Midterm + Visualization
After the October 19 midterm, begin data visualization with Lecture 7. Build your first ggplot2 histograms, connect the code to the chart, and describe a numeric distribution in Classwork 10.
Start Lecture 7 from the beginning, then complete Classwork 10: Distribution of a Numeric Variable. Use the built-in mpg data to make histograms of city fuel economy, compare bin widths, and explain the patterns you see.
Focus
- A chart begins with a question
- Data, aesthetic mappings, and geoms
- Histograms and distribution shapes
Prepare
- Start Lecture 7 at the title slide
- Open an R script and load tidyverse
- Have Classwork 10 ready; no CSV import is needed
Practice
- Plot city fuel economy from mpg
- Compare 1-MPG and 5-MPG bin widths
- Explain the axes, peaks, and tails
Learning targets
- State a question that a chart can help answer.
- Identify the data frame, aesthetic mapping, and geom in a
ggplot()expression. - Build a histogram of a numeric variable with
geom_histogram(). - Explain what the horizontal axis represents and what each bar’s height counts.
- Compare how different bin widths reveal or hide detail in the same data.
- Describe a distribution’s peaks and longer tail without confusing the tail with the tallest bars.
Core pattern: a numeric distribution
library(tidyverse)
ggplot(data = mpg,
mapping = aes(x = cty)) +
geom_histogram(binwidth = 2) +
labs(x = "City fuel economy (MPG)",
y = "Number of vehicle configurations")Each row in mpg represents a vehicle model configuration, and cty records city fuel economy in miles per gallon. The histogram groups these values into bins and counts the rows in each bin; no variable needs to be mapped to y.
In Classwork 10, change the bin width to 1 and 5, compare the resulting charts, and describe their peaks and tails. The data stay the same even when the visible detail changes.
Lecture slides
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