Graphing Univariate Densities

# Graphing Univariate Densities
### Derek Ogle, May 2020

---

# Background

- Morphological measurements of Florida Black Bear (*Ursus americanus floridanus*).
  - Published in [Bartareau, T.M. 2017. Estimating the live body weight of American Black Bears in Florida. Journal of Fish and Wildlife Management 8:234-239](https://www.fwspubs.org/doi/10.3996/012016-JFWM-003).
  - Data provided as a supplement.

.center[
<img src="https://derekogle.com/NCGraphing/modules/zimgs/blackbear.jpg" width="500px" />
<font size="1">Credit: <a href="https://news.illinois.edu/view/6367/803074">University of Illinois</a></font>
]

---

# Background

- Downloaded 'fwma-08-01-02_table s1.xls' Excel file from [Journal of Fish and Wildlife Management](https://www.fwspubs.org/doi/10.3996/012016-JFWM-003) and saved as [BlackBearsFL.csv]("https://raw.githubusercontent.com/droglenc/NCData/master/BlackBearsFL.csv").

--
- Loaded data into `flbb` data.frame.
  - Renamed some variables to remove spaces and capitalization.
  - Changed sex labels and made `sex` a factor variable.

```r
#!# Set to your own working directory and have just your filename below.
flbb <- read.csv("https://raw.githubusercontent.com/droglenc/NCData/master/BlackBearsFL.csv") %>%
  rename(sex=SEX,
         age=AGE..yr.,
         length=BODY.LENGTH..cm.,
         weight=BODY.WEIGHT..kg.,
         chest=CHEST.GIRTH..cm.) %>%
  mutate(sex=plyr::mapvalues(sex,
                             from=c("FEMALE","MALE"),
                             to=c("Female","Male")),
         sex=factor(sex))
```

---

# Background

```r
head(flbb)
```

```
       DATE    sex  age weight chest length
1 4/17/2003 Female 0.10   2.47  29.8   39.0
2 3/22/2002 Female 0.17   5.30  29.0   46.5
3  4/8/2003 Female 0.17   5.64  30.2   49.0
4  4/1/2003 Female 0.17   5.85  31.4   50.4
5 7/19/2012 Female 0.42   6.80  39.0   74.0
6 7/19/2012 Female 0.42  13.15  56.2   90.5
```

- Variables are:
  - **DATE**: Date of capture.
  - **sex**: Sex of bear (`Female` or `Male`).
  - **age**: Estimated age of the bear (years).
  - **weight**: Body weight of the bear (kg).
  - **chest**: Chest girth of the bear (cm).
  - **length**: Body length of the bear (cm).

--
- Our focus here will be on `weight` and `sex`.

---

# Histogram

.pull-left[
- Displays the frequency of observations in "bins" of the quantitative *X* variable.
  - i.e., the distribution of *X*.
]

---

# Histogram

.pull-left[
- Constructed with `geom_histogram()`.
  - `bins=`: Number of bins to use.
  - `binwidth=`: Width of the bins.
  - `breaks=`: Bin boundary values.
  - `boundary=`: Left-most boundary of first bin.
  - `center=`: Center of first bin.
  - `closed=`: Are bins closed on `"right"` (*default*) or `"left"`.
]

---

# A Mostly Default Histogram

---

```r
*p <- ggplot(data=flbb, mapping=aes(x=weight))
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/hist1_auto_1_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb, mapping=aes(x=weight)) +  
* geom_histogram()
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/hist1_auto_2_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb, mapping=aes(x=weight)) +  
  geom_histogram() +  
* scale_y_continuous(name="Frequency of Bears")
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/hist1_auto_3_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb, mapping=aes(x=weight)) +  
  geom_histogram() +  
  scale_y_continuous(name="Frequency of Bears") +  
* scale_x_continuous(name="Weight (kg)",breaks=seq(0,250,25))
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/hist1_auto_4_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb, mapping=aes(x=weight)) +  
  geom_histogram() +  
  scale_y_continuous(name="Frequency of Bears") +  
  scale_x_continuous(name="Weight (kg)",breaks=seq(0,250,25)) +  
* theme_bw()
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/hist1_auto_5_output-1.png" width="100%" />
]]

---

# Histogram with Typical Modifications

---

```r
p <- ggplot(data=flbb, mapping=aes(x=weight)) +  
  geom_histogram(  
  ) +  
  scale_y_continuous(  
    name="Frequency of Bears",  
  ) +  
  scale_x_continuous(  
    name="Weight (kg)",breaks=seq(0,250,25),  
  ) +  
  theme_bw()  
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/hist2_non_seq_1_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb, mapping=aes(x=weight)) +  
  geom_histogram(  
*   color="gray30",
  ) +  
  scale_y_continuous(  
    name="Frequency of Bears",  
  ) +  
  scale_x_continuous(  
    name="Weight (kg)",breaks=seq(0,250,25),  
  ) +  
  theme_bw()  
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/hist2_non_seq_2_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb, mapping=aes(x=weight)) +  
  geom_histogram(  
    color="gray30",  
*   fill="gray30",
  ) +  
  scale_y_continuous(  
    name="Frequency of Bears",  
  ) +  
  scale_x_continuous(  
    name="Weight (kg)",breaks=seq(0,250,25),  
  ) +  
  theme_bw()  
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/hist2_non_seq_3_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb, mapping=aes(x=weight)) +  
  geom_histogram(  
    color="gray30",  
    fill="gray30",  
*   alpha=0.25,
  ) +  
  scale_y_continuous(  
    name="Frequency of Bears",  
  ) +  
  scale_x_continuous(  
    name="Weight (kg)",breaks=seq(0,250,25),  
  ) +  
  theme_bw()  
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/hist2_non_seq_4_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb, mapping=aes(x=weight)) +  
  geom_histogram(  
    color="gray30",  
    fill="gray30",  
    alpha=0.25,  
*   binwidth=10,
  ) +  
  scale_y_continuous(  
    name="Frequency of Bears",  
  ) +  
  scale_x_continuous(  
    name="Weight (kg)",breaks=seq(0,250,25),  
  ) +  
  theme_bw()  
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/hist2_non_seq_5_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb, mapping=aes(x=weight)) +  
  geom_histogram(  
    color="gray30",  
    fill="gray30",  
    alpha=0.25,  
    binwidth=10,  
*   boundary=0,closed="left"
  ) +  
  scale_y_continuous(  
    name="Frequency of Bears",  
  ) +  
  scale_x_continuous(  
    name="Weight (kg)",breaks=seq(0,250,25),  
  ) +  
  theme_bw()  
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/hist2_non_seq_6_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb, mapping=aes(x=weight)) +  
  geom_histogram(  
    color="gray30",  
    fill="gray30",  
    alpha=0.25,  
    binwidth=10,  
    boundary=0,closed="left"  
  ) +  
  scale_y_continuous(  
    name="Frequency of Bears",  
*   expand=expansion(mult=c(0,0.05))
  ) +  
  scale_x_continuous(  
    name="Weight (kg)",breaks=seq(0,250,25),  
  ) +  
  theme_bw()  
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/hist2_non_seq_7_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb, mapping=aes(x=weight)) +  
  geom_histogram(  
    color="gray30",  
    fill="gray30",  
    alpha=0.25,  
    binwidth=10,  
    boundary=0,closed="left"  
  ) +  
  scale_y_continuous(  
    name="Frequency of Bears",  
    expand=expansion(mult=c(0,0.05))  
  ) +  
  scale_x_continuous(  
    name="Weight (kg)",breaks=seq(0,250,25),  
*   expand=expansion(mult=c(0,0))
  ) +  
  theme_bw()  
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/hist2_non_seq_8_output-1.png" width="100%" />
]]

---

# Histogram with Typical Modifications

### Finish by Removing Vertical Grids

---

```r
*p <- ggplot(data=flbb, mapping=aes(x=weight)) +
* geom_histogram(color="gray30",fill="gray30",alpha=0.25,
*                binwidth=10,boundary=0,closed="left") +
* scale_y_continuous(name="Frequency of Bears",
*                    expand=expansion(mult=c(0,0.05))) +
* scale_x_continuous(name="Weight (kg)",breaks=seq(0,250,25),
*                    expand=expansion(mult=c(0,0))) +
* theme_bw()
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/hist3_user_1_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb, mapping=aes(x=weight)) +  
  geom_histogram(color="gray30",fill="gray30",alpha=0.25,  
                 binwidth=10,boundary=0,closed="left") +  
  scale_y_continuous(name="Frequency of Bears",  
                     expand=expansion(mult=c(0,0.05))) +  
  scale_x_continuous(name="Weight (kg)",breaks=seq(0,250,25),  
                     expand=expansion(mult=c(0,0))) +  
  theme_bw() +  
* theme(panel.grid.major.x=element_blank(),
*       panel.grid.minor.x=element_blank())
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/hist3_user_2_output-1.png" width="100%" />
]]

---

# Stacked Histogram (Split by Groups)

---

```r
p <- ggplot(data=flbb,mapping=aes(  
              x=weight,  
            )) +  
  geom_histogram(binwidth=10,boundary=0,closed="left",  
  ) +  
  scale_y_continuous(name="Frequency of Bears",  
                     expand=expansion(mult=c(0,0.05))) +  
  scale_x_continuous(name="Weight (kg)",breaks=seq(0,250,25),  
                     expand=expansion(mult=c(0,0))) +  
  theme_bw() +  
  theme(panel.grid.major.x=element_blank(),  
        panel.grid.minor.x=element_blank())  
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/histsplit1_non_seq_1_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb,mapping=aes(  
              x=weight,  
*             color=sex,
            )) +  
  geom_histogram(binwidth=10,boundary=0,closed="left",  
  ) +  
  scale_y_continuous(name="Frequency of Bears",  
                     expand=expansion(mult=c(0,0.05))) +  
  scale_x_continuous(name="Weight (kg)",breaks=seq(0,250,25),  
                     expand=expansion(mult=c(0,0))) +  
  theme_bw() +  
  theme(panel.grid.major.x=element_blank(),  
        panel.grid.minor.x=element_blank())  
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/histsplit1_non_seq_2_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb,mapping=aes(  
              x=weight,  
              color=sex,  
*             fill=sex
            )) +  
  geom_histogram(binwidth=10,boundary=0,closed="left",  
  ) +  
  scale_y_continuous(name="Frequency of Bears",  
                     expand=expansion(mult=c(0,0.05))) +  
  scale_x_continuous(name="Weight (kg)",breaks=seq(0,250,25),  
                     expand=expansion(mult=c(0,0))) +  
  theme_bw() +  
  theme(panel.grid.major.x=element_blank(),  
        panel.grid.minor.x=element_blank())  
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/histsplit1_non_seq_3_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb,mapping=aes(  
              x=weight,  
              color=sex,  
              fill=sex  
            )) +  
  geom_histogram(binwidth=10,boundary=0,closed="left",  
*                alpha=0.25
  ) +  
  scale_y_continuous(name="Frequency of Bears",  
                     expand=expansion(mult=c(0,0.05))) +  
  scale_x_continuous(name="Weight (kg)",breaks=seq(0,250,25),  
                     expand=expansion(mult=c(0,0))) +  
  theme_bw() +  
  theme(panel.grid.major.x=element_blank(),  
        panel.grid.minor.x=element_blank())  
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/histsplit1_non_seq_4_output-1.png" width="100%" />
]]

---

# Stacked Histogram (Split by Groups)

### Modify the Default Colors

---

```r
*p <- ggplot(data=flbb,
*           mapping=aes(x=weight,color=sex,fill=sex)) +
* geom_histogram(binwidth=10,boundary=0,closed="left",
*                alpha=0.25) +
* scale_y_continuous(name="Frequency of Bears",
*                    expand=expansion(mult=c(0,0.05))) +
* scale_x_continuous(name="Weight (kg)",breaks=seq(0,250,25),
*                    expand=expansion(mult=c(0,0))) +
* theme_bw() +
* theme(panel.grid.major.x=element_blank(),
*       panel.grid.minor.x=element_blank())
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/histsplit2_user_1_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb,  
            mapping=aes(x=weight,color=sex,fill=sex)) +  
  geom_histogram(binwidth=10,boundary=0,closed="left",  
                 alpha=0.25) +  
  scale_y_continuous(name="Frequency of Bears",  
                     expand=expansion(mult=c(0,0.05))) +  
  scale_x_continuous(name="Weight (kg)",breaks=seq(0,250,25),  
                     expand=expansion(mult=c(0,0))) +  
  theme_bw() +  
  theme(panel.grid.major.x=element_blank(),  
        panel.grid.minor.x=element_blank()) +  
* scale_color_manual(values=c("red","blue"))
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/histsplit2_user_2_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb,  
            mapping=aes(x=weight,color=sex,fill=sex)) +  
  geom_histogram(binwidth=10,boundary=0,closed="left",  
                 alpha=0.25) +  
  scale_y_continuous(name="Frequency of Bears",  
                     expand=expansion(mult=c(0,0.05))) +  
  scale_x_continuous(name="Weight (kg)",breaks=seq(0,250,25),  
                     expand=expansion(mult=c(0,0))) +  
  theme_bw() +  
  theme(panel.grid.major.x=element_blank(),  
        panel.grid.minor.x=element_blank()) +  
  scale_color_manual(values=c("red","blue")) +  
* scale_fill_manual(values=c("red","blue"))
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/histsplit2_user_3_output-1.png" width="100%" />
]]

---

# Density Plot

---

.pull-left[
# Density Plot
- Displays the density of observations of the quantitative *X* variable.
  - i.e., relative distribution of *X*.
  - A smoothed representation of a histogram.
- Constructed with `geom_density()`.
  - Use `adjust=` to modify "smoothing effect."
    - Smaller values = less smoothing.
]

---

# Density Plot

### With Same Modifications as Histogram

---

```r
p <- ggplot(data=flbb, mapping=aes(x=weight)) +  
  geom_density(  
  ) +  
  scale_x_continuous(  
    name="Weight (kg)",  
    breaks=seq(0,250,25),expand=expansion(mult=c(0,0)),  
  ) +  
  scale_y_continuous(name="Relative Density of Bears",  
                     expand=expansion(mult=c(0,0.05))) +  
  theme_bw() +  
  theme(panel.grid.major.x=element_blank(),  
        panel.grid.minor.x=element_blank())  
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/density1_non_seq_1_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb, mapping=aes(x=weight)) +  
  geom_density(  
*   color="gray30",
  ) +  
  scale_x_continuous(  
    name="Weight (kg)",  
    breaks=seq(0,250,25),expand=expansion(mult=c(0,0)),  
  ) +  
  scale_y_continuous(name="Relative Density of Bears",  
                     expand=expansion(mult=c(0,0.05))) +  
  theme_bw() +  
  theme(panel.grid.major.x=element_blank(),  
        panel.grid.minor.x=element_blank())  
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/density1_non_seq_2_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb, mapping=aes(x=weight)) +  
  geom_density(  
    color="gray30",  
*   fill="gray30",
  ) +  
  scale_x_continuous(  
    name="Weight (kg)",  
    breaks=seq(0,250,25),expand=expansion(mult=c(0,0)),  
  ) +  
  scale_y_continuous(name="Relative Density of Bears",  
                     expand=expansion(mult=c(0,0.05))) +  
  theme_bw() +  
  theme(panel.grid.major.x=element_blank(),  
        panel.grid.minor.x=element_blank())  
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/density1_non_seq_3_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb, mapping=aes(x=weight)) +  
  geom_density(  
    color="gray30",  
    fill="gray30",  
*   alpha=0.25
  ) +  
  scale_x_continuous(  
    name="Weight (kg)",  
    breaks=seq(0,250,25),expand=expansion(mult=c(0,0)),  
  ) +  
  scale_y_continuous(name="Relative Density of Bears",  
                     expand=expansion(mult=c(0,0.05))) +  
  theme_bw() +  
  theme(panel.grid.major.x=element_blank(),  
        panel.grid.minor.x=element_blank())  
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/density1_non_seq_4_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb, mapping=aes(x=weight)) +  
  geom_density(  
    color="gray30",  
    fill="gray30",  
    alpha=0.25  
  ) +  
  scale_x_continuous(  
    name="Weight (kg)",  
    breaks=seq(0,250,25),expand=expansion(mult=c(0,0)),  
*   limits=c(0,250)
  ) +  
  scale_y_continuous(name="Relative Density of Bears",  
                     expand=expansion(mult=c(0,0.05))) +  
  theme_bw() +  
  theme(panel.grid.major.x=element_blank(),  
        panel.grid.minor.x=element_blank())  
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/density1_non_seq_5_output-1.png" width="100%" />
]]

---

# Density Plot

### Adjusting "Smoothness"

---

```r
p <- ggplot(data=flbb, mapping=aes(x=weight)) +  
  geom_density(color="gray30",fill="gray30",alpha=0.25,  
  ) +  
  scale_x_continuous(name="Weight (kg)",breaks=seq(0,250,25),  
                     expand=expansion(mult=c(0,0)),  
                     limits=c(0,250)) +  
  scale_y_continuous(name="Relative Density of Bears",  
                     expand=expansion(mult=c(0,0.05))) +  
  theme_bw() +  
  theme(panel.grid.major.x=element_blank(),  
        panel.grid.minor.x=element_blank())  
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/density2_non_seq_1_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb, mapping=aes(x=weight)) +  
  geom_density(color="gray30",fill="gray30",alpha=0.25,  
*              adjust=0.25
  ) +  
  scale_x_continuous(name="Weight (kg)",breaks=seq(0,250,25),  
                     expand=expansion(mult=c(0,0)),  
                     limits=c(0,250)) +  
  scale_y_continuous(name="Relative Density of Bears",  
                     expand=expansion(mult=c(0,0.05))) +  
  theme_bw() +  
  theme(panel.grid.major.x=element_blank(),  
        panel.grid.minor.x=element_blank())  
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/density2_non_seq_2_output-1.png" width="100%" />
]]

---

# Density Plot by Groups

---

```r
p <- ggplot(data=flbb, mapping=aes(  
  x=weight,  
  )) +  
  geom_density(alpha=0.25) +  
  scale_x_continuous(name="Weight (kg)",  
                     breaks=seq(0,250,25),limits=c(0,250),  
                     expand=expansion(mult=c(0,0))) +  
  scale_y_continuous(name="Relative Density of Bears",  
                     expand=expansion(mult=c(0,0.05))) +  
  theme_bw() +  
  theme(panel.grid.major.x=element_blank(),  
        panel.grid.minor.x=element_blank())  
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/densitysplit1_non_seq_1_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb, mapping=aes(  
  x=weight,  
* color=sex,
  )) +  
  geom_density(alpha=0.25) +  
  scale_x_continuous(name="Weight (kg)",  
                     breaks=seq(0,250,25),limits=c(0,250),  
                     expand=expansion(mult=c(0,0))) +  
  scale_y_continuous(name="Relative Density of Bears",  
                     expand=expansion(mult=c(0,0.05))) +  
  theme_bw() +  
  theme(panel.grid.major.x=element_blank(),  
        panel.grid.minor.x=element_blank())  
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/densitysplit1_non_seq_2_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb, mapping=aes(  
  x=weight,  
  color=sex,  
* fill=sex
  )) +  
  geom_density(alpha=0.25) +  
  scale_x_continuous(name="Weight (kg)",  
                     breaks=seq(0,250,25),limits=c(0,250),  
                     expand=expansion(mult=c(0,0))) +  
  scale_y_continuous(name="Relative Density of Bears",  
                     expand=expansion(mult=c(0,0.05))) +  
  theme_bw() +  
  theme(panel.grid.major.x=element_blank(),  
        panel.grid.minor.x=element_blank())  
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/densitysplit1_non_seq_3_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb, mapping=aes(  
  x=weight,  
  color=sex,  
  fill=sex  
  )) +  
  geom_density(alpha=0.25) +  
  scale_x_continuous(name="Weight (kg)",  
                     breaks=seq(0,250,25),limits=c(0,250),  
                     expand=expansion(mult=c(0,0))) +  
  scale_y_continuous(name="Relative Density of Bears",  
                     expand=expansion(mult=c(0,0.05))) +  
* scale_color_manual(values=c("red","blue")) +
  theme_bw() +  
  theme(panel.grid.major.x=element_blank(),  
        panel.grid.minor.x=element_blank())  
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/densitysplit1_non_seq_4_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb, mapping=aes(  
  x=weight,  
  color=sex,  
  fill=sex  
  )) +  
  geom_density(alpha=0.25) +  
  scale_x_continuous(name="Weight (kg)",  
                     breaks=seq(0,250,25),limits=c(0,250),  
                     expand=expansion(mult=c(0,0))) +  
  scale_y_continuous(name="Relative Density of Bears",  
                     expand=expansion(mult=c(0,0.05))) +  
  scale_color_manual(values=c("red","blue")) +  
* scale_fill_manual(values=c("red","blue")) +
  theme_bw() +  
  theme(panel.grid.major.x=element_blank(),  
        panel.grid.minor.x=element_blank())  
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/densitysplit1_non_seq_5_output-1.png" width="100%" />
]]

---

# Boxplot

---

.pull-left[
# Boxplot
- Displays summary statistics that describe the distribution of the quantitative *X* variable.
  - "Box" is from 1st quartile (Q1) to 3rd quartile (Q3).
  - Line in box is median.
  - "Whiskers" extend to min/max or last point not considered an "outlier."
  - "Outliers" are more than 1.5*IQR from Q1 or Q3.
- Usually for comparing groups.
- Constructed with `geom_boxplot()`.
]

---

# Boxplot with Typical Modifications

---

```r
*p <- ggplot(data=flbb, mapping=aes(x=sex,y=weight))
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/boxplot1_auto_1_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb, mapping=aes(x=sex,y=weight)) +  
* geom_boxplot(fill="gray30",alpha=0.25)
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/boxplot1_auto_2_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb, mapping=aes(x=sex,y=weight)) +  
  geom_boxplot(fill="gray30",alpha=0.25) +  
* scale_x_discrete(name="Sex")
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/boxplot1_auto_3_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb, mapping=aes(x=sex,y=weight)) +  
  geom_boxplot(fill="gray30",alpha=0.25) +  
  scale_x_discrete(name="Sex") +  
* scale_y_continuous(name="Weight (kg)",
*                    limits=c(0,250),breaks=seq(0,250,25),
*                    expand=expansion(mult=c(0,0)))
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/boxplot1_auto_4_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb, mapping=aes(x=sex,y=weight)) +  
  geom_boxplot(fill="gray30",alpha=0.25) +  
  scale_x_discrete(name="Sex") +  
  scale_y_continuous(name="Weight (kg)",  
                     limits=c(0,250),breaks=seq(0,250,25),  
                     expand=expansion(mult=c(0,0))) +  
* theme_bw()
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/boxplot1_auto_5_output-1.png" width="100%" />
]]

---

# Boxplot with Colors by Group

---

```r
p <- ggplot(data=flbb, mapping=aes(  
    x=sex,y=weight,  
  )) +  
  geom_boxplot(alpha=0.25) +  
  scale_x_discrete(name="Sex") +  
  scale_y_continuous(name="Weight (kg)",  
                     limits=c(0,250),breaks=seq(0,250,25),  
                     expand=expansion(mult=c(0,0))) +  
  theme_bw()   
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/boxplot2_non_seq_1_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb, mapping=aes(  
    x=sex,y=weight,  
*   color=sex,fill=sex
  )) +  
  geom_boxplot(alpha=0.25) +  
  scale_x_discrete(name="Sex") +  
  scale_y_continuous(name="Weight (kg)",  
                     limits=c(0,250),breaks=seq(0,250,25),  
                     expand=expansion(mult=c(0,0))) +  
  theme_bw()   
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/boxplot2_non_seq_2_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb, mapping=aes(  
    x=sex,y=weight,  
    color=sex,fill=sex  
  )) +  
  geom_boxplot(alpha=0.25) +  
  scale_x_discrete(name="Sex") +  
  scale_y_continuous(name="Weight (kg)",  
                     limits=c(0,250),breaks=seq(0,250,25),  
                     expand=expansion(mult=c(0,0))) +  
* scale_color_manual(values=c("red","blue")) +
  theme_bw()   
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/boxplot2_non_seq_3_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb, mapping=aes(  
    x=sex,y=weight,  
    color=sex,fill=sex  
  )) +  
  geom_boxplot(alpha=0.25) +  
  scale_x_discrete(name="Sex") +  
  scale_y_continuous(name="Weight (kg)",  
                     limits=c(0,250),breaks=seq(0,250,25),  
                     expand=expansion(mult=c(0,0))) +  
  scale_color_manual(values=c("red","blue")) +  
* scale_fill_manual(values=c("red","blue")) +
  theme_bw()   
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/boxplot2_non_seq_4_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb, mapping=aes(  
    x=sex,y=weight,  
    color=sex,fill=sex  
  )) +  
  geom_boxplot(alpha=0.25) +  
  scale_x_discrete(name="Sex") +  
  scale_y_continuous(name="Weight (kg)",  
                     limits=c(0,250),breaks=seq(0,250,25),  
                     expand=expansion(mult=c(0,0))) +  
  scale_color_manual(values=c("red","blue")) +  
  scale_fill_manual(values=c("red","blue")) +  
  theme_bw() +  
* theme(legend.position="none")
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/boxplot2_non_seq_5_output-1.png" width="100%" />
]]

---

# Violin Plot

---

.pull-left[
# Violin Plot
- Displays density of the quantitative *X* variable.
- Usually for comparing groups.
- Constructed with `geom_violin()`.
  - Very similar to use of `geom_boxplot()`.
    - Suggest using `trim=FALSE`.
]

---

```r
*p <- ggplot(data=flbb, mapping=aes(x=sex,y=weight))
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/violin1_auto_1_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb, mapping=aes(x=sex,y=weight)) +  
* geom_violin(fill="gray30",alpha=0.25,trim=FALSE)
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/violin1_auto_2_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb, mapping=aes(x=sex,y=weight)) +  
  geom_violin(fill="gray30",alpha=0.25,trim=FALSE) +  
* scale_x_discrete(name="Sex")
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/violin1_auto_3_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb, mapping=aes(x=sex,y=weight)) +  
  geom_violin(fill="gray30",alpha=0.25,trim=FALSE) +  
  scale_x_discrete(name="Sex") +  
* scale_y_continuous(name="Weight (kg)",
*                    limits=c(0,250),breaks=seq(0,250,25),
*                    expand=expansion(mult=c(0,0)))
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/violin1_auto_4_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb, mapping=aes(x=sex,y=weight)) +  
  geom_violin(fill="gray30",alpha=0.25,trim=FALSE) +  
  scale_x_discrete(name="Sex") +  
  scale_y_continuous(name="Weight (kg)",  
                     limits=c(0,250),breaks=seq(0,250,25),  
                     expand=expansion(mult=c(0,0))) +  
* theme_bw()
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/violin1_auto_5_output-1.png" width="100%" />
]]

---

# Violin Plot

### With Individual Points

---

```r
*p <- ggplot(data=flbb, mapping=aes(x=sex,y=weight)) +
* geom_violin(fill="gray30",alpha=0.25,trim=FALSE) +
* scale_x_discrete(name="Sex") +
* scale_y_continuous(name="Weight (kg)",
*                    limits=c(0,250),breaks=seq(0,250,25),
*                    expand=expansion(mult=c(0,0))) +
* theme_bw()
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/violin2_user_1_output-1.png" width="100%" />
]]
---
class: split-50
count: false

```r
p <- ggplot(data=flbb, mapping=aes(x=sex,y=weight)) +  
  geom_violin(fill="gray30",alpha=0.25,trim=FALSE) +  
  scale_x_discrete(name="Sex") +  
  scale_y_continuous(name="Weight (kg)",  
                     limits=c(0,250),breaks=seq(0,250,25),  
                     expand=expansion(mult=c(0,0))) +  
  theme_bw() +  
* geom_jitter(width=0.05,height=0,alpha=0.25)
 
p
```
]]
.column[.content[
<img src="Lecture_BlackBears_files/figure-html/violin2_user_2_output-1.png" width="100%" />
]]

---

# Next Time

<font size="7">We will examine graphs for distributions of categorical data.</font>