Exploring data I

Lecture 4

Author
Affiliation

Dr. Mine Çetinkaya-Rundel

Duke University
STA 199 - Fall 2024

Published

September 10, 2024

Warm-up

While you wait…

Prepare for today’s application exercise: ae-04-gerrymander-explore-I

  • Go to your ae project in RStudio.

  • Make sure all of your changes up to this point are committed and pushed, i.e., there’s nothing left in your Git pane.

  • Click Pull to get today’s application exercise file: ae-04-gerrymander-explore-I.qmd.

  • Wait till the you’re prompted to work on the application exercise during class before editing the file.

Announcements

  • Weekend office hours in Old Chem – bring your Duke Card, you’ll need to swipe/tap in to get in

  • AEs are due by the end of class (precisely, by 2 pm) – done = at least one commit + push

  • Coding principles bonus office hour, RSVP at https://forms.gle/mufcsHnPXejZbkdT8 – Thursday, 9/12 at 7:30 pm at Old Chem 116

  • RStudio visual editor acting up?

    • Using Chrome – make sure to update!

    • Using another browser or update doesn’t solve the problem, come by my office hours to diagnose

  • New resource: Code along videos

  • Any questions about lab before we get started?

Exploratory data analysis

Packages

── Attaching core tidyverse packages ────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     
── Conflicts ──────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Data: gerrymander 

gerrymander
# A tibble: 435 × 12
   district last_name first_name party16 clinton16 trump16 dem16 state
   <chr>    <chr>     <chr>      <chr>       <dbl>   <dbl> <dbl> <chr>
 1 AK-AL    Young     Don        R            37.6    52.8     0 AK   
 2 AL-01    Byrne     Bradley    R            34.1    63.5     0 AL   
 3 AL-02    Roby      Martha     R            33      64.9     0 AL   
 4 AL-03    Rogers    Mike D.    R            32.3    65.3     0 AL   
 5 AL-04    Aderholt  Rob        R            17.4    80.4     0 AL   
 6 AL-05    Brooks    Mo         R            31.3    64.7     0 AL   
 7 AL-06    Palmer    Gary       R            26.1    70.8     0 AL   
 8 AL-07    Sewell    Terri      D            69.8    28.6     1 AL   
 9 AR-01    Crawford  Rick       R            30.2    65       0 AR   
10 AR-02    Hill      French     R            41.7    52.4     0 AR   
# ℹ 425 more rows
# ℹ 4 more variables: party18 <chr>, dem18 <dbl>, flip18 <dbl>,
#   gerry <fct>

What is gerrymandering?

Data: gerrymander

What is a good first function to use to get to know a dataset?

Data: gerrymander

  • Rows: Congressional districts

  • Columns:

    • Congressional district and state

    • 2016 election: winning party, % for Clinton, % for Trump, whether a Democrat won the House election, name of election winner

    • 2018 election: winning party, whether a Democrat won the 2018 House election

    • Whether a Democrat flipped the seat in the 2018 election

    • Prevalence of gerrymandering: low, mid, and high

Variable types

Variable Type
district categorical, ID
last_name categorical, ID
first_name categorical, ID
party16 categorical
clinton16 numerical, continuous
trump16 numerical, continuous
dem16 categorical
state categorical
party18 categorical
dem18 categorical
flip18 categorical
gerry categorical, ordinal

Univariate analysis

Univariate analysis

Analyzing a single variable:

  • Numerical: histogram, box plot, density plot, etc.

  • Categorical: bar plot, pie chart, etc.

Histogram - Step 1

ggplot(gerrymander)

Histogram - Step 2

ggplot(gerrymander, aes(x = trump16))

Histogram - Step 3

ggplot(gerrymander, aes(x = trump16)) +
  geom_histogram()
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Histogram - Step 4

Histogram - Step 5

ggplot(gerrymander, aes(x = trump16)) +
  geom_histogram(binwidth = 5) +
  labs(
    title = "Percent of vote received by Trump in 2016 Presidential Election",
    subtitle = "From each Congressional District",
    x = "Percent of vote",
    y = "Count"
  )

Box plot - Step 1

ggplot(gerrymander)

Box plot - Step 2

ggplot(gerrymander, aes(x = trump16))

Box plot - Step 3

ggplot(gerrymander, aes(x = trump16)) +
  geom_boxplot()

Box plot - Alternative Step 2 + 3

Box plot - Step 4

ggplot(gerrymander, aes(x = trump16)) +
  geom_boxplot() +
  labs(
    title = "Percent of vote received by Trump in 2016 Presidential Election",
    subtitle = "From each Congressional District",
    x = "Percent of vote",
    y = NULL
  )

Density plot - Step 1

ggplot(gerrymander)

Density plot - Step 2

ggplot(gerrymander, aes(x = trump16))

Density plot - Step 3

ggplot(gerrymander, aes(x = trump16)) +
  geom_density()

Density plot - Step 4

Density plot - Step 5

Density plot - Step 6

Density plot - Step 7

Density plot - Step 8

ggplot(gerrymander, aes(x = trump16)) +
  geom_density(color = "firebrick", fill = "firebrick1", alpha = 0.5, linewidth = 2) +
  labs(
    title = "Percent of vote received by Trump in 2016 Presidential Election",
    subtitle = "From each Congressional District",
    x = "Percent of vote",
    y = "Density"
  )

Summary statistics

Distribution of votes for Trump in the 2016 election

Describe the distribution of percent of vote received by Trump in 2016 Presidential Election from Congressional Districts.

  • Shape: The distribution of votes for Trump in the 2016 election from Congressional Districts is unimodal and left-skewed.

  • Center: The percent of vote received by Trump in the 2016 Presidential Election from a typical Congressional Districts is 48.7%.

  • Spread: In the middle 50% of Congressional Districts, 34.8% to 58.1% of voters voted for Trump in the 2016 Presidential Election.

  • Unusual observations: -

Bivariate analysis

Bivariate analysis

Analyzing the relationship between two variables:

  • Numerical + numerical: scatterplot

  • Numerical + categorical: side-by-side box plots, violin plots, etc.

  • Categorical + categorical: stacked bar plots

  • Using an aesthetic (e.g., fill, color, shape, etc.) or facets to represent the second variable in any plot

Side-by-side box plots

ggplot(
  gerrymander, 
  aes(
    x = trump16, 
    y = gerry
    )
  ) +
  geom_boxplot()

Summary statistics

Density plots

ggplot(
  gerrymander, 
  aes(
    x = trump16, 
    color = gerry
    )
  ) +
  geom_density()

Filled density plots

ggplot(
  gerrymander, 
  aes(
    x = trump16, 
    color = gerry,
    fill = gerry
    )
  ) +
  geom_density()

Better filled density plots

ggplot(
  gerrymander, 
  aes(x = trump16, color = gerry, fill = gerry)
  ) +
  geom_density(alpha = 0.5)

Better colors

ggplot(
  gerrymander, 
  aes(x = trump16, color = gerry, fill = gerry)
  ) +
  geom_density(alpha = 0.5) +
  scale_color_colorblind() +
  scale_fill_colorblind()

Violin plots

ggplot(
  gerrymander, 
  aes(x = trump16, y = gerry, color = gerry)
  ) +
  geom_violin() +
  scale_color_colorblind() +
  scale_fill_colorblind()

Multiple geoms

ggplot(
  gerrymander, 
  aes(x = trump16, y = gerry, color = gerry)
  ) +
  geom_violin() +
  geom_point() +
  scale_color_colorblind() +
  scale_fill_colorblind()

Multiple geoms

ggplot(
  gerrymander, 
  aes(x = trump16, y = gerry, color = gerry)
  ) +
  geom_violin() +
  geom_jitter() +
  scale_color_colorblind() +
  scale_fill_colorblind()

Remove legend

ggplot(
  gerrymander, 
  aes(x = trump16, y = gerry, color = gerry)
  ) +
  geom_violin() +
  geom_jitter() +
  scale_color_colorblind() +
  scale_fill_colorblind() +
  theme(legend.position = "none")

Multivariate analysis

Multivariate analysis

Analyzing the relationship between multiple variables:

  • In general, one variable is identified as the outcome of interest

  • The remaining variables are predictors or explanatory variables

  • Plots for exploring multivariate relationships are the same as those for bivariate relationships, but conditional on one or more variables

    • Conditioning can be done via faceting or aesthetic mappings (e.g., scatterplot of y vs. x1, colored by x2, faceted by x3)

  • Summary statistics for exploring multivariate relationships are the same as those for bivariate relationships, but conditional on one or more variables

    • Conditioning can be done via grouping (e.g., correlation between y and x1, grouped by levels of x2 and x3)

Application exercise

ae-04-gerrymander-explore-I

  • 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-04-gerrymander-explore-I.qmd.

  • Work through the application exercise in class, and render, commit, and push your edits by the end of class.