(y = wt, 
        x = mpg, 
        color = am)) +
    geom_point(
      aes(shape = vs)
    ) +
    labs(
      x = "Weight (1000 lbs)",
      y = "Fuel Efficiency (Miles / gallon)",
      title = "RAHHHHHHHHHHHHHHHH",
      color = "Informative Label",
      shape = "sta1nana"
    ) +
    theme(legend.position = c(0.5,0.5),
          legend.text = element_text(size = 40),
          legend.direction = "horizontal",
          panel.background = element_rect(fill = "chartreuse"))

Honorable mentschen: Zachary Hyman

Code

library(jpeg)
library(grid)

mt_clean <- mtcars |> 
  mutate(
    am = if_else(am == 0, "automatic", "manual"),
    am = fct_relevel(am, "manual", "automatic")
  )

jcz_file <- "images/13/jcz.jpg" 
image_data <- jpeg::readJPEG(jcz_file)
grob_image <- rasterGrob(image_data, width = unit(1, "npc"), 
                         height = unit(1, "npc"), interpolate = FALSE)

ggplot(mt_clean, aes(x = wt, y = mpg, color = factor(carb), 
                     shape = am, size = hp)) +
  annotation_custom(grob_image, xmin = -Inf, xmax = Inf, ymin = -Inf, 
                    ymax = Inf) +
  geom_point(alpha = 0.6) +
  labs(
    x = "Your Negligence",
    y = "Zito Anger Level",
    color = "Carburetors",
    shape = "AM",
    size = "horsepower",
    title = "Zito Owns Your Car",
    subtitle = "And he wants it back."
  ) +
  theme_calc() +
  theme(
    legend.position = "bottom"
  ) +
  facet_wrap(~ carb) +
  scale_color_economist()

Honorable mentschen: Parker Upton

Code

library(jpeg)

mtcars_categorical <- mtcars |>
  mutate(
    transmission = case_when(
      am == 0 ~ "Automatic",
      am == 1 ~ "Manual"
    ),
      engine = case_when(
        vs == 0 ~ "V-shaped",
        vs == 1 ~ "straight"
      )
  )

url <- "https://scholars.duke.edu/profile-images/thumbnail500/0935990.jpg"
tmp <- tempfile(fileext = ".jpg")

download.file(url, tmp, mode = "wb")
img <- readJPEG(tmp)

ggplot(mtcars_categorical, 
       aes(x = wt, y = mpg, color = am)) +
  annotation_raster(
    img,
    xmin = -Inf, xmax = Inf,
    ymin = -Inf, ymax = Inf
  ) +
  geom_point(size = 10, shape = 14) +
  scale_color_gradient(low = "yellow", high = "yellow") +
  labs(
    x = "Gallons Per Mileage",
    y = "Weight in 1000 pounds (measured in thousands of pounds, not hundreds, 
    not kilograms, not grams, not liters, not John Zitos, but most certainly 
    thousands of pounds",
    title = "CAr StUFf",
    subtitle = "Idk what to put here"
  ) +
  coord_cartesian(xlim = c(0, 10), ylim = c(-1000, 500)) +
  theme(
    panel.background = element_rect(fill = "black"),
    plot.background = element_rect(fill = "orange", color = "black"),
    legend.position = c(0.85, 0.15)
  )

Third place: Henry O’Hara

“I know we are supposed to make 5 changes to the graph, but one main one was really all I needed. No need to explain why it’s ugly.”

Code

#I used Google Gemini 3 to show me how to make the image the background.
#The date was 2.15.26
#The link was https://gemini.google.com/share/baddfa4f4e0e

library(magick)
library(ggpubr)

unc_url <- "https://i.ytimg.com/vi/QGUKD_UgSmE/hq720.jpg?sqp=-oaymwEhCK4FEIIDSFryq4qpAxMIARUAAAAAGAElAADIQj0AgKJD&rs=AOn4CLADGSk5U3I0StJLmaxqeBIdd4AE-w"
unc_img <- magick::image_read(unc_url)

mtcars |> 
  mutate(am = factor(am, levels = c(1,0), 
                     labels = c("Manual", "Automatic")),
         vs = factor(vs, levels = c(1,0),
                     labels = c("Straight", "V-Shaped"))
         ) |> 
  ggplot(aes(x = wt, y = mpg, color = am, shape = vs)) +
  background_image(unc_img) +
  geom_point() +
  labs(
    x = "Weight (1000 lbs)",
    y = "Miles / gallon",
    color = "Transmission",
    shape = "Engine",
    title = "Weight vs. Fuel Efficiency (Ugly)",
    subtitle = "By Transmission and Engine Type"
  ) + 
  theme_light() +
  theme(legend.position = "right")

First place: Sammy Ciuni

Code

library(jpeg)
library(png)
library(ggimage)
library(ggpubr)
library(systemfonts)

long_live_sal <- readJPEG("images/13/download.jpg")
left_eye <- "images/13/left-eye.png"
right_eye <- "images/13/right-eye.png"

mtcars |>
  mutate(am = case_when(
      am == 1 ~ "Manual", 
      .default = "Automatic"),
    vs = case_when(
      vs == 1 ~ "Straight", 
      .default = "V-shaped"),
    img = if_else(
      vs == "Straight", left_eye, right_eye
     )) |>
ggplot(aes(x = wt, 
           y = mpg,
           shape = factor(vs))) +
  background_image(long_live_sal) +
  geom_image(aes(
    image = img), size = 0.1) +
  geom_point(aes(color = am), size = 3, alpha = 0.3) +
  labs(
    x = "Weight (1000 lbs)",
    y = "Miles / Gallon",
    color = "Transmission Type",
    shape = "Engine",
    title = "Gas Mileage Compared to Vehicle Weight",
    subtitle = "and Transmission Type"
  ) +
  scale_x_continuous(breaks = seq(0, 6, 0.09)) +
  scale_color_discrete(limits = c("Manual", "Automatic")) +
  scale_shape_discrete(limits = c("V-shaped", "Straight")) +
  theme(legend.position = "right",
        plot.title = element_text(size = 30))

First place: Austin Rios

Know Your Meme.

Code

library(png)
library(cowplot)
library(magick)

jackie_photo <- readPNG("images/13/jackie_o.png")

theme_set(theme_cowplot())

bad_graph <- mtcars |> 
  mutate(am = if_else(am == 0, "manhandle it", "not freaky :("),
         am = fct_relevel(am, "manhandle it", "not freaky :("), 
         vs = if_else(vs == 0, "bella hadid", "straight")
         ) |> 
  ggplot(aes(x = wt, y = mpg, color = am, shape = vs)) +
  geom_point(alpha = 1) +
  theme(legend.position = "inside",
        plot.title = element_text(size = 25, color = "lawngreen"),
        plot.subtitle = element_text(size = 30, color = "mediumvioletred"),
       # plot.background = element_rect(fill = "yellow3"),
        text = element_text(color = "gold4", family = "serif")
        ) + 
  scale_color_manual(values = c("manhandle it" = "gray99", 
                               "not freaky :(" = "gray98")) + 
  labs(
    x = "how much junk in the trunk",
    y = "jackie kennedy miles per sheet metal eaten", 
    title = "jackie want sheet metal",
    subtitle = "omnomnom", 
    color = "prindl",
    shape = "engine size ?"
  )

ggdraw() +
  draw_image(jackie_photo, scale = 1.2) + 
  draw_plot(bad_graph)

Project

The bottom line, at the top

Write an in-depth article that might appear in the popular press (NYT, Chronicle, etc);
Audience: intelligent but non-technical reader that is unfamiliar with the domain;
Emphasize clear communication, attractive and informative graphics, and carefully chosen numerical summaries;
R code and raw output should not appear;
All figures and tables should be labeled with captions.

Telling a story

Setup

library(tidyverse)
library(tidymodels)
library(ggrepel)
library(ggthemes)
library(palmerpenguins)

Multiple ways of telling a story

Sequential reveal: Motivation, then resolution
Instant reveal: Resolution, and hidden in it motivation

Simplicity vs. complexity

When you’re trying to show too much data at once you may end up not showing anything.

Never assume your audience can rapidly process complex visual displays
Don’t add variables to your plot that are tangential to your story
Don’t jump straight to a highly complex figure; first show an easily digestible subset (e.g., show one facet first)
Aim for memorable, but clear

Project note: Make sure to leave time to iterate on your plots after you practice your presentation. If certain plots or outputs are getting too wordy to explain, take time to simplify them!

Consistency vs. repetitiveness

Be consistent but don’t be repetitive.

Use consistent features throughout plots (e.g., same color represents same level on all plots)
Aim to use a different type of summary or visualization for each distinct analysis

Project note: If possible, ask a friend who is not in the class to listen to your presentation and then ask them what they remember. Then, ask yourself: is that what you wanted them to remember?

Participate 📱💻

When you read an article, which style do you prefer?

Sequential reveal
Instant reveal
neither

Scan the QR code or go HERE. Log in with your Duke NetID.

Designing effective visualizations

Data

d <- tribble(
  ~category,                     ~value,
  "Cutting tools"                , 0.03,
  "Buildings and administration" , 0.22,
  "Labor"                        , 0.31,
  "Machinery"                    , 0.27,
  "Workplace materials"          , 0.17
)
d

# A tibble: 5 × 2
  category                     value
  <chr>                        <dbl>
1 Cutting tools                 0.03
2 Buildings and administration  0.22
3 Labor                         0.31
4 Machinery                     0.27
5 Workplace materials           0.17

Keep it simple

Judging relative area

Use color to draw attention

Play with themes for a non-standard look

Go beyond ggplot2 themes – ggthemes

Tell a story

Leave out non-story details

Order matters

Clearly indicate missing data

Reduce cognitive load

Use descriptive titles

Annotate figures

Plot sizing and layout

Sample plots

p_hist <- ggplot(mtcars, aes(x = mpg)) +
  geom_histogram(binwidth = 2)

p_text <- mtcars |>
  rownames_to_column() |>
  ggplot(aes(x = disp, y = mpg)) +
  geom_text_repel(aes(label = rowname)) +
  coord_cartesian(clip = "off")

Small `fig-width`

For a zoomed-in look

```{r}
#| fig-width: 3
#| fig-asp: 0.618

p_hist
```

Large `fig-width`

For a zoomed-out look

```{r}
#| fig-width: 6
#| fig-asp: 0.618

p_hist
```

`fig-width` affects text size

Multiple plots on a slide

First, ask yourself, must you include multiple plots on a slide? For example, is your narrative about comparing results from two plots?

If no, then don’t! Move the second plot to to the next slide!
If yes, use columns and sequential reveal.

Quarto

Writing your project report with Quarto

Figure sizing: fig-width, fig-height, etc. in code chunks.
Figure layout: layout-ncol for placing multiple figures in a chunk.
Further control over figure layout with the patchwork package.
Chunk options around what makes it in your final report: message, echo, etc.
Cross referencing figures and tables.
Adding footnotes and citations.

As seen in Figure 1, there is a positive and relatively strong relationship between body mass and flipper length of penguins.

ggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g)) +
  geom_point()

Figure 1: The relationship between body mass and flipper length of penguins.

As seen in @fig-penguins, there is a positive and relatively strong relationship between body mass and flipper length of penguins.

```{r}
#| label: fig-penguins

ggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g)) +
  geom_point()
```

Cross referencing tables

Output
Input

Table 1 displays summaries of flipper length by species.

penguins |>
  group_by(species) |>
  summarize(
    Mean = mean(flipper_length_mm, na.rm = TRUE),
    Median = median(flipper_length_mm, na.rm = TRUE),
    SD = sd(flipper_length_mm, na.rm = TRUE)
  ) |>
  knitr::kable(digits = 3)

Table 1: Flipper length summaries by species

species	Mean	Median	SD
Adelie	189.954	190	6.539
Chinstrap	195.824	196	7.132
Gentoo	217.187	216	6.485

@tbl-penguins displays summaries of flipper length by species.

```{r}
#| label: tbl-penguins
#| tbl-cap: Flipper length summaries by species

penguins |>
  group_by(species) |>
  summarize(
    Mean = mean(flipper_length_mm, na.rm = TRUE),
    Median = median(flipper_length_mm, na.rm = TRUE),
    SD = sd(flipper_length_mm, na.rm = TRUE)
  ) |>
  knitr::kable(digits = 3)
```

Take A Sad Plot & Make It Better

Going the extra mile

Trends instructional staff employees in universities

The American Association of University Professors (AAUP) is a nonprofit membership association of faculty and other academic professionals. This report by the AAUP shows trends in instructional staff employees between 1975 and 2011, and contains the following image. What trends are apparent in this visualization?

ae-14-effective-dataviz

Go to your ae project in RStudio.
If you haven’t yet done so, make sure all of your changes up to this point are committed and pushed, i.e., there’s nothing left in your Git pane.
If you haven’t yet done so, click Pull to get today’s application exercise file: ae-14-effective-dataviz.qmd.
Work through the application exercise in class, and render, commit, and push your edits.

Data prep

Code

library(tidyverse)
library(scales)

staff <- read_csv("data/instructional-staff.csv")

staff_long <- staff |>
  pivot_longer(
    cols = -faculty_type, names_to = "year",
    values_to = "percentage"
  ) |>
  mutate(
    percentage = as.numeric(percentage),
    faculty_type = fct_relevel(
      faculty_type,
      "Full-Time Tenured Faculty",
      "Full-Time Tenure-Track Faculty",
      "Full-Time Non-Tenure-Track Faculty",
      "Part-Time Faculty",
      "Graduate Student Employees"
    ),
    year = as.numeric(year),
    faculty_type_color = if_else(faculty_type == "Part-Time Faculty", "firebrick3", "gray40")
  )

Pick a purpose

Code

p <- ggplot(
  staff_long,
  aes(
    x = year,
    y = percentage,
    color = faculty_type_color, group = faculty_type
    )
  ) +
  geom_line(linewidth = 1, show.legend = FALSE) +
  labs(
    x = NULL,
    y = "Percent of Total Instructional Staff",
    color = NULL,
    title = "Trends in Instructional Staff Employment Status, 1975-2011",
    subtitle = "All Institutions, National Totals",
    caption = "Source: US Department of Education, IPEDS Fall Staff Survey"
  ) +
  scale_y_continuous(labels = label_percent(accuracy = 1, scale = 1)) +
  scale_color_identity() +
  theme(
    plot.caption = element_text(size = 8, hjust = 0),
    plot.margin = margin(0.1, 0.6, 0.1, 0.1, unit = "in")
  ) +
  coord_cartesian(clip = "off") +
  annotate(
    geom = "text",
    x = 2012, y = 41, label = "Part-Time\nFaculty",
    color = "firebrick3", hjust = "left", size = 5
  ) +
  annotate(
    geom = "text",
    x = 2012, y = 13.5, label = "Other\nFaculty",
    color = "gray40", hjust = "left", size = 5
  ) +
  annotate(
    geom = "segment",
    x = 2011.5, xend = 2011.5,
    y = 7, yend = 20,
    color = "gray40", linetype = "dotted"
  )

p

Use labels to communicate the message

Code

p +
  labs(
    title = "Instruction by part-time faculty on a steady increase",
    subtitle = "Trends in Instructional Staff Employment Status, 1975-2011\nAll Institutions, National Totals",
    caption = "Source: US Department of Education, IPEDS Fall Staff Survey",
    y = "Percent of Total Instructional Staff",
    x = NULL
  )

Simplify

Code

p +
  labs(
    title = "Instruction by part-time faculty on a steady increase",
    subtitle = "Trends in Instructional Staff Employment Status, 1975-2011\nAll Institutions, National Totals",
    caption = "Source: US Department of Education, IPEDS Fall Staff Survey",
    y = "Percent of Total Instructional Staff",
    x = NULL
  ) +
  theme(panel.grid.minor = element_blank())

Summary

Represent percentages as parts of a whole
Place variables representing time on the x-axis when possible
Pay attention to data types, e.g., represent time as time on a continuous scale, not years as levels of a categorical variable
Prefer direct labeling over legends
Use accessible colors
Use color to draw attention
Pick a purpose and label, color, annotate for that purpose
Communicate your main message directly in the plot labels
Simplify before you call it done (a.k.a. “Before you leave the house, look in the mirror and take one thing off”)

Data science communication

While you wait: Participate 📱💻

Reminder

Ugly plot

Honorable mention: Ben Mei-Dan

Honorable mention: Celina Chen

Honorable mention: Lachlan Black

Honorable mentschen: Nicholas Wang

Honorable mentschen: Zachary Hyman

Honorable mentschen: Parker Upton

Third place: Henry O’Hara

First place: Sammy Ciuni

First place: Austin Rios

Project

The bottom line, at the top

Telling a story

Setup

Multiple ways of telling a story

Simplicity vs. complexity

Consistency vs. repetitiveness

Participate 📱💻

Designing effective visualizations

Data

Keep it simple

Judging relative area

Use color to draw attention

Play with themes for a non-standard look

Go beyond ggplot2 themes – ggthemes

Tell a story

Leave out non-story details

Order matters

Clearly indicate missing data

Reduce cognitive load

Use descriptive titles

Annotate figures

Plot sizing and layout

Sample plots

Small fig-width

Large fig-width

fig-width affects text size

Multiple plots on a slide

Quarto

Writing your project report with Quarto

Cross referencing figures

Cross referencing tables

Take A Sad Plot & Make It Better

Going the extra mile

Trends instructional staff employees in universities

ae-14-effective-dataviz

Data prep

Pick a purpose

Use labels to communicate the message

Simplify

Summary

Small `fig-width`

Large `fig-width`

`fig-width` affects text size