Introduction to

class: center, middle, inverse, title-slide

.title[
# Introduction to <br><br> <img src="img/ggplot2-logo.png" width="175" />
]
.author[
### <br><br><br>Jason Thomas <br> <br>R Working Group
]
.date[
### <br>October 25th, 2024
]

---

# Motivation

* Plots are dope!  (no motivation necessary)

* OK, but why use ggplot2?

+ peer pressure?  everybody is doing it <br>

(lots of examples on the web & extensions)

+ efficiency -- same tool for many different plots

+ does a pretty good job

---
# Outline

* Basic logic & concepts

* Tour of different "types" of plots (`geoms`)

+ examples for setting colors & faceting

* Grouped data

* Labels, Axes, & Themes

* Saving

---
# R packages

Several packages are used for making the plots we'll see today.  You can
install them with...

``` r
install.packages(c("tidyverse", "RColorBrewer", "viridis",  # color
                   "gridExtra"))                            # layout
# only need to run this one time on your computer
# (may need to install again if you upgrade R to a new version)
```

<p style="font-size:20px; text-align:center">
("palletteer" is another package for adding different color options) <br>
</p>

and load them with

``` r
library(tidyverse)  ## this will load ggplot2
library(ggplot2)    ## not needed if loading tidyverse
library(RColorBrewer)
library(viridis)
library(gridExtra)
```

---
class: inverse, center, middle

# Basic Logic & Concepts

---
background-image: url("img/anatomy.png")
background-color: white

<p style="color:red; font-weight:bold; font-size:32px; position:absolute; top:0">Anatomy of a Plot</p>

---
class: slide-font-25
# Anatomy of a plot

The main function for creating a plot is `ggplot()`,  which makes use of...

* **Data**

* **Aesthetics** -- `aes()` creates mappings of variables/values to objects appearing on the plot

* **Layer(s)** consisting of

+ **Geometric** objects -- `geom` determines the type of plot
  (e.g., scatterplot or barchart) and get added as a layer with <br>
  `ggplot(data, aes()) + geom_xxxx()`
  
  + **Statistical** transformation, e.g., line fitted to a cloud of points
  
  + position adjustment for overlapping geoms/objects
  
  + (more data & aesthetic mapping)

---
# Anatomy of a plot (cont.)

* **Scales & coordinate systems** 
 
  + `coord` is usually left out since the Cartesian system handles most cases...
  except maps!
  
  + scaling is largely done under the hood, but emerges when setting colors

* **Faceting** -- using a 3rd "dimension" (plotting subsets of the data)

* **Plot annotations** -- labeling and adjusting the theme

---
# Aesthetics

Each aesthetic can be mapped to a variable (or constant) using `aes()`
or within geoms

* vertical & horizontal position: `aes(x, y)`
* size -- all geoms
* color -- all geoms
* shape -- `geom_point()`
* linetype & linewidth -- `geom_line()`
* fill -- `geom_bar()`
* label -- `geom_text()`

---
# Layering in action

``` r
ggplot(data = mtcars)
```

---
# Layering in action

``` r
ggplot(data = mtcars,
       mapping = aes(x = mpg, y = wt))
```

<div class="figure" style="text-align: center">
<img src="ggplot2_np_files/figure-html/unnamed-chunk-4-1.png" alt="(mapping too)" width="43%" />
<p class="caption">(mapping too)</p>
</div>

---
# Layering in action

``` r
ggplot(data = mtcars,
       mapping = aes(x = mpg, y = wt)) + ## don't forget the '+' !!
  geom_point()
```

---
# Structure of a ggplot

``` r
p <- ggplot(mtcars, aes(x = mpg, y = wt, color=factor(cyl))) + 
  geom_point()
names(p)
```

```
##  [1] "data"        "layers"      "scales"      "guides"      "mapping"    
##  [6] "theme"       "coordinates" "facet"       "plot_env"    "layout"     
## [11] "labels"
```

``` r
summary(p)
```

```
## data: mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb [32x11]
## mapping:  x = ~mpg, y = ~wt, colour = ~factor(cyl)
## faceting: <ggproto object: Class FacetNull, Facet, gg>
##     compute_layout: function
##     draw_back: function
##     draw_front: function
##     draw_labels: function
##     draw_panels: function
##     finish_data: function
##     init_scales: function
##     map_data: function
##     params: list
##     setup_data: function
##     setup_params: function
##     shrink: TRUE
##     train_scales: function
##     vars: function
##     super:  <ggproto object: Class FacetNull, Facet, gg>
## -----------------------------------
## geom_point: na.rm = FALSE
## stat_identity: na.rm = FALSE
## position_identity
```

---
# Global vs. Local

``` r
ggplot(mtcars, aes(x = mpg, y = wt, color=factor(cyl))) +
  geom_point() + geom_smooth(method="lm")
```

---
# Global vs. Local

``` r
ggplot(mtcars, aes(x = mpg, y = wt)) +
  geom_point(aes(color = factor(cyl))) + geom_smooth(method="lm")
```

---
# A few more aesthetics

``` r
ggplot(mtcars, aes(x = mpg, y = wt,
                   color = factor(cyl),
*                  shape = factor(cyl))) +
* geom_point(size = 3)
```

---
# A few more aesthetics

``` r
ggplot(mtcars, aes(x = mpg, y = wt,
                   color = factor(cyl),
                   shape = factor(cyl),
*                  size = cyl)) +
  geom_point()
```

---
# A few more aesthetics

* Note that when a mapping is specified inside `aes()`, we get an
extra legend that describes this mapping

+ we could have set the mapping in the geom: <br>
  `geom_point(aes(size = cyl))`
  
* If we set the size outside of `aes()`, then it is not considered
a mapping (so no legend)

We'll return to a similar situation with color in a short bit...

---
# A few more aesthetics

``` r
ggplot(mtcars, aes(x = mpg, y = wt, color = factor(cyl))) +
  geom_point() + 
  geom_hline(yintercept = mean(mtcars$wt),
*            linewidth = 2)
```

---
# A few more aesthetics

So what is this doing?  How can we improve it?

``` r
ggplot(mtcars, aes(x = mpg, y = wt, color = factor(cyl))) +
  geom_point() + 
  geom_hline(yintercept = mean(mtcars$wt[mtcars$cyl == 4]), linetype = 1) + 
  geom_hline(yintercept = mean(mtcars$wt[mtcars$cyl == 6]), linetype = 2) + 
  geom_hline(yintercept = mean(mtcars$wt[mtcars$cyl == 8]), linetype = 3) 
```

---
# A few more aesthetics

---
# A few more aesthetics

Improvement?

``` r
ggplot(mtcars, aes(x = mpg, y = wt, color = factor(cyl))) +
  geom_point() + 
  geom_hline(aes(yintercept = mean(wt), linetype = factor(cyl)))
```

---
# A few more aesthetics

Biscuits!

---
# A few more aesthetics

Roll these dice? (try to guess what this code will do)

``` r
df_mean_wt <- mtcars %>% group_by(cyl) %>% summarize(mean_wt = mean(wt))
ggplot(mtcars, aes(x = mpg, y = wt, color = factor(cyl))) +
  geom_point() + 
  geom_hline(aes(yintercept = mean_wt, linetype = factor(cyl)),
*            data = df_mean_wt)
```

<br> (output on the next slide)

---
# A few more aesthetics

Thanks dplyr!!

---
# Exercise

Can you improve the previous plot by color coding the horizontal lines?
<br><br>
(Here is the code to produce the previous plot)

---
class: codefs-50
# A few more aesthetics

``` r
rando <- data.frame(x = runif(10), y = runif(10))
ggplot(rando, aes(x = x, y = y)) + geom_point() + 
  geom_abline(intercept = 0, slope = 1, linetype = 2) # another useful line
```

---
class: slide-font-28
# More on color

* Easy to set colors manually (or using a grey scale)
 
  + The R function `colors()` will print out over 600 options

* There are several different tools for setting colors & ggplot2...

+ need to install packages (e.g. [RColorBrewer](https://cran.r-project.org/package=RColorBrewer),
  [viridis](https://cran.r-project.org/web/packages/viridis/index.html),
  [paletteer](https://cran.r-project.org/web/packages/paletteer/index.html))

+ [useful reference with examples](https://r-graph-gallery.com/ggplot2-color.html)
  
  + Viridis gives color scales that are easier to read by those with colorblindness

* Your options depend on if you are mapping color to a continuous variable (e.g.,
gradients) or categorical variable (e.g. color brewer)

---
# Setting color: manually

``` r
ggplot(mtcars, aes(x = mpg, y = wt, color = factor(cyl))) + geom_point() +
  geom_vline(aes(xintercept = mean(mpg)),
*            color = "red")
```

---
# Setting color: notes

* When you want to map a color to a variable, then specify the `color` option
*inside* of `aes()`

* If you to set a color to a constant value then use the `color` option
*outside* of `aes()` and within the `geom`

* When you want to *change* the colors mapped to a variable, then there are
several different tools we can use...

+ (you may need to install the corresponding R package for some of these)

---
# Setting color: manually

``` r
ggplot(mtcars, aes(x = mpg, y = wt, color = factor(cyl))) + 
  geom_point() +
* scale_color_manual(values = c("green", "red", "blue"))
```

---
# Setting color: grey scale

``` r
ggplot(mtcars, aes(x = mpg, y = wt, color = factor(cyl))) + geom_point() + 
* scale_color_grey(start = 0.0, end = 0.4)
```

---
class: codefs-50
# Setting color: gradients

``` r
ggplot(mtcars, aes(x = mpg, y = wt, color = cyl)) + # cyl now continuous
  geom_point() + 
* scale_color_gradient(low="red", high="blue")
```

---
class: slide-font-20
# Setting color: color brewer

[R Color Brewer Palettes](https://r-graph-gallery.com/38-rcolorbrewers-palettes.html)

``` r
ggplot(mtcars, aes(x = mpg, y = wt, color = factor(cyl))) + geom_point() +
* scale_color_brewer(palette = "BuPu")
```

---
class: slide-font-20
# Setting color: Viridis

[Viridis](https://cran.r-project.org/web/packages/viridis/vignettes/intro-to-viridis.html)

``` r
ggplot(mtcars, aes(x = mpg, y = wt, color = factor(cyl))) + geom_point() +
* scale_color_viridis(discrete = TRUE, option = "magma")
```

---
class: inverse, center, middle

# Tour of `geoms`

[list of geoms](https://ggplot2.tidyverse.org/reference/#geoms)

---
# Tour of geoms: barchart

``` r
ggplot(mtcars, aes(x = cyl)) + 
* geom_bar()
```

---
# Tour of geoms: barchar (ordered)

``` r
ggplot(mtcars, aes(x = fct_infreq(factor(cyl)))) +  # need tidyverse for this
  geom_bar()
```

---
# Tour of geoms: barchart (custom)

``` r
mtcars %>% mutate(cyl2 = factor(cyl, levels = c(6, 4, 8))) %>%
  ggplot(aes(x = cyl2)) + geom_bar()
```

---
# Tour of geoms: barchart

``` r
ggplot(mtcars, aes(x = cyl)) + 
* geom_bar(fill = c("blue", "pink4", "green"))
```

---
# Tour of geoms: barchart

``` r
ggplot(mtcars, aes(x = factor(cyl), fill = factor(cyl))) +  geom_bar() +
* scale_color_brewer(palette = "Pastel1")
```

---
# Tour of geoms: boxplot

``` r
ggplot(mtcars, aes(x = factor(cyl), y = mpg, color = factor(cyl))) +
  geom_boxplot()
```

---
# Tour of geoms: boxplot

``` r
ggplot(mtcars, aes(x = factor(cyl), y = mpg, fill = factor(am))) +
  geom_boxplot()
```

---
# Tour of geoms: text

``` r
ggplot(mtcars, aes(x = wt, mpg,
*                  label = rownames(mtcars))) +
* geom_text()
```

---
# Tour of geoms: text

``` r
ggplot(mtcars, aes(x = wt, mpg, label = rownames(mtcars))) + geom_point() +
* geom_text(hjust = "right", nudge_x = -0.15)
```

---
# Tour of geoms: text

``` r
ggplot(mtcars, aes(x = wt, mpg, label = rownames(mtcars))) + geom_point() +
* geom_text(vjust = 0, nudge_y = 0.5)
```

---
# Tour of geoms: different text

``` r
ggplot(mtcars, aes(x = factor(cyl))) +  geom_bar() +
  annotate("text", label = c("4 cyl", "6 cyl", "8 cyl"),
           x = 1:3, y = max(table(mtcars$cyl)) + 2, col = "red")
```

---
# Tour of geoms: density

``` r
ggplot(mtcars, aes(mpg)) + geom_density()
```

---
# Tour of geoms: density

``` r
ggplot(mtcars, aes(mpg, fill=factor(cyl))) + geom_density()
```

---
# Tour of geoms: density

``` r
ggplot(mtcars, aes(mpg, fill=factor(cyl))) +
* geom_density(alpha = 0.5)
```

---
# Tour of geoms: histogram

``` r
ggplot(mpg, mapping = aes(x = hwy)) +
  geom_histogram(binwidth = 5)
```

---
# Tour of geoms: histogram

``` r
mpg %>% filter(manufacturer == "audi") %>%
  ggplot(aes(x=hwy)) + geom_histogram(binwidth = 1)
```

---
# Tour of geoms: histogram

``` r
mpg %>% filter(manufacturer == "audi") %>%
  ggplot(aes(x=log(hwy))) + geom_histogram(binwidth = .01)
```

---
# Tour of geoms: histogram

``` r
ggplot(mpg, aes(x=log(hwy))) + geom_histogram(binwidth = .01) +
* facet_wrap(vars(manufacturer, year))
```

---
# More with Faceting

* Possible to specify number of rows (and columns) with faceting

* Can combine previous trick with summarized data set

``` r
p <- ggplot(mtcars, aes(mpg, wt)) +
  geom_point() +
* facet_wrap(~ cyl, nrow=2)
df_wt <- mtcars %>% group_by(cyl) %>% summarize(avg_wt = mean(wt)) %>% as.data.frame()
p + geom_hline(aes(yintercept = avg_wt), data=df_wt)
```

<br> (output on the next slide)

---
# More with Faceting

---
class: inverse, center, middle

# Grouped data

---
# Nested data?

``` r
data("ChickWeight")  ## let's look at the data
```

<div class="datatables html-widget html-fill-item" id="htmlwidget-00a057a1971f45338073" style="width:100%;height:auto;"></div>
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---
# Nested data?

How does the weight vary over time?

``` r
ggplot(ChickWeight, aes(Time, weight)) + geom_line()
```

<div class="figure" style="text-align: center">
<img src="ggplot2_np_files/figure-html/unnamed-chunk-49-1.png" alt="uhhhh...that ain't it" width="40%" />
<p class="caption">uhhhh...that ain't it</p>
</div>

---
# Group option

Adding `group = Chick` to the aesthetic mapping...

``` r
ggplot(ChickWeight, aes(Time, weight, group = Chick)) + geom_line()
```

<div class="figure" style="text-align: center">
<img src="ggplot2_np_files/figure-html/unnamed-chunk-50-1.png" alt="Bingo!" width="40%" />
<p class="caption">Bingo!</p>
</div>

---
# Group option

Adding a layer with a different "grouping" (all Chicks together)

``` r
ggplot(ChickWeight, aes(Time, weight, group = Chick)) + geom_line() +
  geom_smooth(aes(group = 1), linewidth = 2, se = FALSE)
```