Creating Good Graphics

Stat 251

2026-02-19

Homework: Graphics

How does the homework tie to the readings?
Working on skills: how to make graphics
Work on concepts: what are mappings, and how do they effect the conclusions
Work on presentation: what are more effective ways of presenting information

Homework rubric

Rubric for homework assignment. Data exploration is worth 2 points, and includes each variable explored and each plot described with 1-2 sentences. Grammar of graphics - probabilities and grammar of graphics - agreement are worth 1.5 points each. Ugly chart in R is worth 2.5 points, including the explanation of why the chart is ugly. Ugly chart in python is worth 2.5 points, including the explanation of why the chart is ugly.

Graphics week homework

Part I: Make visual summaries for two new data sets and think about mappings
Part II: Use your knowledge to create the worst!

Data Exploration

Make charts for all variables that are listed by name:

groundhogs.csv

lat, long
country
isGroundhog
active
predictionsCount

predictions.csv

isGroundhog
year
shadow

Variable `active`

library(tidyverse)
groundhogs <- read.csv("https://raw.githubusercontent.com/stat-assignments/eda-groundhogs/97ab0e01b64aa3a1749247983a9b05a0c30b5c0c/groundhogs.csv")
groundhogs %>% ggplot(aes(x = active)) + geom_bar()

This is a barchart of the variable active, the variable is mapped to the x axis, the count for each bar (corresponding to the height of the bars) is mapped to y. Finding: Very few (2) groundhogs are not active.

Probabilities and Agreement

Answer the following two questions using charts. Explain your chart, and explain how it answers the question.

Do different groundhogs have different probabilities of predicting 6 more weeks of winter?

How much do North American groundhogs tend to agree on their predictions?

Do different groundhogs have different probabilities of predicting 6 more weeks of winter?

predictions <- read.csv("https://raw.githubusercontent.com/stat-assignments/eda-groundhogs/refs/heads/main/groundhog-predictions.csv")

predictions %>% 
  mutate(name = reorder(factor(name), name, length)) %>%
  ggplot(aes(x = name)) + geom_bar() +
  geom_bar(aes( weight = shadow), fill = "darkorange") + 
  theme(axis.text.x = element_text(angle = 60, hjust = 1)) + 
  ggtitle("Number of predictions\nNumber of times seeing a shadow in orange")

What about missing values in the shadow variable?

How do we need to change the previous chart?

predictions <- read.csv("https://raw.githubusercontent.com/stat-assignments/eda-groundhogs/refs/heads/main/groundhog-predictions.csv")

predictions %>% 
  filter(!is.na(shadow)) %>%
  mutate(name = reorder(factor(name), name, length)) %>%
  ggplot(aes(x = name)) + geom_bar(aes(fill=factor(shadow)), position = "fill") +
  theme(axis.text.x = element_text(angle = 60, hjust = 1))