Data Visualization

class: center, middle, inverse, title-slide

.title[
# Data Visualization
]
.subtitle[
## Chapter 0. Course Description
]
.author[
### Iñaki Úcar
]
.institute[
### Department of Statistics | uc3m-Santander Big Data Institute
]
.institute[
### Master in Computational Social Science
]
.date[
### Licensed under Creative Commons Attribution <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a> Last generated: 2023-09-13
]

---

class: base24

# Big Picture

.pull-left[
![:vspace 108]()
### Data programming
- Data reading
- Data transformation ⎯⎯⎯⟶

### Research design
- File organization
- Working with git ⎯⎯⎯⎯⎯⟶
]

.pull-right[
### Data visualization
- Fundamentals of graphical practice
- The grammar of graphs
![:vspace 36]()
- Data visualization in R
![:vspace 126]()
- Data communication in R
]

---
class: base24

# Background and Goals

The learning outcomes of this course are:

- Knowledge of the general principles of analytical design, graphical elements and their visual perception.

- Ability to select the type of representation and graphic elements most appropriate to the type of data and the result to be communicated.

- Ability to read, understand, analyze and elaborate graphic representations with social data.

- Ability to produce automated reports and dashboards with reproducible visualizations.

The course is **very practical**, with a mix of master classes, discussion and practice.

---
class: base24

# Course Contents and Schedule

![:vspace 70]()

- **Session 01-03**: Chapter 1. Fundamentals of Graphical Practice

- **Session 04-08**: Chapter 2. The Grammar of Graphics

- **Session 09-10**: Chapter 3. Data Visualization in R

- **Session 11, 14**: Chapter 4. Data Communication in R

- **Session 12-13**: Discussion and Evaluation
 ![:hspace 117]()⟶ (Dec 13) Student presentations 1
 ![:hspace 117]()⟶ (Dec 20) Student presentations 2

---
class: base24

# Logistics

### Slots & Location

- **Session 01-14**: Wednesday, 18--21 h, room 2.A.04
 - Nov 08: in a conference, need to reschedule
 - Nov 15: may need to reschedule too
 - Dec 06 (holiday) → Jan 10 (Wednesday)

### Resources

- **Teaching materials** at https://csslab.uc3m.es/dataviz
- We will use [**R**](https://www.r-project.org/) and
  the [**RStudio**](https://www.rstudio.com/) IDE for all the computational work

.center[
![:scale 120px](assets/img/R.png)
![:hspace 90]()
![:scale 260px](assets/img/RStudio.png)
]

---
class: base24

# Communication and Tutoring

![:vspace 35]()

### (Tentative) Office hours

- Monday 15--17 h
- Friday 15--17 h
- Please, **make an appointment by email**

I will primarily communicate with you outside of class and office hours
through [Aula Global](https://aulaglobal.uc3m.es) (updates, grades, ...).

- You can use **course forum** to ask general questions
- You can contact me by e-mail: [inaki.ucar@uc3m.es](mailto:inaki.ucar@uc3m.es)
- You may find me at my office: 7.3.J25 (Leganés), 18.2.D35 (Getafe)

---
class: base24

# Course Assessment

### **25%** Participation

- Proposed exercises + discussion

### **75%** Final project

- **Session 01-06**: Chart selection (mid-Oct)
  - Select a graph and send it for approval + scope definition
- **Session 07-10**: Chart replication (mid-Nov)
  - Develop R code that replicates the graph step by step
- **Session 11-13**: Blog post (mid-Dec)
  - Write a [Distill](https://rstudio.github.io/distill/) Rmd article
    that discusses (why did you choose it, strengths, weaknesses)
    and replicates the graph
- **Session 12-13**: Project presentation
  - Presentation (MAY be in Rmd) + questions (5 + 5 min.)

---
class: inverse, center, middle

# Final Project Example: Gapminder’s World Health Chart

---
class: inverse

---
class: base24

# gapminder.org

---

# Coordinates and Axes: Base Plot

```r
p <- ggplot(filter(gapminder, year == 2007)) +
 aes(gdpPercap, lifeExp) + ylab("Life expectancy") + xlab("Income")
p
```

---

# Coordinates and Axes: Scales and Breaks

```r
breaks <- 500*2^c(0:8)
klabel <- scales::label_number(suffix="k", scale=1e-3)
labels <- c(breaks[1:5], klabel(breaks[-(1:5)]))

p <- p + scale_y_continuous(limits=c(10, 95), breaks=seq(10, 90, 10)) +
 scale_x_log10(limits=range(breaks), breaks=breaks, labels=labels)
p
```

---

# Theme

```r
p <- p + theme_classic(base_size=16) +
 theme(panel.grid.major=element_line(), legend.position="none")
p
```

---

# Annotations: Year

```r
p <- p + geom_text(aes(8000, 50, label=year), size=65, color="lightgray")
p
```

---

# Annotations: Income Levels

```r
tlevel <- c(1300, 5000, 14000, 40000)
blevel <- c(3000, 8000, 24000)
ilevel <- c("INCOME LEVEL 1", "LEVEL 2", "LEVEL 3", "LEVEL 4")

p <- p + geom_vline(xintercept=blevel, color="darkgray") +
 annotate("text", x=tlevel, y=95, color="darkgray", vjust=0, size=3, label=ilevel) +
 annotate("text", x=blevel, y=95, color="darkgray", vjust=0, size=5, label="◆")
p
```

---

# Annotations: Units

```r
p <- p +
 annotate("text", x=128000, y=10, hjust=0.95, vjust=1, size=3,
 label="per person (GDP/capita, PPP$ inflation-adjusted") +
 annotate("text", x=500, y=95, hjust=0.5, vjust=-1.5, size=3, angle=90, label="years")
p
```

---

# Data: Bubbles

```r
p <- p + geom_point(aes(color=continent, size=pop), alpha=0.7)
p
```

---

# Data: Adjustments

```r
p <- p + geom_point(aes(size=pop), color="#333333", shape=21) +
 scale_size_area(max_size=25)
p
```

---

# Data: Colors

```r
ccolors <- c("#00d5e9", "#7feb00", "#ffe700", "#ff5872")
p <- p + scale_color_manual(values=ccolors)
p
```

---

# Legend

```r
legend <- ggplot(world) +
 aes(long, lat, group=group, map_id=region, fill=continent) +
 geom_map(map=world) +
 scale_fill_manual(values=ccolors) +
 theme_void() + theme(legend.position="none")
legend
```

---

# Everything Together

```r
ggplot(filter(gapminder, year == 2007)) +
  # coordinates and axes
  aes(gdpPercap, lifeExp) + ylab("Life expectancy") + xlab("Income") +
  scale_y_continuous(limits=c(10, 95), breaks=seq(10, 90, 10)) +
  scale_x_log10(limits=range(breaks), breaks=breaks, labels=labels) +
  # theme
  theme_classic(base_size=16) +
  theme(panel.grid.major=element_line(), legend.position="none") +
  # annotations
  geom_text(aes(8000, 50, label=year), size=65, color="lightgray") +
  geom_vline(xintercept=blevel, color="darkgray") +
  annotate("text", x=tlevel, y=95, color="darkgray", vjust=0, size=3, label=ilevel) +
  annotate("text", x=blevel, y=95, color="darkgray", vjust=0, size=5, label="◆") +
  annotate("text", x=128000, y=10, hjust=0.95, vjust=1, size=3,
           label="per person (GDP/capita, PPP$ inflation-adjusted") +
  annotate("text", x=500, y=95, hjust=0.5, vjust=-1.5, size=3, angle=90, label="years") +
  # data
  geom_point(aes(color=continent, size=pop), alpha=0.7) +
  geom_point(aes(size=pop), color="#333333", shape=21) +
  scale_size_area(max_size=25) + scale_color_manual(values=ccolors) +
  # legend
  annotation_custom(ggplotGrob(legend), xmin=log10(16000), ymin=10, ymax=40)
```

---

# Result

---

# Bonus: Multiple Years

---

# Bonus: Dynamic

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---
class: inverse, center, middle

# (Demo Post)