Data Communication

class: center, middle, inverse, title-slide

.title[
# Data Communication
]
.author[
### MACSS 30500 <br /> University of Chicago
]

---

# Agenda

* Review layers + EDA
* Communicating with Data
  * Labels
  * Annotations
  * Scales
  * Themes
  * Layout

---
class: inverse, middle

# Communicating with data

---
### Setup

For this, we're going to work with these packages -- remember to install them if you haven't yet!

``` r
library(tidyverse)
library(scales)
```

```
## 
## Attaching package: 'scales'
```

```
## The following object is masked from 'package:purrr':
## 
##     discard
```

```
## The following object is masked from 'package:readr':
## 
##     col_factor
```

``` r
library(ggrepel)
library(patchwork)
```

---

## Labels
There are many ways to label your axes -- two primary ways:

* `labs()`: can specify x and y [labs example](https://r4ds.hadley.nz/communication.html#labels)
* `theme()`: can customize with more detail [theme example](https://ggplot2-book.org/themes.html)
  * note: confusingly, this isn't the same as the color theme!

We will mostly use labs in our examples (for now ... theme is particularly helpful for rotating axis text!)

---

### Labels: examples

Here are our baseline options:

``` r
labs(
  x = "",
  y = "", 
  title = waiver(),
  subtitle = waiver(),
  caption = waiver(),
  tag = waiver(),
  alt = waiver(),
  alt_insight = waiver()
)
```

---
## Example: `taylor` package: 
Add labels to this plot's axes:

.panelset[
  .panel[.panel-name[Code]

``` r
library(taylor)
data(eras_tour_surprise) 
ggplot(eras_tour_surprise, aes(x = dress)) + 
  geom_bar(stat = "count", fill = c("deepskyblue", "blue", "deeppink", "darkgreen", "tomato1", 
                                      "steelblue1", "red3", "hotpink2", "chocolate3", "slateblue4", "gold2" )) 
```
]

.panel[.panel-name[Plot]
![](index_files/figure-html/taylor-1-1.png)

(add labs(x = "Suprise Song Dress"))
]
]

---
## Example: `taylor` package: 
Add labels to this plot's axes:

.panelset[
  .panel[.panel-name[Code]

``` r
ggplot(eras_tour_surprise, aes(x = forcats::fct_infreq(dress))) + 
    geom_bar(stat = "count", fill = c("red3","gold2","darkgreen",  "chocolate3" ,  "deeppink", "blue","slateblue4", "steelblue1", "deepskyblue",   "tomato4", "hotpink2")) + labs(title = "Taylor Swift Surprise Song Dress Colors", subtitle = "Eras Tour", caption = 'source: taylor package', x = "Dress", y = "Number of times worn for performance")
```
]

.panel[.panel-name[Plot]
![](index_files/figure-html/taylor-s-1.png)

(advanced: map color names to dresses to avoid sort issues!)
]
]

---

### Info/guidance on titles

* Book suggests only informative titles: re what is occurring
* I disagree
* Context and role: what is your goal and who is your audience

---
## Annotatations
Sometimes you want to add additional elements to your plots!

* `geom_text()`
* `geom_label_repel()`
* `geom_label()`
*  lines: `geom_hline()` and `geom_vline()`
*  shapes: `geom_rect()` and `geom_segment()`
* additional layers: e.g. a second dataset with `geom_point()`

---
## Annotatations

.panelset[
  .panel[.panel-name[Code]

``` r
trend_text <- "Larger engine sizes tend to have lower fuel economy." |>
  str_wrap(width = 30)
ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point() +
  annotate(
    geom = "label", x = 3.5, y = 38,
    label = trend_text,
    hjust = "left", color = "red"
  ) +
  annotate(
    geom = "segment",
    x = 3, y = 35, xend = 5, yend = 25, color = "red",
    arrow = arrow(type = "closed")
  )
```
]

.panel[.panel-name[Plot]
![](index_files/figure-html/trend-line-1.png) 
]
]

---

## Annotations example:

.panelset[
  .panel[.panel-name[Code]

``` r
library(taylor)
data(taylor_albums)

taylor_albums %>% 
  mutate(album_name = factor(album_name, c(album_levels, "Other"),
                             labels = c(gsub("POETS DEPARTMENT",
                                             "POETS\nDEPARTMENT",
                                             album_levels), "Other"))) %>%
  ggplot(aes(x = album_name, y = metacritic_score, fill = album_name)) +
  geom_bar(stat = "identity", color = "black") + geom_text(aes(label = metacritic_score), vjust = -0.5, color = "gray15") + theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))  + 
  scale_fill_albums() + theme(legend.position = "none")
```
]

.panel[.panel-name[Plot]
![](index_files/figure-html/taylor-ex-1.png) 
]
]

---
## Scales
This is how your axes appear -- from their limitations to their breaks/ticks! Examples below are for continuous scales, but the same logic applies to discrete!

* `scale_x_continuous()` 
* `scale_y_continuous()` 
* `scale_color_*()`
* `label_*()`
  * e.g. `label_percent()`
  * `label_dollar()`
* [Color Brewer](https://ggplot2.tidyverse.org/reference/scale_brewer.html) (great for pre-set colors)
  * e.g. `scale_color_brewer()`

---
### Scales: examples

.panelset[
  .panel[.panel-name[Code]

``` r
outliers <- mpg %>% filter(hwy > 34 | displ > 6 | hwy < 15)
ggplot(mpg, aes(x = displ, y = hwy, color = drv)) +
  geom_point(alpha = 0.5) + geom_label_repel(data = outliers, aes(x = displ, y = hwy, label = model ))+
  geom_smooth(se = FALSE) +
  geom_text( data = label_info, 
    aes(x = displ, y = hwy, label = drive_type),
    fontface = "bold", size = 5, nudge_y = 2
  ) + scale_y_continuous(breaks = seq(15, 40, by = 5)) +
  theme(legend.position = "none")
```
]

.panel[.panel-name[Plot]
![](index_files/figure-html/mpg-many-1.png) 
]
]

---
### Scales: examples

.panelset[
  .panel[.panel-name[Code]

``` r
taylor_album_songs %>% 
    mutate(album_name = factor(album_name, c(album_levels, "Other"),
                               labels = c(gsub("POETS DEPARTMENT",
                                               "POETS\nDEPARTMENT",
                                               album_levels), "Other"))) %>%

ggplot(aes(x = key_name, fill = album_name)) +
    geom_bar(color = "black", position = "fill") + 
    scale_y_continuous(name = "Percentage", labels = label_percent()) +
    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))  + 
    scale_fill_albums() + theme(legend.position = "right") +
  labs(title = "Keys used in Taylor Swift's Albums", caption = "source: taylor package")
```
]

.panel[.panel-name[Plot]
![](index_files/figure-html/taylor-ex-scales-1.png) 
]
]

---
## Themes
PREPARE FOR FUN!!!!

This is how you can make your best plot! The possibilities are endless!! (Source: [ThemePark package](https://github.com/MatthewBJane/ThemePark))

.panelset[
  .panel[.panel-name[Code]

``` r
library(ThemePark)
ggplot(barbie_box_office |> dplyr::mutate(daily=daily/1000000), 
       aes(x = day, y = daily)) +
    geom_smooth(method = 'loess', 
                color  = barbie_theme_colors["dark"],
                fill   = barbie_theme_colors["light"]) +
    geom_point(color = barbie_theme_colors["medium"]) +
    labs(title = "Barbie Scatter Plot",
         subtitle = "Daily box office takings",
         caption  = "Source: www.boxofficemojo.com",
         x = "Days since release",
         y = "Box office takings (million USD)") +
    theme_barbie()
```
]

.panel[.panel-name[Plot]
![](index_files/figure-html/theme-barbie-1.png) 
]
]

---

## Other themes

* Built-in options (e.g. `theme_bw()`)
* Outside-but-still-popular options (`ggthemes` package)
* Google 'thing you like' + 'ggplot' and chances are there is a theme for you!

---
## You can really add any theme to anything!

``` r
ggplot(mpg, aes(x = displ, y = hwy, color = class)) + 
    geom_point() +  labs(title = "Displ vs Hwy", x = "Highway mpg", y = "Displacement") + 
  theme(legend.position = "none") + theme_barbie()
```

![](index_files/figure-html/unnamed-chunk-4-1.png)

---

## Layout

.panelset[
  .panel[.panel-name[Code]

``` r
p1 <- ggplot(mpg, aes(x = displ, y = hwy, color = class)) + 
    geom_point(show.legend = FALSE) +  labs(title = "Displ vs Hwy", x = "Highway mpg", y = "Displacement") + 
  scale_color_brewer(palette = "Dark2")
p2 <- ggplot(mpg, aes(x = drv, y = hwy)) + 
  geom_boxplot(color = "slateblue", fill = "slateblue4", alpha = 0.4)  + 
  labs(title = "Drivetrain vs Hwy mpg", y = "Highway mpg", x = "Drivetrain")
p3 <- ggplot(mpg, aes(x = cty, y = hwy, color = class)) + 
  geom_point(show.legend = FALSE) + theme(legend.position = "none") + scale_color_brewer(palette = "Dark2") +
  labs(title = "City vs Highway MPG", x = "City mpg", y = "Highway mpg")
p4 <- ggplot(mpg, aes(x = class, y = cty, fill = class)) + 
  geom_bar(stat = "summary", fun = "mean") + theme(legend.position = "bottom") + scale_fill_brewer(palette = "Dark2")

(guide_area() / (p1 | p3) / (p2 | p4)) +
  plot_annotation(
    title = "City and highway mileage for cars with different drive trains",
    caption = "Source: https://fueleconomy.gov.") +
  plot_layout(  guides = "collect",
    heights = c(1, 3, 2, 4) ) &
  theme(legend.position = "top")
```
]

.panel[.panel-name[Plot]
![](index_files/figure-html/layout-1.png) 
]
]

---
class: center, inverse, middle
# PRACTICE!

[go to today's repo](https://classroom.github.com/a/Abgb4rln)

practice of concepts and prep for A4!

---

# Recap:

* Labels
* Annotations
* Scales
* Themes
* Layout