Data visualization with Python II

Lecture 23

Dr. Mine Çetinkaya-Rundel

Duke University
STA 313 - Spring 2026

Warm up

Announcements

  • Project 2 peer evaluation 1 due today at 5 pm – no extensions as we’d like to share summaries before lab tomorrow, those who haven’t yet submitted it received a reminder at 11 am

  • HW 5 posted, due Monday, April 20 at 5 pm

    • A bit on data prep
    • A bit on Shiny
    • A bit on Python
    • At a minimum, read it over before tomorrow’s lab

  • Next week:

    • Monday: In memory of Bill Cleveland – read the Graphical Perception paper by Cleveland and McGill (1984)
    • Wednesday: Your choice! Animation? Visual inference? A primer on dataviz with AI prior to guest speaker following week? 3D pie charts?! You tell me!

Setup

  • R:
library(tidyverse)
  • Python:
import polars as pl
from plotnine import *

From last time

Key differences from ggplot2

ggplot2 (R) plotnine (Python)
aes(x = var) aes(x="var") (quoted strings)
+ at end of line + at start of line (inside parens)
theme(legend.position = ...) theme(legend_position=...) (underscores)
No parens needed Wrap in () for multi-line plots
ggsave() .save() method on plot object 


# Saving a plot
p = ggplot(...) + geom_point()
p.save("my_plot.png", width=10, height=6, dpi=300)

Back to ae-16

Go to ae-16 and work on ae-16-R-and-Python.qmd.

Anscombe’s Quartet

What is Anscombe’s Quartet?

  • Four datasets created by statistician Francis Anscombe in 1973
  • Each dataset has 11 observations with x and y variables
  • Designed to illustrate the importance of visualizing data before analyzing it

The data

anscombe_wide
   x1 x2 x3 x4    y1   y2    y3    y4
1  10 10 10  8  8.04 9.14  7.46  6.58
2   8  8  8  8  6.95 8.14  6.77  5.76
3  13 13 13  8  7.58 8.74 12.74  7.71
4   9  9  9  8  8.81 8.77  7.11  8.84
5  11 11 11  8  8.33 9.26  7.81  8.47
6  14 14 14  8  9.96 8.10  8.84  7.04
7   6  6  6  8  7.24 6.13  6.08  5.25
8   4  4  4 19  4.26 3.10  5.39 12.50
9  12 12 12  8 10.84 9.13  8.15  5.56
10  7  7  7  8  4.82 7.26  6.42  7.91
11  5  5  5  8  5.68 4.74  5.73  6.89

Longer data

anscombe <- anscombe_wide |>
  pivot_longer(
    cols = everything(),
    names_to = c(".value", "set"),
    names_pattern = "(.)(.)"
  )

anscombe
# A tibble: 44 × 3
   set       x     y
   <chr> <dbl> <dbl>
 1 1        10  8.04
 2 2        10  9.14
 3 3        10  7.46
 4 4         8  6.58
 5 1         8  6.95
 6 2         8  8.14
 7 3         8  6.77
 8 4         8  5.76
 9 1        13  7.58
10 2        13  8.74
# ℹ 34 more rows

Summary statistics

anscombe |>
  group_by(set) |>
  summarize(
    mean_x = mean(x),
    mean_y = mean(y),
    sd_x = sd(x),
    sd_y = sd(y),
    cor_xy = cor(x, y)
  )
# A tibble: 4 × 6
  set   mean_x mean_y  sd_x  sd_y cor_xy
  <chr>  <dbl>  <dbl> <dbl> <dbl>  <dbl>
1 1          9   7.50  3.32  2.03  0.816
2 2          9   7.50  3.32  2.03  0.816
3 3          9   7.5   3.32  2.03  0.816
4 4          9   7.50  3.32  2.03  0.817

The statistics are (nearly) identical!

  • Mean of x: 9 (exactly)
  • Mean of y: 7.50 (to 2 decimal places)
  • Standard deviation of x: 3.32
  • Standard deviation of y: 2.03
  • Correlation: 0.816
  • Linear regression line: y = 3 + 0.5x

But are the datasets the same?

Let’s visualize!

ggplot(anscombe, aes(x = x, y = y)) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE, color = "steelblue") +
  facet_wrap(
    ~set, 
    labeller = labeller(set = c("1" = "Dataset I", "2" = "Dataset II", "3" = "Dataset III", "4" = "Dataset IV"))
  ) +
  theme_minimal(base_size = 16) +
  labs(
    title = "Anscombe's Quartet",
    subtitle = "Four datasets with nearly identical summary statistics"
  )

Let’s visualize!

What do we see?

  • Dataset I: Linear relationship (what we might expect)
  • Dataset II: Non-linear (quadratic) relationship
  • Dataset III: Perfect linear relationship with one outlier
  • Dataset IV: No relationship, but one extreme point creates correlation


The lesson

Always visualize your data! Summary statistics alone can be misleading.

ae-17

Visualize Anscombe’s Quartet in Python using plotnine! Try to get it to be as close to the plot below as possible.