Introduction to ggplot2

# Introduction to ggplot2
### Derek Ogle, May 2020

---

# Today's Goal

Build upon the basics of ggplot2 that we developed last time by adding more controls over elements of the graph.

---

# Background

- Estimated counts of Gray Wolf (*Canis lupus*) and Moose (*Alces alces*) from Isle Royale.

.center[
<img src="https://derekogle.com/NCGraphing/modules/zimgs/wolves_moose.jpg" width="500px" />
Credit: <a href="https://www.mtu.edu/news/stories/2012/march/wolf-population-isle-royale-drops-lowest-has-ever-been.html">Michigan Technological University</a>
]

---

# Background

- Downloaded 'Data_wolves_moose_Isle_Royale_June2019.xlsx' Excel file from [Wolves & Moose of Isle Royale](https://isleroyalewolf.org/data/data/home.html), deleted first row of the first sheet, and saved the first sheet as [this CSV file](https://raw.githubusercontent.com/droglenc/NCData/master/WolvesMoose_IsleRoyale_June2019.csv).

&nbsp;

--
- Loaded the data into the `irmw` object.

```r
## #!# Set to your own working directory and have just your filename below.
irmw <- read.csv("https://raw.githubusercontent.com/droglenc/NCData/master/WolvesMoose_IsleRoyale_June2019.csv",
 na.strings=c("NA","N/A"))
```

---

# Background

- Selected only five variables.
- Renamed two variables with space in names (and long).
- Changed categorical data from numbers to words and converted to a factor.

```r
irmw %<>%
 select(year,wolves,moose,Jan.Feb..temp..F.,ice.bridges..0.none..1...present.) %>%
 rename(winter_temp=Jan.Feb..temp..F.,
 ice_bridges=ice.bridges..0.none..1...present.) %>%
 mutate(ice_bridges=plyr::mapvalues(ice_bridges,from=c(0,1),to=c("no","yes")),
 ice_bridges=factor(ice_bridges))
```

---

# Background

```r
head(irmw)
```

```
  year wolves moose winter_temp ice_bridges
1 1959     20   538        1.40          no
2 1960     22   564        8.45          no
3 1961     22   572        9.75         yes
4 1962     23   579        2.15         yes
5 1963     20   596       -0.35         yes
6 1964     26   620       12.40          no
```

- Variables are:
 - **year**: Calendar year of observation.
 - **wolves**: Estimated number of wolves present that year.
 - **moose**: Estimated number of moose present that year.
 - **winter_temp**: Mean January-February temperature (oF).
 - **ice_bridges**: Whether or not an ice bridge formed to the mainland.

---

### What is Happening Here? Where is the graph?

```r
p <- ggplot(data=irmw,mapping=aes(x=year,y=wolves)) +
 geom_line()
```

```r
p
```

---

### Controlling Specific Aspects of the Graph

---

# Aesthetics

- Visual properties of geoms on the graph.

--
- Commonly used aesthetics are:
  - `x=`: positioning along x-axis.
  - `y=`: positioning along y-axis.

--
  - `color=`: [color](https://derekogle.com/NCGraphing/resources/colors) of objects or, if 2-d (e.g., bars, some points), object's outline.

--
  - `fill=`: fill [color](https://derekogle.com/NCGraphing/resources/colors) of objects.

--
  - `alpha=`: color transparency color (0 [fully transparent] to 1 [fully opaque]).

--
  - `shape=`: [shape](https://ggplot2.tidyverse.org/articles/ggplot2-specs.html#point) of markers in scatter plots.

--
  - `size=`: how large objects appear.

--
  - `linetype=`: how [lines](https://ggplot2.tidyverse.org/articles/ggplot2-specs.html#lines) should be drawn (`"solid"`, `"dashed"`, `"dotted"`, etc.)

&nbsp;

--
- Which aesthetics are required and allowed vary by geom.

---

# Aesthetics
### Can be *set* for all data within each 'geom'.

---

```r
p <- ggplot(data=irmw, 
 mapping=aes(x=year,y=wolves)) + 
 geom_point( 
 ) + 
 geom_line( 
 ) 
 
p
```
]]
.column[.content[
<img src="Lecture_MooseWolves_files/figure-html/scatline_yr2a_non_seq_1_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=irmw, 
 mapping=aes(x=year,y=wolves)) + 
 geom_point( 
* color="red",
 ) + 
 geom_line( 
 ) 
 
p
```
]]
.column[.content[
<img src="Lecture_MooseWolves_files/figure-html/scatline_yr2a_non_seq_2_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=irmw, 
 mapping=aes(x=year,y=wolves)) + 
 geom_point( 
 color="red", 
* size=1
 ) + 
 geom_line( 
 ) 
 
p
```
]]
.column[.content[
<img src="Lecture_MooseWolves_files/figure-html/scatline_yr2a_non_seq_3_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=irmw, 
 mapping=aes(x=year,y=wolves)) + 
 geom_point( 
 color="red", 
 size=1 
 ) + 
 geom_line( 
* size=0.25,
 ) 
 
p
```
]]
.column[.content[
<img src="Lecture_MooseWolves_files/figure-html/scatline_yr2a_non_seq_4_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=irmw, 
 mapping=aes(x=year,y=wolves)) + 
 geom_point( 
 color="red", 
 size=1 
 ) + 
 geom_line( 
 size=0.25, 
* linetype="dashed"
 ) 
 
p
```
]]
.column[.content[
<img src="Lecture_MooseWolves_files/figure-html/scatline_yr2a_non_seq_5_output-1.png" width="100%" />
]]

---

### Order of geoms Becomes Apparent

---

```r
*p <- ggplot(data=irmw,
* mapping=aes(x=year,y=wolves))
 
p
```
]]
.column[.content[
<img src="Lecture_MooseWolves_files/figure-html/scatline_yr2b_auto_1_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=irmw, 
 mapping=aes(x=year,y=wolves)) + 
* geom_line(size=0.25,linetype="dashed")
 
p
```
]]
.column[.content[
<img src="Lecture_MooseWolves_files/figure-html/scatline_yr2b_auto_2_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=irmw, 
 mapping=aes(x=year,y=wolves)) + 
 geom_line(size=0.25,linetype="dashed") + 
* geom_point(color="red",size=1)
 
p
```
]]
.column[.content[
<img src="Lecture_MooseWolves_files/figure-html/scatline_yr2b_auto_3_output-1.png" width="100%" />
]]

---

# Aesthetics
### Can be *globally mapped* to *a color* in aes() in mapping= in ggplot().

---

```r
p <- ggplot(data=irmw, 
 mapping=aes(x=year,y=wolves, 
 )) + 
 geom_line() + 
 geom_point(size=1) 
 
p
```
]]
.column[.content[
<img src="Lecture_MooseWolves_files/figure-html/scatline_yr3a_non_seq_1_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=irmw, 
 mapping=aes(x=year,y=wolves, 
* color="red"
 )) + 
 geom_line() + 
 geom_point(size=1) 
 
p
```
]]
.column[.content[
<img src="Lecture_MooseWolves_files/figure-html/scatline_yr3a_non_seq_2_output-1.png" width="100%" />
]]

---

# Aesthetics
### Can be *globally mapped* to *a variable* in aes() in mapping= in ggplot().

---

```r
p <- ggplot(data=irmw, 
 mapping=aes(x=year,y=wolves, 
 )) + 
 geom_line() + 
 geom_point(size=1) 
 
p
```
]]
.column[.content[
<img src="Lecture_MooseWolves_files/figure-html/scatline_yr3b_non_seq_1_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=irmw, 
 mapping=aes(x=year,y=wolves, 
* color=ice_bridges
 )) + 
 geom_line() + 
 geom_point(size=1) 
 
p
```
]]
.column[.content[
<img src="Lecture_MooseWolves_files/figure-html/scatline_yr3b_non_seq_2_output-1.png" width="100%" />
]]

---

# Aesthetics
### Can be *mapped* to *a variable* for a specific 'geom' with aes() in the geom_XXXX().

---

```r
p <- ggplot(data=irmw, 
 mapping=aes(x=year,y=wolves)) + 
 geom_line() + 
 geom_point( 
 size=1 
 ) 
 
p
```
]]
.column[.content[
<img src="Lecture_MooseWolves_files/figure-html/scatline_yr3c_non_seq_1_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=irmw, 
 mapping=aes(x=year,y=wolves)) + 
 geom_line() + 
 geom_point( 
* aes(color=ice_bridges),
 size=1 
 ) 
 
p
```
]]
.column[.content[
<img src="Lecture_MooseWolves_files/figure-html/scatline_yr3c_non_seq_2_output-1.png" width="100%" />
]]

---

```r
p <- ggplot(data=irmw, 
 mapping=aes(x=year,y=wolves)) + 
 geom_line() + 
 geom_point( 
 size=1 
 ) 
 
p
```
]]
.column[.content[
<img src="Lecture_MooseWolves_files/figure-html/scatline_yr3d_non_seq_1_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=irmw, 
 mapping=aes(x=year,y=wolves)) + 
 geom_line() + 
 geom_point( 
* aes(color=winter_temp),
 size=1 
 ) 
 
p
```
]]
.column[.content[
<img src="Lecture_MooseWolves_files/figure-html/scatline_yr3d_non_seq_2_output-1.png" width="100%" />
]]

---

```r
p <- ggplot(data=irmw, 
 mapping=aes(x=year,y=wolves)) + 
 geom_line() + 
 geom_point( 
 aes( 
 ), 
 size=1) 
 
p
```
]]
.column[.content[
<img src="Lecture_MooseWolves_files/figure-html/scatline_yr3e_non_seq_1_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=irmw, 
 mapping=aes(x=year,y=wolves)) + 
 geom_line() + 
 geom_point( 
 aes( 
* color=winter_temp,
 ), 
 size=1) 
 
p
```
]]
.column[.content[
<img src="Lecture_MooseWolves_files/figure-html/scatline_yr3e_non_seq_2_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=irmw, 
 mapping=aes(x=year,y=wolves)) + 
 geom_line() + 
 geom_point( 
 aes( 
 color=winter_temp, 
* shape=ice_bridges
 ), 
 size=1) 
 
p
```
]]
.column[.content[
<img src="Lecture_MooseWolves_files/figure-html/scatline_yr3e_non_seq_3_output-1.png" width="100%" />
]]

---

# Scales

- Scales define how values of the aesthetic are mapped to values of the data

--
- For example ...
  - Defines colors used for each group.
  - Defines shapes of points used for each group.
  - Defines characteristics of axes (e.g., labels, breaks, limits).

--
- All scales have sensible defaults; but you may want to change.

&nbsp;

--
- Scale functions are of the form `scale_`*`aesthetic`*`_`*`suffix`*.
  - *`aesthetic`* is an aesthetic name (e.g., `color`, `shape`, `x`).
  - *`suffix`* is the functionality of the scale (e.g., `manual`, `discrete`).

&nbsp;

--
- Basic functionality introduced here, more details in subsequent modules.

---

# Using Scales to Change ...
### Default Colors
### Axis Names and Breaks

---

```r
*p <- ggplot(data=irmw,
* mapping=aes(x=year,y=wolves)) +
* geom_line() +
* geom_point(aes(color=ice_bridges),size=1)
 
p
```
]]
.column[.content[
<img src="Lecture_MooseWolves_files/figure-html/scatline_yr4a_user_1_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=irmw, 
 mapping=aes(x=year,y=wolves)) + 
 geom_line() + 
 geom_point(aes(color=ice_bridges),size=1) + 
* scale_color_manual(values=c("blue","red"),
* name="Ice Bridge?")
 
p
```
]]
.column[.content[
<img src="Lecture_MooseWolves_files/figure-html/scatline_yr4a_user_2_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=irmw, 
 mapping=aes(x=year,y=wolves)) + 
 geom_line() + 
 geom_point(aes(color=ice_bridges),size=1) + 
 scale_color_manual(values=c("blue","red"), 
 name="Ice Bridge?") + 
* scale_x_continuous(name="Year",
* breaks=seq(1960,2020,10),
* minor_breaks=seq(1959,2019,1))
 
p
```
]]
.column[.content[
<img src="Lecture_MooseWolves_files/figure-html/scatline_yr4a_user_3_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=irmw, 
 mapping=aes(x=year,y=wolves)) + 
 geom_line() + 
 geom_point(aes(color=ice_bridges),size=1) + 
 scale_color_manual(values=c("blue","red"), 
 name="Ice Bridge?") + 
 scale_x_continuous(name="Year", 
 breaks=seq(1960,2020,10), 
 minor_breaks=seq(1959,2019,1)) + 
* scale_y_continuous(name="Estimated Number of Wolves",
* breaks=seq(0,50,5),
* minor_breaks=NULL)
 
p
```
]]
.column[.content[
<img src="Lecture_MooseWolves_files/figure-html/scatline_yr4a_user_4_output-1.png" width="100%" />
]]

---

# Themes

- A theme controls elements of the graph not related to data; e.g.,
  - Background color.
  - Size of font.
  - Gridlines.

--
- Each of these elements can be individually modified.

--
- Entire themes can be created and saved for continual use.

--
- We will revisit details of themes in a later module.

--
- However, common themes exist; e.g.,
  - `theme_bw()`
  - `theme_gray()`
  - `theme_minimal()`
  - `theme_classic()`

---

```r
p <- ggplot(data=irmw, 
 mapping=aes(x=year,y=wolves)) + 
 geom_line() + 
 geom_point(aes(color=ice_bridges),size=1) + 
 scale_color_manual(values=c("blue","red"), 
 name="Ice Bridge?") + 
 scale_x_continuous(name="Year", 
 breaks=seq(1960,2020,5)) + 
 scale_y_continuous(name="Estimated Number of Wolves", 
 breaks=seq(0,50,5)) 
 
p
```
]]
.column[.content[
<img src="Lecture_MooseWolves_files/figure-html/scatline_yr5_1_1_output-1.png" width="100%" />
]]

---

```r
*p 
```
]]
.column[.content[
<img src="Lecture_MooseWolves_files/figure-html/scatline_yr5a_user_1_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p + 
* theme_classic()
```
]]
.column[.content[
<img src="Lecture_MooseWolves_files/figure-html/scatline_yr5a_user_2_output-1.png" width="100%" />
]]

---

```r
* p
```
]]
.column[.content[
<img src="Lecture_MooseWolves_files/figure-html/scatline_yr5b_user_1_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
 p + 
* theme_minimal()
```
]]
.column[.content[
<img src="Lecture_MooseWolves_files/figure-html/scatline_yr5b_user_2_output-1.png" width="100%" />
]]

---

```r
*p 
```
]]
.column[.content[
<img src="Lecture_MooseWolves_files/figure-html/scatline_yr5c_user_1_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p + 
* theme_bw()
```
]]
.column[.content[
<img src="Lecture_MooseWolves_files/figure-html/scatline_yr5c_user_2_output-1.png" width="100%" />
]]

---

## Final Thought for Next Time

> "[This book will] introduce you to `ggplot2` as a novice, unfamiliar with the grammar; teach you the basics so that you can re-create plots you are already familiar with [and] show you how to use the grammar to create new types of graphics ..." -- Wickham (2009)

# Next Time

We will examine graphs for distributions of quantitative data.