Colors/Themes + Inclusive Design Principles

class: center, middle, inverse, title-slide

.title[
# Colors/Themes + Inclusive Design Principles
]
.author[
### Maithreyi Gopalan
]
.date[
### Week 4
]

---

layout: true

---
# Agenda
* Learn about C.R.A.P
* Color basics
  + 3 basic ways color is used
* Color blindness
* Some common problems with color use
* Quick discussion of palettes
* Quick discussion of themes
* Lab 4
* Q & A with Dr. Daniel Anderson, who pioneered this EDS specialization and class (next week)!

---
# Learning Objectives
* Learn a bit more about good/inclusive design principles 
* Understand different types of color palettes
  + ...and when you should use one versus another
* Understand and be able to effectively evaluate concerns related to color
blindness
* Be able to fluently change colors/fills within ggplot

---
# Universal principles

* Contrast

* Repetition

* Alignment

* Proximity

+ Pioneered by [Robin Patricia Williams](https://clueify.com/blog/crap-design-principles/)
---

* These design principles apply everywhere!

* Graphic design, art, music, architecture… and graphs!

---

# Is that gray background okay?

``` r
theme_set(theme_gray())
ggplot(mpg, aes(x = displ, y = hwy, color = drv)) +
  geom_point(size = 2)
```

``` r
  theme(gray)
```

```
##  Named list()
##  - attr(*, "class")= chr [1:2] "theme" "gg"
##  - attr(*, "complete")= logi FALSE
##  - attr(*, "validate")= logi TRUE
```

+ It adds contrast! Some people (including me) just don't like it!!!

---

# Applying CRAP to ggplot

* We can follow CRAP principles to make big improvements to our plots

* Claus Wilke's chapter covers lots of these graph-specific principles

* We can apply these principles to ggplot plots

---

# Like this!

.pull-left[

``` r
library(hrbrthemes)

ggplot(mpg, aes(x = displ, y = hwy, 
                color = drv)) +
  geom_point(size = 2) +
  theme_ipsum_ps()
```
]

.pull-right[
![](w4_files/figure-html/hrbrlight-1.png)
]

---

# And this!

.pull-left[

``` r
library(hrbrthemes)

ggplot(mpg, aes(x = displ, y = hwy, 
                color = drv)) +
  geom_point(size = 2) +
  theme_modern_rc()
```
]

.pull-right[
![](w4_files/figure-html/hrbrdark-1.png)
]

---

# Or this!

.pull-left[

``` r
library(ggthemes)

ggplot(mpg, aes(x = displ, y = hwy, 
                color = drv)) +
  geom_point(size = 2) +
  scale_color_economist() +
  theme_economist()
```
]

.pull-right[
![](w4_files/figure-html/economist-1.png)
]

---

# And even this!

.pull-left[

``` r
#remotes::install_github("gadenbuie/ggpomological")
library(ggpomological)

ggplot(mpg, aes(x = displ, y = hwy, 
                color = drv)) +
  geom_point(size = 2) +
  scale_color_pomological() +
  theme_pomological_fancy()
```
]

.pull-right[
![](w4_files/figure-html/pomological-1.png)
]

---

# One magic, powerful function

`theme()`

+ Depending on time we come back to an anatomy of `theme` the end today or next week!

---
# Before we get too deep into themes of which colors is an essential element...
### Some very practical advice about colors
* Keep straight when color is mapped to a variable through `aes` and when it's
modifying an element overall

+ Former requires `scale_color_*` or `scale_fill_*` while the latter does not

--
* Keep straight colors and fills (see former bullet)

--
* Use advice of others to your advantage (e.g., http://colorbrewer2.org/)

---
class: inverse bottom center
background-image:url(http://socviz.co/assets/ch-01-luminance-contrast-color.png)
background-size:contain

# Why color choice matters

---
class: inverse bottom center
background-image:url(http://socviz.co/assets/ch-01-luminance-contrast-bw.png)
background-size:contain

# Why color choice matters

---
class: inverse-red middle
# Another quick example

.realbig[
.middle[
.center[
[{rayshader}](https://resources.rstudio.com/rstudio-conf-2019/3d-mapping-plotting-and-printing-with-rayshader)
]
]
]

---
# 3 fundamental uses of color

--
1. Distinguish groups from each other

--
1. Represent data values

--
1. Highlight

---
class:inverse-blue middle

# Color as a tool to distinguish

---
# Discrete items
* Often no intrinsic order

--
### Qualitative color scale
* Finite number of colors
	+ Chosen to maximize distinctness, while also be equivalent
	+ Equivalent
		- No  color should stand out
		- No impression of order

---
###Some Examples 
![](img/someexamples.png)
		
.footnote[See more about the Okabe Ito palette origins [here](https://easystats.github.io/see/reference/scale_color_okabeito.html)
]
---
# How do we use them?
Imagine we have data like this

``` r
popgrowth_df
```

```
## # A tibble: 51 × 7
##   region    division           state          pop2000  pop2010 popgrowth   area
##   <fct>     <chr>              <fct>            <dbl>    <dbl>     <dbl>  <dbl>
## 1 Midwest   East North Central Michigan       9938444  9883640  -0.00551 56539.
## 2 Northeast New England        Rhode Island   1048319  1052567   0.00405  1034.
## 3 South     West South Central Louisiana      4468976  4533372   0.0144  43204.
## 4 Midwest   East North Central Ohio          11353140 11536504   0.0162  40861.
## 5 Northeast Middle Atlantic    New York      18976457 19378102   0.0212  47126.
## 6 South     South Atlantic     West Virginia  1808344  1852994   0.0247  24038.
## # ℹ 45 more rows
```

---
# Maybe a plot like this

.pull-left[

``` r
ggplot(popgrowth_df, 
       aes(x = popgrowth,
           y = state)) + 
  geom_col(alpha = 0.9)
```
]

.pull-right[
![](w4_files/figure-html/unnamed-chunk-3-1.png)
]

---
# Alternatively, fill by region

.pull-left[

``` r
ggplot(popgrowth_df, 
       aes(x = popgrowth,
           y = state)) + 
  geom_col(aes(fill = region),
           alpha = 0.9)
```
]

.pull-right[
![](w4_files/figure-html/unnamed-chunk-4-1.png)
]

---
# Problem with default palette

![](w4_files/figure-html/colorblind1-1.png)

---
# Alternative: viridis

.pull-left[

``` r
ggplot(popgrowth_df, 
       aes(x = popgrowth,
           y = state)) + 
  geom_col(aes(fill = region),
           alpha = 0.9) + 
* scale_fill_viridis_d()
```
]

.pull-right[
![](w4_files/figure-html/unnamed-chunk-5-1.png)
]

---
# Revised version

![](w4_files/figure-html/unnamed-chunk-6-1.png)

---
# The Okabe Ito palette

.pull-left[

* From [Color Universal Design](https://easystats.github.io/see/reference/scale_color_okabeito.html)

``` r
*library(colorblindr)

ggplot(popgrowth_df, 
       aes(x = popgrowth,
           y = state)) + 
  geom_col(aes(fill = region),
           alpha = 0.9) +
* scale_fill_OkabeIto()
```
]

.pull-right[
![](w4_files/figure-html/unnamed-chunk-7-1.png)
]

---
# Okabe Ito for colorblindness

![](w4_files/figure-html/unnamed-chunk-8-1.png)

---
# How am I checking for colorblindness?
* Also part of the **{colorblindr}** package ([here](https://github.com/clauswilke/colorblindr))
  + depends on the dev versions of **{colorspace}** and **{cowplot}**, which are useful packages in their own right

``` r
#devtools::install_github("wilkelab/cowplot")
#install.packages("colorspace", repos = "http://R-Forge.R-project.org")

#devtools::install_github("clauswilke/colorblindr")
```

---
# A note on installation

Occasionally people have run into issues with the install on the previous slide.

If that happens, try `install.packages("colorBlindness")` instead, and use the `cvdPlot` function.

(It's just a modified version of `cvd_grid` from *colorblindr*)

---

``` r
p <- ggplot(popgrowth_df, 
       aes(x = popgrowth,
           y = state)) + 
  geom_col(aes(fill = region),
           alpha = 0.9) +
  scale_fill_OkabeIto() + 
* theme_void() # not necessary but I like it

*colorblindr::cvd_grid(p)
```

![](w4_files/figure-html/okabe-ito2-1.png)

---
class: inverse-orange middle

# Colors for continuous values

---
# Sequential scale examples

![](img/sequentialexamples.png)

---
# Sequential scales
* Which values are larger/smaller

--
* How distant two values are from each other

--
	+ Scale must be perceptually uniform across its entire range

--
	+ Similar to an interval scale, but for color

--
* Often based on a single .bolder[hue]

--
* Multi-hue sequential scales tend to follow gradients in the natural world

---
class: inverse-red middle
# Common uses of sequential palettes

---
# Heatmaps

First the data:

``` r
hm <- diamonds %>% 
  select(table, price, depth, carat) %>% 
  corrr::correlate() %>% 
  pivot_longer(-term) %>% 
  mutate(name = fct_reorder(name, value),
         term = fct_reorder(term, value))
hm
```

```
## # A tibble: 16 × 3
##   term  name   value
##   <fct> <fct>  <dbl>
## 1 table table NA    
## 2 table price  0.127
## 3 table depth -0.296
## 4 table carat  0.182
## 5 price table  0.127
## 6 price price NA    
## # ℹ 10 more rows
```

---

``` r
ggplot(hm, aes(name, term)) +
  geom_tile(aes(fill = value)) +
  coord_fixed() 
```

![](w4_files/figure-html/unnamed-chunk-10-1.png)

---

``` r
ggplot(hm, aes(name, term)) +
  geom_tile(aes(fill = value)) +
  coord_fixed() +
*scale_fill_distiller(palette = "Blues")
```

![](w4_files/figure-html/heatmap2-1.png)

---
# Change the NA value

In any `scale_*` you can change the `NA` value, including to `"transparent"`.

``` r
ggplot(hm, aes(name, term)) +
  geom_tile(aes(fill = value)) +
  coord_fixed() +
	scale_fill_distiller(palette = "Blues",
*                     na.value = "#b0bfb0")
```

![](w4_files/figure-html/heatmap2b-1.png)

---
# Use this to our advantage

``` r
hm2 <- diamonds %>% 
  select(table, price, depth, carat) %>% 
  corrr::correlate() %>% 
* corrr::shave(upper = FALSE) %>%
  pivot_longer(-term)
```

---

``` r
hm2_default <- ggplot(hm2, aes(name, term)) +
  geom_tile(aes(fill = value), color = "gray70") +
  coord_fixed() +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank())
hm2_default
```

![](w4_files/figure-html/unnamed-chunk-12-1.png)

---

``` r
hm2_default +
	scale_fill_viridis_c(
*  option = "magma",
*  na.value = "transparent"
  ) 
```

![](w4_files/figure-html/heatmap4-1.png)

`option = c("viridis", "magma", "inferno", "plasma")`

---
# Choropleths

``` r
library(tidycensus)
library(tigris)
options(tigris_use_cache = TRUE)
options(tigris_class="sf")

#census_api_key("Insert your key", install=TRUE)

vars_pl_2020 <-  load_variables(2020, "pl")
#View(vars)
```

![](w4_files/figure-html/lane1-1.png)

---
# Heat palette 
.pull-left[

``` r
lane %>%
  mutate(pct = 100 * (value / summary_value)) %>%
  ggplot(aes(fill = pct, color = pct)) +
  geom_sf() +
  coord_sf(crs = 26915) + 
  theme_dviz_map(font_size = 25, font_family = "Roboto") +
  scale_fill_continuous_sequential("Heat") +
  scale_color_continuous_sequential("Heat") +
  labs(title = "Percentage of people identifying as White",
       subtitle = "Lane County",
       caption = "US Census Decennial Tract Data")
```

![](w4_files/figure-html/lane2-1.png)
]
.pull-right[
![](w4_files/figure-html/lane2-1.png)
]
---
# Options
* `scale_fill_continuous_sequential("Heat")` 
* `scale_color_continuous_sequential("Heat")` 
* `scale_fill_discrete_sequential("Heat")`
* `scale_color_discrete_sequential("Heat")`

---
# viridis palette

![](w4_files/figure-html/lane3-1.png)

---

# Diverging palettes

![](img/divergingexamples.png)

---
# Earth palette

![](w4_files/figure-html/or1-1.png)

---
![](w4_files/figure-html/ca1-1.png)

---
# Back to our heatmap
Use a diverging palette that balances at zero.

Notice the transparency is now a bit problematic...

``` r
library(colorspace)
hm2_default +
  scale_fill_continuous_divergingx(
    palette = "Tropic",
    mid = 0,
    limits = c(-1, 1),
    rev = TRUE,
    na.value = "transparent"
  )
```

---
![](w4_files/figure-html/unnamed-chunk-14-1.png)

---
# Try a different palette
Most diverging palettes have a light gray or white center point. Some have black. Let's try one of those.

``` r
hm2_default +
  scale_fill_continuous_diverging(
    palette = "Tofino",
    mid = 0,
    limits = c(-1, 1),
    rev = TRUE,
    na.value = "transparent"
  )
```

---
![](w4_files/figure-html/unnamed-chunk-16-1.png)

--
I don't love it

---
# One more try!
Back to our last palette, but change the `NA` value to a light gray

``` r
hm2_default +
  scale_fill_continuous_divergingx(
    palette = "Tropic",
    mid = 0,
    limits = c(-1, 1),
    rev = TRUE,
    na.value = "gray70"
  )
```

---
![](w4_files/figure-html/unnamed-chunk-18-1.png)

--
Maybe better? I give up for now...

---
# More on colorspace

Great package with great documentation. Let's go look!

[colorspace](http://colorspace.r-forge.r-project.org/)

---
# More diagnostics

``` r
our_scale <- diverge_hcl(5, palette = "Tropic", rev = TRUE) 
demoplot(our_scale, "bar")
```

![](w4_files/figure-html/unnamed-chunk-20-1.png)

---

``` r
demoplot(our_scale, "heatmap")
```

![](w4_files/figure-html/unnamed-chunk-21-1.png)

---

``` r
demoplot(our_scale, "map")
```

![](w4_files/figure-html/unnamed-chunk-22-1.png)

---

``` r
demoplot(our_scale, "lines")
```

![](w4_files/figure-html/unnamed-chunk-23-1.png)

---

``` r
demoplot(our_scale, "scatter")
```

![](w4_files/figure-html/unnamed-chunk-24-1.png)

---
# Diferent background

Note - this is base graphics

``` r
par(bg = "gray20", cex = 3, mar = rep(0, 4))
demoplot(our_scale, "scatter")
```

![](w4_files/figure-html/unnamed-chunk-25-1.png)

---

``` r
hclplot(our_scale)
```

![](w4_files/figure-html/unnamed-chunk-26-1.png)

---

``` r
specplot(our_scale, type = "o")
```

![](w4_files/figure-html/unnamed-chunk-27-1.png)

---
# Check colorblindness

We can check for our palette, in addition to our figure overall

``` r
swatchplot("Our palette" = our_scale, cvd = TRUE)
```

![](w4_files/figure-html/unnamed-chunk-28-1.png)

---
# Interactivity
You can do this all online interactively with the demo plots

http://hclwizard.org:3000/hclwizard/

--
## CVD Emulator online

http://hclwizard.org:3000/cvdemulator/

---
class: inverse-blue center middle
# Color as a tool to highlight

---
# MPG data
Basic scatterplot of weight to highway mpg

``` r
ggplot(mpg, aes(displ, hwy)) +
	geom_point()
```

![](w4_files/figure-html/basic-scatter-1.png)

---
# Highlight compact cars

``` r
ggplot(mpg, aes(displ, hwy)) +
	geom_point(color = "gray80") +
	geom_point(data = filter(mpg, class == "compact"),
	           color = "#C55644")
```

![](w4_files/figure-html/compact-cars-scatter-1.png)

---
# Highlight manual cars

``` r
ggplot(mpg, aes(displ, hwy)) +
	geom_point(color = "gray80") +
	geom_point(data = filter(mpg, str_detect(trans, "manual")),
	           color = "#C55644")
```

![](w4_files/figure-html/compact-cars-scatter-h1-1.png)

---
# Add a legend
Couple of different ways - mostly hacky

* Pretend you have a column called "compact"

``` r
ggplot(mpg, aes(displ, hwy)) +
	geom_point(color = "gray80") +
	geom_point(data = filter(mpg, class == "compact"),
	           aes(color = "compact")) +
  scale_color_manual(values = "#C55644") +
  theme(
    legend.title = element_blank(),
    legend.position = c(0.7, 0.9),
    legend.box.background = element_rect(colour = "black"),
    legend.box.margin = margin(t = -0.5, unit = "cm")
  )
```

---
![](w4_files/figure-html/unnamed-chunk-30-1.png)

---
# Back to our states plot

### Highlight Oregon and Washington

``` r
#popgrowth_df <- popgrowth_df %>%
 # arrange(desc(popgrowth)) %>%
 # mutate(state = factor(state, levels = state))

ggplot(popgrowth_df, aes(x=popgrowth, y=state)) + 
  geom_col(aes(fill = region), alpha = 0.9) + 
  geom_col(data=filter(popgrowth_df, state == 'Oregon' | state == 'Washington'), 
           fill = NA, 
           color = '#C55644',
           linewidth = 1,
           width = 0.9) +  # Added width parameter to make it slightly narrower
  scale_fill_OkabeIto()
```

---

![](w4_files/figure-html/basic-highlight-or-eval-1.png)

```
## List of 1
##  $ axis.text.y:List of 11
##   ..$ family       : NULL
##   ..$ face         : NULL
##   ..$ colour       : NULL
##   ..$ size         : num 11
##   ..$ hjust        : NULL
##   ..$ vjust        : NULL
##   ..$ angle        : NULL
##   ..$ lineheight   : NULL
##   ..$ margin       : NULL
##   ..$ debug        : NULL
##   ..$ inherit.blank: logi FALSE
##   ..- attr(*, "class")= chr [1:2] "element_text" "element"
##  - attr(*, "class")= chr [1:2] "theme" "gg"
##  - attr(*, "complete")= logi FALSE
##  - attr(*, "validate")= logi TRUE
```

---
# Color labels

``` r
states <- unique(popgrowth_df$state)

label_color <- ifelse(states == "Oregon" | states == "Washington", 
                "#C55644",
                "gray30")
label_color
```

```
##  [1] "gray30"  "gray30"  "gray30"  "gray30"  "gray30"  "gray30"  "gray30"  "gray30"  "gray30"  "gray30"  "gray30"  "gray30"  "gray30"  "gray30" 
## [15] "gray30"  "gray30"  "gray30"  "gray30"  "gray30"  "gray30"  "gray30"  "gray30"  "gray30"  "gray30"  "gray30"  "gray30"  "gray30"  "gray30" 
## [29] "gray30"  "gray30"  "gray30"  "gray30"  "gray30"  "#C55644" "gray30"  "gray30"  "gray30"  "gray30"  "#C55644" "gray30"  "gray30"  "gray30" 
## [43] "gray30"  "gray30"  "gray30"  "gray30"  "gray30"  "gray30"  "gray30"  "gray30"  "gray30"
```

``` r
label_face <- ifelse(states == "Oregon" | states == "Washington", 
                "bold",
                "plain")

label_face
```

```
##  [1] "plain" "plain" "plain" "plain" "plain" "plain" "plain" "plain" "plain" "plain" "plain" "plain" "plain" "plain" "plain" "plain" "plain" "plain"
## [19] "plain" "plain" "plain" "plain" "plain" "plain" "plain" "plain" "plain" "plain" "plain" "plain" "plain" "plain" "plain" "bold"  "plain" "plain"
## [37] "plain" "plain" "bold"  "plain" "plain" "plain" "plain" "plain" "plain" "plain" "plain" "plain" "plain" "plain" "plain"
```

---

``` r
ggplot(popgrowth_df, aes(x=popgrowth, y=state)) + 
  geom_col(aes(fill = region), alpha = 0.9) + 
  geom_col(data=filter(popgrowth_df, state == 'Oregon' | state == 'Washington'), 
           fill = NA, 
           color = '#C55644',
           linewidth = 1,
           width = 0.9) +  # Added width parameter to make it slightly narrower
  scale_fill_OkabeIto()+
  theme(
    axis.text.y = element_text(
*     color = label_color,
*     face = label_face
    )
  ) 
```

---
![](w4_files/figure-html/orwa-highlight-eval-1.png)

---
# Even better

``` r
accent_OkabeIto <- palette_OkabeIto[c(1, 2, 7, 4, 5, 3, 6)]
accent_OkabeIto[1:4] <- desaturate(lighten(accent_OkabeIto[1:4], .4), .8)
accent_OkabeIto[5:7] <- darken(accent_OkabeIto[5:7], .3)
gg_color_swatches(7) +
	scale_fill_manual(values = accent_OkabeIto)
```

![](w4_files/figure-html/unnamed-chunk-31-1.png)

---

``` r
ggplot(popgrowth_df, aes(x=popgrowth, y=state)) + 
  geom_col(aes(fill = region), alpha = 0.9) + 
  geom_col(data=filter(popgrowth_df, state == 'Oregon' | state == 'Washington'), 
           fill = NA, 
           color = '#C55644',
           linewidth = 1,
           width = 0.9) +  
* scale_fill_manual(values = accent_OkabeIto) +
  theme(axis.text.y = element_text(color = label_color,
                                   face = label_face)) 
```

---
![](w4_files/figure-html/orwa-highlight-eval2-1.png)

---
# Or even better (imo)

``` r
*library(ggtext)
ggplot(popgrowth_df, aes(x=popgrowth, y=state)) + 
  geom_col(aes(fill = region), alpha = 0.9) + 
  geom_col(data=filter(popgrowth_df, state == 'Oregon' | state == 'Washington'), 
           fill = NA, 
           color = '#C55644',
           linewidth = 1,
           width = 0.9) +  # Added width parameter to make it slightly narrower
  scale_fill_manual(values = accent_OkabeIto) +
* scale_x_continuous(expand = c(0, 0)) +
  labs(title = "Population growth by region",
       subtitle = "The <span style = 'color: #C55644'>**northwest**</span> is where it's at") +
  theme(axis.text.y = element_text(color = label_color, 
                                   face = label_face),
*       plot.subtitle = element_markdown())
```

---

![](w4_files/figure-html/unnamed-chunk-33-1.png)
---
# One last attempt

![](w4_files/figure-html/unnamed-chunk-34-1.png)

---
# Back to cars plot

We can do this same thing and avoid the legend altogether.

``` r
ggplot(mpg, aes(displ, hwy)) +
	geom_point(color = "gray80") +
	geom_point(data = filter(mpg, class == "compact"),
	           color = "#C55644") +
  labs(
    title = "Cars with bigger engines get lower miles per gallon",
    subtitle = "<span style = 'color: #C55644'>**Compact**</span> cars typically have small engines"
  ) +
  theme(plot.subtitle = element_markdown())
```

---
![](w4_files/figure-html/unnamed-chunk-36-1.png)

---
class: inverse-blue
# Data viz in Wild

Erick

Emily

### Saratessa and Sophia on deck

---
# Last example

``` r
data(sleepstudy, package = "lme4")
head(sleepstudy)
```

```
##   Reaction Days Subject
## 1      250    0     308
## 2      259    1     308
## 3      251    2     308
## 4      321    3     308
## 5      357    4     308
## 6      415    5     308
```

---
# Plot by subject

``` r
ggplot(sleepstudy, aes(Days, Reaction, group = Subject)) +
	geom_line() 
```

![](w4_files/figure-html/plot1-1.png)

---

``` r
*library(gghighlight)
ggplot(sleepstudy, aes(Days, Reaction, group = Subject)) +
	geom_line() +
*gghighlight(max(Reaction) > 400)
```

![](w4_files/figure-html/gghighlight1-1.png)

---

``` r
library(gghighlight) 
ggplot(sleepstudy, aes(Days, Reaction, color = Subject)) +
	geom_line() +
	gghighlight(max(Reaction) > 400) + 
*scale_color_OkabeIto()
```

![](w4_files/figure-html/gghighlight2-1.png)

---

``` r
library(gghighlight)
ggplot(sleepstudy, aes(Days, Reaction, color = Subject)) +
	geom_line() +
*facet_wrap(~Subject) +
	gghighlight(max(Reaction) > 400) + 
	scale_color_OkabeIto()
```

![](w4_files/figure-html/gghighlight3-1.png)

---
# Highlighting
The [gghighlight]() package is really neat, but I have had issues with it in the past.

If you have troubles, you can always create them more manually has we did previously

---
# Example
This is a little tricky. First, create a subset of the data that only has the cases you want to highlight

``` r
# filter 
high <- sleepstudy %>% 
  filter(Reaction > 400) %>% 
  select(Subject)

# highlight dataframe
hl <- semi_join(sleepstudy, high)
```

---
Next, create the background data. We usually just *drop* the faceting column, but in this case we still need the id, so we'll rename it.

``` r
# rename facet column; use for grouping
bg_data <- sleepstudy %>% 
  rename(id = Subject)
```

---
Finally, plot the highlight data, adding a layer with the background data

``` r
ggplot(hl, aes(Days, Reaction)) +
  geom_line(
*   aes(group = id),
    color = "gray80",
*   data = bg_data
  ) +
	geom_line(
	  aes(color = Subject)
  ) + 
	facet_wrap(~Subject)
```

---
![](w4_files/figure-html/unnamed-chunk-40-1.png)

---
class: inverse-red middle
# Very quick aside
You can do this with any data - a great alternative to stacked histograms.

---
# Standard faceted histogram

``` r
ggplot(diamonds, aes(depth)) +
  geom_histogram(aes(fill = cut)) +
  facet_wrap(~cut) +
  guides(fill = "none") # drop legend
```

![](w4_files/figure-html/unnamed-chunk-41-1.png)

---
# Add a background layer

``` r
ggplot(diamonds, aes(depth)) +
  geom_histogram(
*   data = select(diamonds, -cut),
*   fill = "gray80"
  ) +
  geom_histogram(aes(fill = cut)) +
  facet_wrap(~cut) +
  guides(fill = "none") # drop legend
```

---
![](w4_files/figure-html/unnamed-chunk-43-1.png)

---
class: inverse-blue center middle
# A few other things to consider

---
# Double encodings

![](w4_files/figure-html/iris-scatter1-1.png)

--
This plot is less than ideal. Why?

---
# Color blindness

![](w4_files/figure-html/color-blind-iris_scatter-1.png)

---
# Better version

![](w4_files/figure-html/iris-scatter-1.png)

---
# Color blindness check

![](w4_files/figure-html/color-blind-iris_scatter2-1.png)

---
class:inverse-blue center middle
# Common problems with color

---
# Too many colors

More than 5-ish categories generally becomes too difficult to track

``` r
ggplot(popgrowth_df, aes(pop2000, popgrowth, color = state)) +
	geom_point()
```

![](w4_files/figure-html/too-many-colors-1.png)

---
# Use labels

More than 5-ish categories generally becomes too difficult to track

``` r
*library(ggrepel)

ggplot(popgrowth_df, aes(pop2000, popgrowth)) +
	geom_point(color = "gray70") +
*geom_text_repel(aes(label = state))
```

![](w4_files/figure-html/states-labeled-1.png)

---
# Better

Get a subset

``` r
to_label <- c("Alaska", "Arizona", "California", "Florida", "Wisconsin", 
              "Louisiana", "Nevada", "Michigan", "Montana", "New Mexico", 
              "Pennsylvania", "New York", "Oregon", "Rhode Island",
              "Tennessee", "Texas", "Utah", "Vermont")
subset_states <- popgrowth_df %>%
	filter(state %in% to_label)
```

---

``` r
*library(ggrepel)

ggplot(popgrowth_df, aes(pop2000, popgrowth)) +
	geom_point(color = "gray70") +
*geom_text_repel(aes(label = state),
*                data = subset_states,
*                min.segment.length = 0)
```

![](w4_files/figure-html/repeled-labels-1.png)

(still lots more cleaning up we could do here...)

---
# Rainbow palette

``` r
rainbow(3)
```

```
## [1] "#FF0000" "#00FF00" "#0000FF"
```

``` r
rainbow(7)
```

```
## [1] "#FF0000" "#FFDB00" "#49FF00" "#00FF92" "#0092FF" "#4900FF" "#FF00DB"
```

---
# Pretty! Doesn't work well
See [here](https://www.poynter.org/archive/2013/why-rainbow-colors-arent-always-the-best-options-for-data-visualizations/) for one (of many) articles on why this is the case

.pull-left[

``` r
ggplot(popgrowth_df, aes(x = popgrowth, y = state)) + 
  geom_col(aes(fill = state)) +
  scale_fill_manual(values = rainbow(51)) + 
  guides(fill = "none") 
```

![](w4_files/figure-html/rainbow-pop-1.png)
]

.pull-right[
![](w4_files/figure-html/rainbow-pop-1.png)
]

---
# Last few notes on palettes
* Do some research, find what you like **and** what tends to work well

* Check for colorblindness

* Look into http://colorbrewer2.org/

+ `scale_color_brewer()` and `scale_fill_brewer()` ship with ggplot2

---
# For example

``` r
ggplot(popgrowth_df, aes(x = popgrowth, y = state)) + 
  geom_col(aes(fill = region),
           alpha = 0.9) +
* scale_fill_brewer(palette = "Set2")
```

---

![](w4_files/figure-html/unnamed-chunk-45-1.png)

---
# Paleteer package

.center[
[![](img/paletteer_logo.png)](https://github.com/EmilHvitfeldt/paletteer)
]

---
class: inverse-red middle

# The anatomy of a `ggplot()` theme

---
# Theme system

![](img/ggplottheme.png)

.footnote[
By [Henry Wang](https://henrywang.nl/ggplot2-theme-elements-demonstration/)
]
---

# Theme elements

* Each element in the plot can be targeted

* Plot title = `plot.title`

* Grid lines = `panel.grid`

* Legend background = `legend.background`]

---

# Theme functions

* Use special functions to manipulate specific elements

* Text-based things = `element_text()`

* Rectangular things (backgrounds) = `element_rect()`

* Line-based things (axis lines, grid lines) = `element_line()`

* Disable element completely = `element_blank()`

---

# How to learn `theme()`

* The `theme()` function has **94** possible arguments(!!!)

* You can get hyper-specific with things like `axis.ticks.length.x.bottom`

* The only way to learn how to use `theme()`<br>is to use it and tinker with it

---
class: inverse-red middle

# Review Lab 3

---
class: inverse-green middle

# Lab 4

---
class: inverse-blue middle

# Q & A with Dr. Daniel Anderson next week

---
class: inverse-green middle

# Next time
More colors, themes, refinement of plots