Lecture 2: EDA; aesthetic mappings

suppressPackageStartupMessages(library(tidyverse))
library(gapminder)
knitr::opts_chunk$set(fig.width=5, fig.height=3, fig.align="center")

Learning Objectives

Here is what we expect you to know, corresponding to today’s lecture:

• Draw conclusions from a graph regarding the effect and confidence
• Identify the grammar components from a graph
• Identify the graph from grammar components
• Identify an appropriate graph given the variable types to be depicted
  • There’s more room for flexibility when there are 3+ data variables

You are not expected to memorize the names of the plot types for your quizzes.

Review

Last time, we mostly looked at:

1. Components: what is a statistical graphic (or “graph”)?
   • Defined by the grammar of graphics
2. Tooling: how to make a plot (with ggplot2)
   • Use case: appropriate plots given the types of variables to be plotted.

Elaborate:

• What are the grammar components?
  • Correction from last time: aesthetic vs aesthetic mapping.
• What are the appropriate plots for…
  • (First of all, what are the two main types of variables? )
  • 2 numeric variables?
  • 1 numeric variable?
  • 1 numeric, 1 categorical variable?

We’ve also been getting set up for the other three main elements of this course

(we can think of these as a forming a flowchart: effective choice -> components + theme -> tooling -> EDA)

3. Theming: the look/decorations on a plot.
   • Lecture 3 (in part)
4. Exploratory Data Analysis (EDA)
   • Elaboration today (soon).
5. Plotting Effectiveness
   • Chiefly Lecture 5
   • Plotting principal alluded to so far: show me the data!
   • Example: pinhead plots vs. violin+jitter plots.

ggplot(gapminder, aes(continent, lifeExp)) +
    geom_violin() +
    geom_jitter(width=0.2, alpha=0.2) +
    ggtitle("Jitter + Violin Plot")

gapminder %>% 
    group_by(continent) %>% 
    summarize(sd = sd(lifeExp),
              mean_life_exp = mean(lifeExp)) %>%
    ggplot(aes(continent)) +
    geom_errorbar(aes(ymax = mean_life_exp + sd,
                      ymin = mean_life_exp - sd),
                  width=0.2) +
    geom_col(aes(y=mean_life_exp)) +
    ggtitle("Pinhead plot") +
    ylab("lifeExp")

Today

• EDA
• Continue 1- and 2-variable plot types.
• 3+ variable plot types (through more aesthetics, and facetting).

We’ll fill out the lec2-worksheet.Rmd worksheet for the latter two.

EDA

Intro

What is exploratory data analysis (EDA)? (What is Data Science?) There’s no one definition. Generally:

• Answering questions through data exploration; as opposed to making model assumptions, model fitting, or hypothesis tests.
• Allows for us to answer questions qualitatively (not numerically). Questions are typically less specific and more “human”:
  • “Has life expectancy been increasing over time?”
  • vs. specifying mean
• Usually the first part of an analysis.

When reading a plot, there are generally two main components to consider:

• Effect (example: is there a relationship/trend?)
• Confidence (in the effect)

(Link to 552: estimate + uncertainty)

Examples of EDA

Which species has the largest Sepal Width? How certain are you? (Did I need a hypothesis test for this? This is not to say that hypothesis tests are not useful)

iris %>% 
    ggplot(aes(Species, Sepal.Width)) +
    geom_violin() +
    geom_jitter(width=0.2)

Which continent will have the highest life expectancy in 2020? Will a model help you here?

gapminder %>% 
    group_by(continent, year) %>% 
    summarize(mean_life_exp = mean(lifeExp)) %>% 
    ggplot(aes(year, mean_life_exp)) +
    geom_point() +
    geom_line(aes(colour=continent, group=continent))

Is there a relationship between GDP per capita and life expectancy? Describe the dependence. How confident are you?

ggplot(gapminder, aes(gdpPercap, lifeExp)) +
    geom_point(alpha=0.2) +
    scale_x_log10()

How certain are you with this one?

set.seed(10)
gapminder %>% 
    sample_n(10) %>% 
    ggplot(aes(gdpPercap, lifeExp)) +
    geom_point() +
    scale_x_log10()

Is there a relationship between the sepal and petal lengths of the setosa plant? How confident are you?

iris %>% 
    filter(Species == "setosa") %>% 
    ggplot(aes(Petal.Length, Sepal.Length)) +
    geom_jitter() +
    ggtitle("Species: Setosa")

Poll:

/poll Dependence strong weak none

/poll "Confidence" "highly confident", "somewhat confident", "little confidence", "no confidence"