Hands-on Exercise 4c: Visualising Uncertainty

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11.1 Learning Outcome

  • Plot statistics error bars using ggplot2
  • Plot interactive error bars using ggplot2, plotly, and DT
  • Create advanced uncertainty visualizations using ggdist
  • Create hypothetical outcome plots (HOPs) using the ungeviz package

11.2 Getting Started

11.2.1 Installing and Loading Packages

pacman::p_load(plotly, crosstalk, DT, 
               ggdist, ggridges, colorspace,
               gganimate, tidyverse)

11.2.2 Importing the Data

exam <- read_csv("/Users/sharon/OneDrive - Singapore Management University/isss608data/hands-on_exercise2/Exam_data.csv")
Rows: 322 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (4): ID, CLASS, GENDER, RACE
dbl (3): ENGLISH, MATHS, SCIENCE

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

11.3 Visualizing the Uncertainty of Point Estimates

Summary Statistics

my_sum <- exam %>%
  group_by(RACE) %>%
  summarise(
    n = n(),
    mean = mean(MATHS),
    sd = sd(MATHS)
  ) %>%
  mutate(se = sd / sqrt(n - 1))

Display the Table

knitr::kable(head(my_sum), format = 'html')
RACE n mean sd se
Chinese 193 76.50777 15.69040 1.132357
Indian 12 60.66667 23.35237 7.041005
Malay 108 57.44444 21.13478 2.043177
Others 9 69.66667 10.72381 3.791438

11.3.1 Standard Error Bars

ggplot(my_sum) +
  geom_errorbar(
    aes(x = RACE, ymin = mean - se, ymax = mean + se),
    width = 0.2, colour = "black", alpha = 0.9, linewidth = 0.5
  ) +
  geom_point(
    aes(x = RACE, y = mean),
    stat = "identity", color = "red", size = 1.5, alpha = 1
  ) +
  ggtitle("Standard error of mean maths score by race")

11.3.2 95% Confidence Interval

ggplot(my_sum) +
  geom_errorbar(
    aes(x = reorder(RACE, -mean), ymin = mean - 1.96 * se, ymax = mean + 1.96 * se),
    width = 0.2, colour = "black", alpha = 0.9, linewidth = 0.5
  ) +
  geom_point(
    aes(x = RACE, y = mean),
    stat = "identity", color = "red", size = 1.5, alpha = 1
  ) +
  labs(x = "Maths score", title = "95% confidence interval of mean maths score by race")

11.3.3 99% Confidence Interval with Interactive Plot

shared_df = SharedData$new(my_sum)

bscols(widths = c(4, 8),
  ggplotly(
    ggplot(shared_df) +
      geom_errorbar(aes(x = reorder(RACE, -mean), ymin = mean - 2.58 * se, ymax = mean + 2.58 * se),
                    width = 0.2, colour = "black", alpha = 0.9, size = 0.5) +
      geom_point(aes(x = RACE, y = mean,
                     text = paste("Race:", RACE, "<br>N:", n,
                                  "<br>Avg. Scores:", round(mean, 2),
                                  "<br>99% CI:[", round(mean - 2.58 * se, 2), ",",
                                  round(mean + 2.58 * se, 2), "]")),
                 stat = "identity", color = "red", size = 1.5, alpha = 1) +
      xlab("Race") + ylab("Average Scores") +
      theme_minimal() +
      theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust = 1)) +
      ggtitle("99% Confidence interval of average maths scores by race"),
    tooltip = "text"
  ),
  DT::datatable(shared_df, rownames = FALSE, class = "compact", width = "100%",
                options = list(pageLength = 10, scrollX = TRUE),
                colnames = c("No. of pupils", "Avg Scores", "Std Dev", "Std Error")) %>%
    formatRound(columns = c("mean", "sd", "se"), digits = 2)
)
Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
Warning in geom_point(aes(x = RACE, y = mean, text = paste("Race:", RACE, :
Ignoring unknown aesthetics: text

11.4 Visualising Uncertainty with ggdist

11.4.1 Basic Point + Interval Plot

exam %>%
  ggplot(aes(x = RACE, y = MATHS)) +
  stat_pointinterval() +
  labs(title = "Visualising confidence intervals of mean math score",
       subtitle = "Mean Point + Multiple-interval plot")

11.4.2 Median + 95% Confidence Intervals

exam %>%
  ggplot(aes(x = RACE, y = MATHS)) +
  stat_pointinterval(.width = 0.95, .point = median, .interval = qi) +
  labs(title = "Visualising confidence intervals of median math score",
       subtitle = "Median Point + Multiple-interval plot")
Warning in layer_slabinterval(data = data, mapping = mapping, stat =
StatPointinterval, : Ignoring unknown parameters: `.point` and `.interval`

11.4.3 Showing Both 95% and 99% CI

exam %>%
  ggplot(aes(x = RACE, y = MATHS)) +
  stat_pointinterval(.width = c(0.95, 0.99), show.legend = FALSE) +
  labs(title = "Visualising 95% and 99% confidence intervals",
       subtitle = "Mean Point + Multiple-interval plot")

11.4.4 Gradient + Interval Plot

exam %>%
  ggplot(aes(x = RACE, y = MATHS)) +
  stat_gradientinterval(fill = "skyblue", show.legend = TRUE) +
  labs(title = "Visualising confidence intervals of mean math score",
       subtitle = "Gradient + interval plot")

11.5 Hypothetical Outcome Plots (HOPs)

11.5.1 Install and Load ungeviz Package

devtools::install_github("wilkelab/ungeviz")
Using GitHub PAT from the git credential store.
Skipping install of 'ungeviz' from a github remote, the SHA1 (74e1651b) has not changed since last install.
  Use `force = TRUE` to force installation
library(ungeviz)

11.5.2 Generate HOP Plot

ggplot(data = exam, aes(x = factor(RACE), y = MATHS)) +
  geom_point(position = position_jitter(height = 0.3, width = 0.05),
             size = 0.4, color = "#0072B2", alpha = 1/2) +
  geom_hpline(data = sampler(25, group = RACE),
              height = 0.6, color = "#D55E00") +
  theme_bw() +
  transition_states(.draw, 1, 3)
Warning in geom_hpline(data = sampler(25, group = RACE), height = 0.6, color =
"#D55E00"): Ignoring unknown parameters: `height`