Summary stats + plots

Lecture 4

Dr. Elijah Meyer

NC State University
ST 295 - Spring 2025

2025-01-16

Checklist

– Did you read the prepare material?

– Have you accepted your GitHub organization invite?

– Do you have access to this page?

    > If so, please bookmark it! You will visit this page very often throughout the semester

– (Try it!) Clone your repository for today’s class

  > If you do not see it, please come talk to me. 
  > We will demonstrate how to do this as a class as well.

Announcements

– Quiz-1 released today on Moodle at 12:00pm (due Tuesday before class)

> Largly multiple choice
> One attempt 
> Located on Moodle

– Homework-1 will come out next week

Announcements

Solutions to last AE is live! See the website.

Warm-up

Read this code as a sentence

library(tidyverse)

mtcars |>
  glimpse()

Warm-up

What are these called? What do these do?

#| echo: false

#| eval: false

#| message: false

Practice

We are going to practice making summary statistics! Clone the AE for today’s class.

New functions

– group_by()

– summarise()

– n()

– mean(); median(); sd() …etc.

In summary

– We use the pipe operator when we are writing a sequence of actions

– group_by() groups our data and allows us to create summary statistics on the grouped data

– summarise() allows us to calculate summary statistics!

Plots

What types of plots can we make?

Golden Rule We let the type of variable(s) dictate the appropriate plot

  • Quantitative

  • Categorical

Pick a plot

What plot is appropriate to graph the following scenarios

– One quantitative variable

– One quantitative variable; one categorical variable

– Two quantitative variables

– One categorical variable

– Two categorical variables

– Scatter plot

– Histogram

– Bar plot

– Segmented bar plot

– Box plot

Scatter plot

Two quantitative variables

Histogram

One quantitative variable

Bar plot

One categorical variable

Segmented bar plot

Two categorical variables

Boxplot

One quantitative; One categorical

How do we make graphs?

The process

mtcars

You want to create a visualization. The first thing we need to do is set up the canvas…

The process

    mtcars |>
        ggplot()

The process

    mtcars |>
        ggplot(
        aes(
             x = variable.name, y = variable.name)
               )

aes: describe how variables in the data are mapped to your canvas

The process

+ “and”

When working with ggplot functions, we will add to our canvus using +

The process

    mtcars |>
        ggplot(
        aes(
             x = variable.name, y = variable.name)
               ) +
        geom_point()

The process

AE

Recap of AE

– Construct plots with ggplot().

– Layers of ggplots are separated by +s.

– Aesthetic attributes of a geometries (color, size, transparency, etc.) can be mapped to variables in the data or set by the user.

– Use facet_wrap() when faceting (creating small multiples) by one variable and facet_grid() when faceting by two variables.

🔗 class website