Lecture 7: Base R Plots

class: center, middle, inverse, title-slide

.title[
# Lecture 7: Base R Plots
]
.author[
### Robin Liu
]
.institute[
### UCSB
]
.date[
### 2022-06-30
]

---

# airquality
We will use the `airquality` data frame in the library `datasets`

```r
library(tidyverse)
```

```
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
```

```
## v ggplot2 3.3.6     v purrr   0.3.4
## v tibble  3.1.7     v dplyr   1.0.9
## v tidyr   1.2.0     v stringr 1.4.0
## v readr   2.1.2     v forcats 0.5.1
```

```
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
```

```r
library(datasets)
```
---
# airquality
`?airquality` will give you the metadata in the help window.

Description
    Daily air quality measurements in New York, May to September 1973.
 
    Format
    A data frame with 153 observations on 6 variables.

[,1]	Ozone	numeric	Ozone (ppb)
    [,2]	Solar.R	numeric	Solar R (lang)
    [,3]	Wind	numeric	Wind (mph)
    [,4]	Temp	numeric	Temperature (degrees F)
    [,5]	Month	numeric	Month (1--12)
    [,6]	Day	numeric	Day of month (1--31)
---
# Base R plots
Basic plotting is easy; just call the relevant functions.

```r
plot(airquality$Wind, airquality$Ozone)
```

But we should always add a meaningful title and axis labels...
---
# Base R plots
Basic plotting is easy; just call the relevant functions.

```r
plot(airquality$Wind, airquality$Ozone, main="Ozone and Wind in NYC, 1973",
     xlab="Wind (mph)", ylab="Ozone (ppb)")
```

<img src="Lec7_files/figure-html/unnamed-chunk-3-1.png" width="50%" height="50%" />
---
# Adding a trendline

```r
plot(airquality$Wind, airquality$Ozone, main="Ozone and Wind in NYC, 1973",
     xlab="Wind (mph)", ylab="Ozone (ppb)")
air_trend <- lm(airquality$Ozone ~ airquality$Wind)
abline(air_trend, col = "red")
```

<img src="Lec7_files/figure-html/unnamed-chunk-4-1.png" width="50%" height="50%" />
---
# Your turn
Create a scatter plot of Ozone vs. Solar Radiation with a trendline.
Add a meaningful title and axis labels to the plot.

![](Lec7_files/figure-html/unnamed-chunk-5-1.png)
<div class="countdown" id="timer_62bdd93f" style="right:0;bottom:0;" data-warnwhen="0">
<code class="countdown-time"><span class="countdown-digits minutes">02</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code>
</div>
---
# Histograms
.pull-left[

```r
hist(airquality$Ozone,
     main = "Histogram of Ozone",
     xlab = "Ozone")
```

![](Lec7_files/figure-html/unnamed-chunk-6-1.png)
]
--
.pull-right[

```r
hist(airquality$Ozone, breaks = 20,
     main = "Histogram of Ozone",
     xlab = "Ozone")
```

![](Lec7_files/figure-html/unnamed-chunk-7-1.png)
]
---
# Boxplots
Boxplots use the `y ~ x` notation. In R, this construct is called a `formula`

```r
boxplot(Ozone ~ Month, airquality, xlab = "Month", ylab = "Ozone (ppb)")
```

Median, percentiles, quartiles, IQR, outliers
---
# Formulas

```r
boxplot(Ozone ~ Month, airquality, xlab = "Month", ylab = "Ozone (ppb)")
```

```r
class(y ~ x)
```

```
## [1] "formula"
```

---
# Building plots
It is common to first create a plot and then layer stuff on top.

```r
x <- 1:3
y <- c(1, 3, 8)
plot(x, y) # Creates a new plot
lmout <- lm(y ~ x)
abline(lmout) # adds a line "fitting" the points
```

.pull-left[
<img src="Lec7_files/figure-html/unnamed-chunk-11-1.png" width="85%" height="80%" />
]
.pull-right[
<img src="Lec7_files/figure-html/unnamed-chunk-12-1.png" width="85%" height="80%" />
]
---
# Plot parameters

```r
x <- 1:3
y <- c(1, 3, 8)
plot(x, y, cex=2, col="red", pch=19) # Creates a new plot
lmout <- lm(y ~ x)
abline(lmout, lty=2, lwd=2) # adds a line "fitting" the points
```

.pull-left[
<img src="Lec7_files/figure-html/unnamed-chunk-14-1.png" width="85%" height="80%" />
]
.pull-right[
<img src="Lec7_files/figure-html/unnamed-chunk-15-1.png" width="85%" height="80%" />
]
---
# Plot parameters
Plotting is an art and you should use Google and `?par` to get
a plot to look nice.

```r
x <- 1:3
y <- c(1, 3, 8)
plot(x, y, cex=3, col="chartreuse4", pch=17) # Creates a new plot
lmout <- lm(y ~ x)
abline(lmout, lty=2, lwd=2) # adds a line "fitting" the points
```

.pull-left[
<img src="Lec7_files/figure-html/unnamed-chunk-17-1.png" width="85%" height="80%" />
]
.pull-right[
![:scale 80%](Lec7_files/pch.png)
]

---
# Plot parameters
Plotting is an art and you should use Google and `?par` to get
a plot to look nice.

```r
x <- 1:3
y <- c(1, 3, 8)
plot(x, y, cex=3, col="chartreuse4", pch=17) # Creates a new plot
lmout <- lm(y ~ x)
abline(lmout, lty=2, lwd=2) # adds a line "fitting" the points
```

.pull-left[
<img src="Lec7_files/figure-html/unnamed-chunk-19-1.png" width="85%" height="80%" />
]
.pull-right[
![:scale 75%](Lec7_files/col.png)
]
---
# Building plots
We see a negative trend with wind and ozone.
.pull-left[

```r
plot(airquality$Wind, airquality$Ozone)
```

![](Lec7_files/figure-html/unnamed-chunk-20-1.png)
]

.pull-right[
A flat screen can only show two variables.

unless...? 😳

We can add another dimension of information by employing
*shape* and *color*.
]
---
# Layering Plots Demo 1
It is common for complex plots to start with an *empty* plot
and layer stuff on top.
<img src="Lec7_files/figure-html/unnamed-chunk-21-1.png" style="display: block; margin: auto;" />
---
# Layering Plots Demo 1

```r
with(airquality, plot(Wind, Ozone, main = "Ozone and Wind in New York City", type = "n"))
with(filter(airquality, Month != 5), points(Wind, Ozone, col="red", pch=19))
with(filter(airquality, Month == 5), points(Wind, Ozone, col="blue", pch=17))
legend("topright", pch = c(17, 19), col = c("blue", "red"),
       legend = c("May", "Other Months"))
```

![](Lec7_files/figure-html/unnamed-chunk-22-1.png)

---
# Layering Plots Demo 2
How does *temperature* relate to ozone and wind?
![](Lec7_files/figure-html/unnamed-chunk-23-1.png)

---

# Plotting functions
To plot a function, *discretize* the domain into `x` and *evaluate* the function into `y`.

```r
x <- seq(-2*pi, 2*pi, len = 100)
y <- sin(x)
plot(y ~ x)
```

<img src="Lec7_files/figure-html/unnamed-chunk-24-1.png" width="50%" height="50%" />
---
# Plotting functions
To plot a function, *discretize* the domain into `x` and *evaluate* the function into `y`.

```r
x <- seq(-2*pi, 2*pi, len = 100)
y <- sin(x)
plot(y ~ x, type="l") # type="1" smooths the line
```

---
# Plotting functions
To plot a function, *discretize* the domain into `x` and *evaluate* the function into `y`.

```r
x <- seq(-2*pi, 2*pi, len = 100)
y <- sin(x)
plot(y ~ x, type="l") # type="1" smooths the line
lines(cos(x) ~ x, type="l", col="blue") # "lines" adds to an existing plot
```

<img src="Lec7_files/figure-html/unnamed-chunk-26-1.png" width="50%" height="50%" />
---
# Plotting functions

```r
x <- seq(-2*pi, 2*pi, len = 100)
y <- sin(x)
plot(y ~ x, type="l") # type="1" smooths the line
lines(cos(x) ~ x, type="l", col="blue") # "lines" adds to an existing plot
legend("topright", lty = 1, col = c("black", "blue"), legend = c("sin(x)", "cos(x)"))
```

<img src="Lec7_files/figure-html/unnamed-chunk-27-1.png" width="50%" height="50%" />
---
# Your turn
*Hint*: Use `type`, `lty`, `lwd`, and `col` parameters.
Remember to check out `?par`.

![](Lec7_files/figure-html/unnamed-chunk-28-1.png)