---
title: "Lec 09 - Visualization with ggplot2"
subtitle: "Lecture 09"
author: "Dr. Colin Rundel"
footer: "Sta 523 - Fall 2022"
format:
revealjs:
theme: slides.scss
transition: fade
slide-number: true
self-contained: true
execute:
echo: true
---
```{r setup, message=FALSE, warning=FALSE, include=FALSE}
options(
htmltools.dir.version = FALSE, # for blogdown
width=80
)
knitr::opts_chunk$set(
fig.align = "center", fig.retina = 2, dpi = 150,
out.width = "100%"
)
library(tidyverse)
library(palmerpenguins)
```
#
{fig-align="center" width="40%"}
## The Grammar of Graphics
- Visualization concept created by Leland Wilkinson (The Grammar of Graphics, 1999)
- attempt to taxonimize the basic elements of statistical graphics
- Adapted for R by Hadley Wickham (2009)
- consistent and compact syntax to describe statistical graphics
- highly modular as it breaks up graphs into semantic components
- ggplot2 is not meant as a guide to which graph to use and how to best convey your data (more on that later), but it does have some strong opinions.
## Terminology
A statistical graphic is a...
- mapping of **data**
- which may be **statistically transformed** (summarized, log-transformed, etc.)
- to **aesthetic attributes** (color, size, xy-position, etc.)
- using **geometric objects** (points, lines, bars, etc.)
- and mapped onto a specific **facet** and **coordinate system**
## Anatomy of a ggplot call
```r
ggplot(
data = [dataframe],
mapping = aes(
x = [var x], y = [var y],
color = [var color],
shape = [var shape],
...
)
) +
geom_[some geom](
mapping = aes(
color = [var geom color],
...
)
) +
... # other geometries
scale_[some axis]_[some scale]() +
facet_[some facet]([formula]) +
... # other options
```
## Data - Palmer Penguins
Measurements for penguin species, island in Palmer Archipelago, size (flipper length, body mass, bill dimensions), and sex.
:::: {.columns}
::: {.column width='33%'}
{fig-align="center" width="100%"}
:::
::: {.column width='66%' .small}
```{r echo=FALSE}
options(width = 60)
```
```{r}
library(palmerpenguins)
penguins
```
```{r echo=FALSE}
options(width = 65)
```
:::
::::
## A basic ggplot
:::: {.columns .small}
::: {.column width='50%'}
```{r penguins, fig.show = "hide"}
ggplot(
data = penguins,
mapping = aes(
x = bill_depth_mm,
y = bill_length_mm
)
) +
geom_point() +
labs(
title = "Bill depth and length",
subtitle = paste(
"Dimensions for Adelie, Chinstrap,",
"and Gentoo Penguins"
),
x = "Bill depth (mm)",
y = "Bill length (mm)",
color = "Species"
)
```
:::
::: {.column width='50%'}
```{r ref.label = "penguins", echo = FALSE, warning = FALSE, out.width = "100%", fig.width = 8}
```
:::
::::
# Text <-> Plot
##
::: {.small}
> *Start with the `penguins` data frame*
:::
:::: {.columns}
::: {.column width='50%'}
```{r penguins-0, fig.show = "hide", warning = FALSE}
#| code-line-numbers: "1"
ggplot(data = penguins)
```
:::
::: {.column width='50%'}
```{r ref.label = "penguins-0", echo = FALSE, warning = FALSE, fig.width = 8}
```
:::
::::
##
::: {.small}
> Start with the `penguins` data frame,
> *map bill depth to the x-axis*
:::
:::: {.columns .small}
::: {.column width='50%'}
```{r penguins-1, fig.show = "hide", warning = FALSE}\
#| code-line-numbers: "2"
ggplot(
data = penguins,
mapping = aes(x = bill_depth_mm)
)
```
:::
::: {.column width='50%'}
```{r ref.label = "penguins-1", echo = FALSE, warning = FALSE, fig.width = 8}
```
:::
::::
##
::: {.small}
> Start with the `penguins` data frame,
> map bill depth to the x-axis
> *and map bill length to the y-axis.*
:::
:::: {.columns .small}
::: {.column width='50%'}
```{r penguins-2, fig.show = "hide", warning = FALSE}
#| code-line-numbers: "5"
ggplot(
data = penguins,
mapping = aes(
x = bill_depth_mm,
y = bill_length_mm
)
)
```
:::
::: {.column width='50%'}
```{r ref.label = "penguins-2", echo = FALSE, warning = FALSE, fig.width = 8}
```
:::
::::
##
::: {.small}
> Start with the `penguins` data frame,
> map bill depth to the x-axis
> and map bill length to the y-axis.
> *Represent each observation with a point*
:::
:::: {.columns .small}
::: {.column width='50%'}
```{r penguins-3, fig.show = "hide", warning = FALSE}
#| code-line-numbers: "8"
ggplot(
data = penguins,
mapping = aes(
x = bill_depth_mm,
y = bill_length_mm
)
) +
geom_point()
```
:::
::: {.column width='50%'}
```{r ref.label = "penguins-3", echo = FALSE, warning = FALSE, fig.width = 8}
```
:::
::::
##
::: {.small}
> Start with the `penguins` data frame,
> map bill depth to the x-axis
> and map bill length to the y-axis.
> Represent each observation with a point
> *and map species to the color of each point.*
:::
:::: {.columns .small}
::: {.column width='50%'}
```{r penguins-4, fig.show = "hide", warning = FALSE}
#| code-line-numbers: "10"
ggplot(
data = penguins,
mapping = aes(
x = bill_depth_mm,
y = bill_length_mm
)
) +
geom_point(
mapping = aes(color = species)
)
```
:::
::: {.column width='50%'}
```{r ref.label = "penguins-4", echo = FALSE, warning = FALSE, fig.width = 8}
```
:::
::::
##
::: {.small}
> Start with the `penguins` data frame,
> map bill depth to the x-axis
> and map bill length to the y-axis.
> Represent each observation with a point
> and map species to the color of each point.
> *Title the plot "Bill depth and length"*
:::
:::: {.columns .small}
::: {.column width='50%'}
```{r penguins-5, fig.show = "hide", warning = FALSE}
#| code-line-numbers: "11"
ggplot(
data = penguins,
mapping = aes(
x = bill_depth_mm,
y = bill_length_mm
)
) +
geom_point(
mapping = aes(color = species)
) +
labs(title = "Bill depth and length")
```
:::
::: {.column width='50%'}
```{r ref.label = "penguins-5", echo = FALSE, warning = FALSE, fig.width = 8}
```
:::
::::
##
::: {.small}
> Start with the `penguins` data frame,
> map bill depth to the x-axis
> and map bill length to the y-axis.
> Represent each observation with a point
> and map species to the color of each point.
> Title the plot "Bill depth and length",
> *add the subtitle "Dimensions for Adelie, Chinstrap, and Gentoo Penguins"*
:::
:::: {.columns .small}
::: {.column width='50%'}
```{r penguins-6, fig.show = "hide", warning = FALSE}
#| code-line-numbers: "13-15"
ggplot(
data = penguins,
mapping = aes(
x = bill_depth_mm,
y = bill_length_mm
)
) +
geom_point(
mapping = aes(color = species)
) +
labs(
title = "Bill depth and length",
subtitle = paste("Dimensions for Adelie,",
"Chinstrap, and Gentoo",
"Penguins")
)
```
:::
::: {.column width='50%'}
```{r ref.label = "penguins-6", echo = FALSE, warning = FALSE, fig.width = 8}
```
:::
::::
##
::: {.small}
> Start with the `penguins` data frame,
> map bill depth to the x-axis
> and map bill length to the y-axis.
> Represent each observation with a point
> and map species to the color of each point.
> Title the plot "Bill depth and length",
> add the subtitle "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
> *label the x and y axes as "Bill depth (mm)" and "Bill length (mm)", respectively*
:::
:::: {.columns .small}
::: {.column width='50%'}
```{r penguins-7, fig.show = "hide", warning = FALSE}
#| code-line-numbers: "16-17"
ggplot(
data = penguins,
mapping = aes(
x = bill_depth_mm,
y = bill_length_mm
)
) +
geom_point(
mapping = aes(color = species)
) +
labs(
title = "Bill depth and length",
subtitle = paste("Dimensions for Adelie,",
"Chinstrap, and Gentoo",
"Penguins"),
x = "Bill depth (mm)",
y = "Bill length (mm)"
)
```
:::
::: {.column width='50%'}
```{r ref.label = "penguins-7", echo = FALSE, warning = FALSE, fig.width = 8}
```
:::
::::
##
::: {.small}
> Start with the `penguins` data frame,
> map bill depth to the x-axis
> and map bill length to the y-axis.
> Represent each observation with a point
> and map species to the color of each point.
> Title the plot "Bill depth and length",
> add the subtitle "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
> label the x and y axes as "Bill depth (mm)" and "Bill length (mm)", respectively,
> *label the legend "Species"*
:::
:::: {.columns .small}
::: {.column width='50%'}
```{r penguins-8, fig.show = "hide", warning = FALSE}
#| code-line-numbers: "18"
ggplot(
data = penguins,
mapping = aes(
x = bill_depth_mm,
y = bill_length_mm
)
) +
geom_point(
mapping = aes(color = species)
) +
labs(
title = "Bill depth and length",
subtitle = paste("Dimensions for Adelie,",
"Chinstrap, and Gentoo",
"Penguins"),
x = "Bill depth (mm)",
y = "Bill length (mm)",
color = "Species"
)
```
:::
::: {.column width='50%'}
```{r ref.label = "penguins-8", echo = FALSE, warning = FALSE, fig.width = 8}
```
:::
::::
##
::: {.small}
> Start with the `penguins` data frame,
> map bill depth to the x-axis
> and map bill length to the y-axis.
> Represent each observation with a point
> and map species to the color of each point.
> Title the plot "Bill depth and length",
> add the subtitle "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
> label the x and y axes as "Bill depth (mm)" and "Bill length (mm)", respectively,
> label the legend "Species",
> *and add a caption for the data source.*
:::
:::: {.columns .small}
::: {.column width='50%'}
```{r penguins-9, fig.show = "hide", warning = FALSE}
#| code-line-numbers: "19"
ggplot(
data = penguins,
mapping = aes(
x = bill_depth_mm,
y = bill_length_mm
)
) +
geom_point(
mapping = aes(color = species)
) +
labs(
title = "Bill depth and length",
subtitle = paste("Dimensions for Adelie,",
"Chinstrap, and Gentoo",
"Penguins"),
x = "Bill depth (mm)",
y = "Bill length (mm)",
color = "Species",
caption = "Source: palmerpenguins package"
)
```
:::
::: {.column width='50%'}
```{r ref.label = "penguins-9", echo = FALSE, warning = FALSE, fig.width = 8}
```
:::
::::
##
::: {.small}
> Start with the `penguins` data frame,
> map bill depth to the x-axis
> and map bill length to the y-axis.
> Represent each observation with a point
> and map species to the color of each point.
> Title the plot "Bill depth and length",
> add the subtitle "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
> label the x and y axes as "Bill depth (mm)" and "Bill length (mm)", respectively,
> label the legend "Species",
> and add a caption for the data source.
> *Finally, use the viridis color palete for all points.*
:::
:::: {.columns .small}
::: {.column width='50%'}
```{r penguins-10, fig.show = "hide", warning = FALSE}
#| code-line-numbers: "21"
ggplot(
data = penguins,
mapping = aes(
x = bill_depth_mm,
y = bill_length_mm
)
) +
geom_point(
mapping = aes(color = species)
) +
labs(
title = "Bill depth and length",
subtitle = paste("Dimensions for Adelie,",
"Chinstrap, and Gentoo",
"Penguins"),
x = "Bill depth (mm)",
y = "Bill length (mm)",
color = "Species",
caption = "Source: palmerpenguins package"
) +
scale_color_viridis_d()
```
:::
::: {.column width='50%'}
```{r ref.label = "penguins-10", echo = FALSE, warning = FALSE, fig.width = 8}
```
:::
::::
## Argument names
Often we omit the names of first two arguments when building plots with `ggplot()`.
:::: {.columns}
::: {.column width='50%'}
```{r named-args, eval = FALSE}
ggplot(
data = penguins,
mapping = aes(
x = bill_depth_mm,
y = bill_length_mm
)
) +
geom_point(
mapping = aes(color = species)
) +
scale_color_viridis_d()
```
:::
::: {.column width='50%'}
```{r not-named-args, eval = FALSE}
ggplot(
penguins,
aes(
x = bill_depth_mm,
y = bill_length_mm
)
) +
geom_point(
aes(color = species)
) +
scale_color_viridis_d()
```
:::
::::
::: {.aside}
*Note* that `ggplot` and `geom_*` swap the order of the `data` and `mapping` arguments.
:::
# Aesthetics
## Aesthetics options
Commonly used characteristics of plotting geometries that can be **mapped to a specific variable** in the data, examples include:
- position (`x`, `y`)
- `color`
- `shape`
- `size`
- `alpha` (transparency)
Different geometries have different aesthetics that can be used - see the [ggplot2 geoms](https://ggplot2.tidyverse.org/reference/index.html#Geoms) help files for listings.
* Aesthetics given in `ggplot()` apply to all `geom`s.
* Aesthetics for a specific `geom_*()` can be overridden via `mapping` or as an argument.
## color
:::: {.columns .medium}
::: {.column width='35%'}
```{r color, fig.show = "hide", warning = FALSE}
ggplot(
penguins,
aes(
x = bill_depth_mm,
y = bill_length_mm
)
) +
geom_point(
aes(color = species)
)
```
:::
::: {.column width='65%'}
```{r ref.label = "color", echo = FALSE, warning = FALSE}
```
:::
::::
## Shape
Mapped to a different variable than `color`
:::: {.columns .medium}
::: {.column width='35%'}
```{r shape-island, fig.show = "hide", warning = FALSE}
ggplot(
penguins,
aes(
x = bill_depth_mm,
y = bill_length_mm
)
) +
geom_point(
aes(
color = species,
shape = island
)
)
```
:::
::: {.column width='65%'}
```{r ref.label = "shape-island", echo = FALSE, warning = FALSE}
```
:::
::::
## Shape
Mapped to same variable as `color`
:::: {.columns .medium}
::: {.column width='35%'}
```{r shape-species, fig.show = "hide", warning = FALSE}
ggplot(
penguins,
aes(
x = bill_depth_mm,
y = bill_length_mm
)
) +
geom_point(
aes(
color = species,
shape = species
)
)
```
:::
::: {.column width='65%'}
```{r ref.label = "shape-species", echo = FALSE, warning = FALSE}
```
:::
::::
## Size
Using a fixed value (note this value is outside of the `aes` call)
:::: {.columns .medium}
::: {.column width='35%'}
```{r size1, fig.show = "hide", warning = FALSE}
ggplot(
penguins,
aes(
x = bill_depth_mm,
y = bill_length_mm
)
) +
geom_point(
aes(
color = species,
shape = species
),
size = 3
)
```
:::
::: {.column width='65%'}
```{r ref.label = "size1", echo = FALSE, warning = FALSE}
```
:::
::::
## Size
Mapped to a variable
:::: {.columns .medium}
::: {.column width='35%'}
```{r size2, fig.show = "hide", warning = FALSE}
ggplot(
penguins,
aes(
x = bill_depth_mm,
y = bill_length_mm
)
) +
geom_point(
aes(
color = species,
shape = species,
size = body_mass_g
),
)
```
:::
::: {.column width='65%'}
```{r ref.label = "size2", echo = FALSE, warning = FALSE}
```
:::
::::
## Alpha
:::: {.columns .medium}
::: {.column width='35%'}
```{r alpha, fig.show = "hide", warning = FALSE}
ggplot(
penguins,
aes(
x = bill_depth_mm,
y = bill_length_mm
)
) +
geom_point(
aes(
color = species,
shape = species,
alpha = body_mass_g
),
size = 3
)
```
:::
::: {.column width='65%'}
```{r ref.label = "alpha", echo = FALSE, warning = FALSE}
```
:::
::::
## Mapping vs settings
- **Mapping** - Determine an aesthetic (the size, alpha, etc.) of a geom based on the values of a variable in the data
- wrapped by `aes()` and pass as `mapping` argument to `ggplot()` or `geom_*()`.
- **Setting** - Determine an aesthetic (the size, alpha, etc.) of a geom **not** based on the values of a variable in the data, usually a constant value.
- passed directly into `geom_*()` as an argument.
. . .
From the previous slide `color`, `shape`, and `alpha` are all **aesthetics** while `size` was a **setting**.
# Faceting
## Faceting
- Smaller plots that display different subsets of the data
- Useful for exploring conditional relationships and large data
- Sometimes referred to as "small multiples"
## facet_grid
:::: {.columns .medium}
::: {.column width='35%'}
```{r facet, warning = FALSE, fig.show = "hide"}
ggplot(
penguins,
aes(
x = bill_depth_mm,
y = bill_length_mm
)
) +
geom_point() +
facet_grid(
species ~ island
)
```
:::
::: {.column width='65%'}
```{r ref.label = "facet", echo = FALSE, warning = FALSE}
```
:::
::::
## Compare with ...
:::: {.columns .medium}
::: {.column width='35%'}
```{r facet_comp, warning = FALSE, fig.show = "hide"}
ggplot(
penguins,
aes(
x = bill_depth_mm,
y = bill_length_mm
)
) +
geom_point(
aes(
color = species,
shape = island
),
size = 3
)
```
:::
::: {.column width='65%'}
```{r ref.label = "facet_comp", echo = FALSE, warning = FALSE}
```
:::
::::
## facet_grid (cols)
:::: {.columns .medium}
::: {.column width='35%'}
```{r facet2, warning = FALSE, fig.show = "hide"}
ggplot(
penguins,
aes(
x = bill_depth_mm,
y = bill_length_mm
)
) +
geom_point() +
facet_grid(
~ species
)
```
:::
::: {.column width='65%'}
```{r ref.label = "facet2", echo = FALSE, warning = FALSE}
```
:::
::::
## facet_grid (rows)
:::: {.columns .medium}
::: {.column width='35%'}
```{r facet3, warning = FALSE, fig.show = "hide"}
ggplot(
penguins,
aes(
x = bill_depth_mm,
y = bill_length_mm
)
) +
geom_point() +
facet_grid(
species ~ .
)
```
:::
::: {.column width='65%'}
```{r ref.label = "facet3", echo = FALSE, warning = FALSE}
```
:::
::::
## facet_wrap
:::: {.columns .medium}
::: {.column width='35%'}
```{r facetwrap, warning = FALSE, fig.show = "hide"}
ggplot(
penguins,
aes(
x = bill_depth_mm,
y = bill_length_mm
)
) +
geom_point() +
facet_wrap(
~ species
)
```
:::
::: {.column width='65%'}
```{r ref.label = "facetwrap", echo = FALSE, warning = FALSE}
```
:::
::::
## facet_wrap
:::: {.columns .medium}
::: {.column width='35%'}
```{r facetwrap2, warning = FALSE, fig.show = "hide"}
ggplot(
penguins,
aes(
x = bill_depth_mm,
y = bill_length_mm
)
) +
geom_point() +
facet_wrap(
~ species, ncol = 2
)
```
:::
::: {.column width='65%'}
```{r ref.label = "facetwrap2", echo = FALSE, warning = FALSE}
```
:::
::::
## Faceting and color
:::: {.columns .medium}
::: {.column width='35%'}
```{r facet-color-legend, fig.show = "hide", warning = FALSE}
ggplot(
penguins,
aes(
x = bill_depth_mm,
y = bill_length_mm,
color = species
)
) +
geom_point() +
facet_grid(
species ~ sex
)
```
:::
::: {.column width='65%'}
```{r ref.label = "facet-color-legend", echo = FALSE, warning = FALSE}
```
:::
::::
## Hiding legend elements
:::: {.columns .medium}
::: {.column width='35%'}
```{r facet-color-no-legend, fig.show = "hide", warning = FALSE}
ggplot(
penguins,
aes(
x = bill_depth_mm,
y = bill_length_mm,
color = species
)
) +
geom_point() +
facet_grid(
species ~ sex
) +
guides(color = "none")
```
:::
::: {.column width='65%'}
```{r ref.label = "facet-color-no-legend", echo = FALSE, warning = FALSE}
```
:::
::::
# A brief plot tour
of ggplot2 plots
```{r}
#| include: false
knitr::opts_chunk$set(out.width="75%")
```
## Histograms
::: {.medium}
```{r hist1, warning = FALSE}
ggplot(
penguins,
aes(x = body_mass_g)
) +
geom_histogram()
```
:::
## Histograms - bins
::: {.medium}
```{r hist3, warning = FALSE}
ggplot(
penguins,
aes( x = body_mass_g )
) +
geom_histogram(bins = 50)
```
:::
## Histograms - binwidth
::: {.medium}
```{r hist2, warning = FALSE}
ggplot(
penguins,
aes( x = body_mass_g )
) +
geom_histogram(binwidth = 250)
```
:::
## Histograms - color
::: {.medium}
```{r hist5, warning = FALSE}
ggplot(
penguins,
aes(x = body_mass_g,
color = species )
) +
geom_histogram(bins = 20)
```
:::
## Histograms - fill
::: {.medium}
```{r hist4, warning = FALSE}
ggplot(
penguins,
aes(x = body_mass_g,
fill = species )
) +
geom_histogram(bins = 20)
```
:::
## Histograms - position
::: {.medium}
```{r hist_pos, warning = FALSE}
#| out-width: 66%
ggplot(
penguins,
aes(x = body_mass_g,
fill = species )
) +
geom_histogram(
bins = 20, alpha = 0.5,
position = "identity"
)
```
:::
## Histograms - facets
::: {.medium}
```{r hist6, warning = FALSE}
#| out-width: 66%
ggplot(
penguins,
aes(x = body_mass_g,
fill = species )
) +
geom_histogram(bins = 20) +
facet_grid(species ~ .) +
guides(fill = "none")
```
:::
## Density plot
::: {.medium}
```{r dens1, warning = FALSE}
ggplot(
penguins,
aes(x = body_mass_g)
) +
geom_density()
```
:::
## Density plot - fill
::: {.medium}
```{r dens3, warning = FALSE}
ggplot(
penguins,
aes(x = body_mass_g,
fill = species )
) +
geom_density(alpha = 0.25)
```
:::
## Density plot - adjust
::: {.medium}
```{r dens2, warning = FALSE}
#| out-width: 66%
ggplot(
penguins,
aes(x = body_mass_g,
fill = species )
) +
geom_density(
adjust = 0.5, alpha = 0.25
)
```
:::
## Violin plot
::: {.medium}
```{r violin, warning = FALSE}
#| out-width: 66%
ggplot(penguins,
aes(x = species, fill = species,
y = body_mass_g )
) +
geom_violin()
```
:::
## Ridge plot
::: {.medium}
```{r ridge, warning = FALSE}
ggplot(
penguins,
aes(x = body_mass_g,
y = species, fill = species )
) +
ggridges::geom_density_ridges(alpha = 0.5)
```
:::
## Ridge plot - more categories + dplyr
::: {.medium}
```{r ridge2, warning = FALSE}
#| output-location: "slide"
#| out-width: 100%
penguins %>%
mutate(
species_sex = paste0(
species, " (", sex, ")"
)
) %>%
ggplot(
aes(
x = body_mass_g,
y = species_sex,
fill = species
)
) +
ggridges::geom_density_ridges(
alpha = 0.5
)
```
:::
## Box plot
::: {.medium}
```{r box1, warning = FALSE}
ggplot(
penguins,
aes(x = body_mass_g,
y = species )
) +
geom_boxplot()
```
:::
## Box plot - coord_flip
::: {.medium}
```{r box2, warning = FALSE}
ggplot(
penguins,
aes(x = body_mass_g,
y = species )
) +
geom_boxplot() +
coord_flip()
```
:::
## Box plot - swap coords
::: {.medium}
```{r box3, warning = FALSE}
ggplot(
penguins,
aes(x = species,
y = body_mass_g )
) +
geom_boxplot()
```
:::
## Scatter plot
::: {.medium}
```{r scatter1, warning = FALSE}
ggplot(
penguins,
aes(x = bill_depth_mm,
y = bill_length_mm,
color = species )
) +
geom_point()
```
:::
## Scatter plot - geom_smooth
::: {.medium}
```{r scatter3, warning = FALSE}
ggplot(
penguins,
aes(x = bill_depth_mm,
y = bill_length_mm,
color = species )
) +
geom_point() +
geom_smooth( fullrange = TRUE )
```
:::
## Scatter plot - geom_smooth w/ lm
::: {.medium}
```{r scatter2, warning = FALSE}
#| output-location: "slide"
#| out-width: 100%
ggplot(
penguins,
aes(
x = bill_depth_mm,
y = bill_length_mm,
color = species
)
) +
geom_point() +
geom_smooth(
method = "lm",
se = FALSE,
fullrange = TRUE
)
```
:::
## Line plot
::: {.medium}
```{r line, warning = FALSE}
#| out-width: 75%
penguins %>%
count(species, year) %>%
ggplot(
aes(x = year, y = n,
color = species, group = species )
) +
geom_line()
```
:::
## Line plot - with points
::: {.medium}
```{r line2, warning = FALSE}
#| out-width: 75%
penguins %>%
count(species, year) %>%
ggplot(
aes(x = year, y = n,
color = species, group = species )
) +
geom_line() +
geom_point()
```
:::
## Bar plot
::: {.medium}
```{r bar1, warning = FALSE}
ggplot(
penguins,
aes( x = species )
) +
geom_bar()
```
:::
## Stacked bar plot
::: {.medium}
```{r bar2, warning = FALSE}
ggplot(
penguins,
aes( x = species,
fill = island )
) +
geom_bar()
```
:::
## Stacked relative frequency bar plot
::: {.medium}
```{r bar3, warning = FALSE}
ggplot(
penguins,
aes( x = species,
fill = island )
) +
geom_bar(position = "fill")
```
:::
## Dodged bar plot
::: {.medium}
```{r bar4, warning = FALSE}
ggplot(
penguins,
aes( x = species,
fill = sex )
) +
geom_bar(position = "dodge")
```
:::
# Exercises
## Exercise 1
Recreate, as faithfully as possible, the following plot using ggplot2 and the `penguins` data.
```{r echo = FALSE, out.width = "60%", fig.height = 5}
penguins %>%
filter(!is.na(sex)) %>%
ggplot(
aes(
x = body_mass_g,
fill = species
)
) +
geom_density(alpha = 0.5, color = NA) +
facet_wrap(~sex, nrow = 2) +
labs(
x = "Body Mass (g)",
y = "",
fill = "Species"
)
```
## Exercise 2
Recreate, as faithfully as possible, the following plot from the `palmerpenguin` package readme in ggplot2.
```{r echo = FALSE, out.width="55%"}
knitr::include_graphics("imgs/palmer_plot_ex2.png")
```
::: {.aside}
See the `palmerpenguins` pkgdown [site](https://allisonhorst.github.io/palmerpenguins/)
:::