Data wrangling & visualization I — ggplot2

Week 1

R/RStudio books

R for Data Science
by Hadley Wickham & Garrett Grolemund

ggplot2: Elegant Graphics for Data Analysis
by Hadley Wickham

Data Visualization

this week: we will learn the basic structure of a ggplot2 plot

Data Transformation

next week: we will learn the key verbs (R commands) to:
- select variables
- filter out observations
- create new variables
- compute summaries

Why make plots?

numeric summaries of data are easy to generate
mean, sd, correlation, list of t-tests, etc…
but numerical summaries are just summaries
they can obscure patterns in the underlying data

Why make plots?

numeric summaries of data are easy to generate
mean, sd, correlation, list of t-tests, etc…
but numerical summaries are just summaries
they can obscure patterns in the underlying data

ALWAYS PLOT YOUR DATA ALWAYS PLOT YOUR DATA ALWAYS PLOT YOUR DATA ALWAYS PLOT YOUR DATA ALWAYS PLOT YOUR DATA ALWAYS PLOT YOUR DATA ALWAYS PLOT YOUR DATA ALWAYS PLOT YOUR DATA ALWAYS PLOT YOUR DATA

Anscombe’s Quartet

Datasaurus Dozen

Why make plots using code?

repeatable
extensible
sharable
durable

Prerequisites

only once:

install.packages("tidyverse")

every time you start RStudio:

library(tidyverse)

Sample dataset: mpg

the ggplot2 package (which comes with tidyverse) includes:
- a tibble called mpg
- type ?mpg in the RStudio console to get a help page on the mpg dataset
- type mpg to see the first few rows

mpg

# A tibble: 234 × 11
   manufacturer model      displ  year   cyl trans drv     cty   hwy fl    class
   <chr>        <chr>      <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
 1 audi         a4           1.8  1999     4 auto… f        18    29 p     comp…
 2 audi         a4           1.8  1999     4 manu… f        21    29 p     comp…
 3 audi         a4           2    2008     4 manu… f        20    31 p     comp…
 4 audi         a4           2    2008     4 auto… f        21    30 p     comp…
 5 audi         a4           2.8  1999     6 auto… f        16    26 p     comp…
 6 audi         a4           2.8  1999     6 manu… f        18    26 p     comp…
 7 audi         a4           3.1  2008     6 auto… f        18    27 p     comp…
 8 audi         a4 quattro   1.8  1999     4 manu… 4        18    26 p     comp…
 9 audi         a4 quattro   1.8  1999     4 auto… 4        16    25 p     comp…
10 audi         a4 quattro   2    2008     4 manu… 4        20    28 p     comp…
# ℹ 224 more rows

Sample dataset: mpg

the ggplot2 package (which comes with tidyverse) includes:
- a tibble called mpg
- type nrow(mpg) to count the number of rows
- type ncol(mpg) to count the number of cols

nrow(mpg)

[1] 234

ncol(mpg)

[1] 11

Sample dataset: mpg

you can also see a view of a data frame using glimpse():

glimpse(mpg)

Rows: 234
Columns: 11
$ manufacturer <chr> "audi", "audi", "audi", "audi", "audi", "audi", "audi", "…
$ model        <chr> "a4", "a4", "a4", "a4", "a4", "a4", "a4", "a4 quattro", "…
$ displ        <dbl> 1.8, 1.8, 2.0, 2.0, 2.8, 2.8, 3.1, 1.8, 1.8, 2.0, 2.0, 2.…
$ year         <int> 1999, 1999, 2008, 2008, 1999, 1999, 2008, 1999, 1999, 200…
$ cyl          <int> 4, 4, 4, 4, 6, 6, 6, 4, 4, 4, 4, 6, 6, 6, 6, 6, 6, 8, 8, …
$ trans        <chr> "auto(l5)", "manual(m5)", "manual(m6)", "auto(av)", "auto…
$ drv          <chr> "f", "f", "f", "f", "f", "f", "f", "4", "4", "4", "4", "4…
$ cty          <int> 18, 21, 20, 21, 16, 18, 18, 18, 16, 20, 19, 15, 17, 17, 1…
$ hwy          <int> 29, 29, 31, 30, 26, 26, 27, 26, 25, 28, 27, 25, 25, 25, 2…
$ fl           <chr> "p", "p", "p", "p", "p", "p", "p", "p", "p", "p", "p", "p…
$ class        <chr> "compact", "compact", "compact", "compact", "compact", "c…

Sample dataset: mpg

type View(mpg) to bring up a spreadsheet-like view of the data frame

Q: Do big engines use more fuel?

displ: engine size, in litres
hwy: fuel efficiency (highway), in miles per gallon (mpg)

Creating a `ggplot`

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))

Creating a `ggplot`

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))

ggplot(data = mpg)
- creates a coordinate system you can add layers to
geom_point()
- adds a layer of points to your plot
- mapping = aes(x = displ, y = hwy)
  - tells geom_point to map displ values on to X-axis and hwy values onto Y-axis

Colour-code by another variable

can we explain why the cars shown in red don’t follow the trend?

Colour-code by another variable

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class))

Bigger marker size

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class),
             size = 3)

Size-code by another variable

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class, size = class))

Alpha transparency

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class, size = class),
             alpha = 0.5)

Facets—wrap

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_wrap(~ class, nrow = 2)

Facets—grid

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_grid(drv ~ cyl)

geoms—`geom_point()`

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))

geoms—`geom_smooth()`

ggplot(data = mpg) + 
  geom_smooth(mapping = aes(x = displ, y = hwy))

geoms–both

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  geom_smooth(mapping = aes(x = displ, y = hwy))

Pair mappings to `ggplot()`

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
    geom_point() + 
    geom_smooth()

geoms—colors

ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color=drv)) + 
    geom_point() + 
    geom_smooth()

`geom_smooth()` options

ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color=drv)) + 
    geom_point() + 
    geom_smooth(method="lm")

`geom_smooth()` options

ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color=drv)) + 
    geom_point() + 
    geom_smooth(method="lm", se=FALSE)

`theme` options

ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color=drv)) + 
    geom_point() + 
    geom_smooth(method="lm", se=FALSE) +
    theme_bw()

Statistical Transformations

some graphs (e.g. scatterplots) plot raw values in your dataset
others (bar charts, histograms, boxplots, smoothers) calculate new values
- called a stat for short

ggplot(data = diamonds) +
  geom_bar(mapping = aes(x = cut))

Statistical Transformations

ggplot(data = diamonds) +
  geom_bar(mapping = aes(x = cut))

Statistical Transformations

Barplot with raw values not stat

# A tibble: 5 × 3
  cut           n  mean
  <ord>     <int> <dbl>
1 Fair       1610 4359.
2 Good       4906 3929.
3 Very Good 12082 3982.
4 Premium   13791 4584.
5 Ideal     21551 3458.

ggplot(data = meanprice) +
  geom_bar(mapping = aes(x = cut, y = mean),
           stat = "identity")

Barplot with summary (e.g. mean)

ggplot(diamonds) +
  stat_summary_bin(aes(x = cut, y = price),
                   fun = "mean", geom = "bar")

So many options!

do the readings
play with the code
homeworks will teach you a little bit more
build on simple examples

The layered grammar of graphics

ggplot(data = <DATA>) + 
  <GEOM_FUNCTION>(
     mapping = aes(<MAPPINGS>),
     stat = <STAT>, 
     position = <POSITION>
  ) +
  <COORDINATE_FUNCTION> +
  <FACET_FUNCTION>

you can uniquely describe any plot as a combination of a dataset, a geom, a set of mappings, a stat, a position adjustment, a coordinate system, and a faceting scheme

The layered grammar of graphics

ggplot(data = <DATA>) + 
  <GEOM_FUNCTION>(
     mapping = aes(<MAPPINGS>),
     stat = <STAT>, 
     position = <POSITION>
  ) +
  <COORDINATE_FUNCTION> +
  <FACET_FUNCTION>

with these 7 parameters you can make any plot

you rarely need to supply all seven parameters to make a graph
ggplot2 will provide useful defaults for everything except the data, the mappings, and the geom function

R for Data Science

work through Chapter 3 Data visualisation
read Chapter 4 Workflow: basics
next week:
- Chapter 5 Data transformation
- Chapter 6 Workflow: scripts
- Chapter 7 Exploratory Data Analysis

`ggplot`—play around!

ggplot is simple to get started with
is as complex as you want it to be
designed as a “grammar” of graphics—systematic
many ways of doing the same thing

`ggplot`—play around!

ggplot2 cheatsheet

R Graph Gallery

`ggplot`—Homework 1

some answers are directly in the lecture slides
some answers are directly in the readings
some answers require you to apply what you have learned
TAs are there to help guide you
- (but not to literally give you the code)

ALWAYS PLOT YOUR DATA

Data wrangling & visualization I — ggplot2

R/RStudio books

Data Visualization

Data Transformation

Why make plots?

Why make plots?

Anscombe’s Quartet

Datasaurus Dozen

Datasaurus Dozen

Why make plots using code?

Prerequisites

Sample dataset: mpg

Sample dataset: mpg

Sample dataset: mpg

Sample dataset: mpg

Q: Do big engines use more fuel?

Creating a ggplot

Creating a ggplot

Creating a ggplot

Colour-code by another variable

Colour-code by another variable

Bigger marker size

Size-code by another variable

Alpha transparency

Facets—wrap

Facets—grid

Facets: wrap vs grid

geoms—geom_point()

geoms—geom_smooth()

geoms–both

Pair mappings to ggplot()

geoms—colors

geom_smooth() options

geom_smooth() options

theme options

Statistical Transformations

Statistical Transformations

Statistical Transformations

Barplot with raw values not stat

Barplot with summary (e.g. mean)

So many options!

The layered grammar of graphics

The layered grammar of graphics

R for Data Science

ggplot—play around!

ggplot—play around!

ggplot—Homework 1

ALWAYS PLOT YOUR DATA

Creating a `ggplot`

Creating a `ggplot`

Creating a `ggplot`

geoms—`geom_point()`

geoms—`geom_smooth()`

Pair mappings to `ggplot()`

`geom_smooth()` options

`geom_smooth()` options

`theme` options

`ggplot`—play around!

`ggplot`—play around!

`ggplot`—Homework 1