Intro to ggplot2

# Intro to ggplot2
### Data Visualization for Social Good <a href='https://correlaid.org/correlaid-x/switzerland/'> CorrelAid Switzerland </a> <a href='https://correlaidswitzerland.github.io/DataViz4Good/'> </a>  <a href='https://correlaid.org/correlaid-x/switzerland/'> </a>  <a href='switzerland@correlaid.org'> </a>  <a href='https://www.linkedin.com/company/correlaidswiss/'> </a>
### February 2021

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 Data Visualization for Social Good | February 2021
 
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# Tidyverse

<ul>
 <li class="m1">The tidyverse is...</li> 
 <ul class="level">
 <li>A collection of user-friendly <high>packages</high> for analyzing <high>tidy data</high></li> 
 <li>An <high>ecosystem</high> for analytics and data science with common design principles</li> 
 <li>A <high>dialect</high> of the R language</li>
 </ul>
</ul>

]

---

# Modular graphics in <mono>ggplot2</mono>

<ul>
 <li class="m1"><highm>data</highm>: the data set</li>
 <li class="m2"><highm>mapping</highm>: the plot's structure</li>
 <ul class="level">
 <li>What do the axes represent?</li>
 <li>What do size, shapes, colors, etc. represent?</li>
 </ul>
 <li class="m3"><highm>geoms</highm>: geometric shapes illustrating data</high></li>
 <li class="m4"><highm>labs</highm>: Plot annotation</high></li>
 <li class="m5"><highm>themes</highm>: Aesthetic details</high></li>
 <li class="m6"><highm>facets</highm>: Stratify plot according to variable</high></li>
 <li class="m7"><highm>scales</highm>: Scaling of dimensions</high></li>
</ul>

]

]

---

# `ggplot()`

<ul>
 <li class="m1">All plots start with <mono>ggplot()</mono></li>
 <li class="m2">Two arguments</li>
 <ul class="level">
 <li><mono>data</mono> | The data set (<mono>tibble</mono>)</li>
 <li><mono>mapping</mono> | The plot structure. Defined using <mono>aes()</mono>
 </ul>
 </li>
</ul>

```r
# averages per year
basel_avg <- basel %>% 
 group_by(year) %>% 
 summarize(
 income_mean = mean(income_mean),
 income_median = mean(income_median))
```

]

```r
ggplot(data = basel_avg)
```

]

---

# `aes()`

<ul>
 <li class="m1"><mono>aes()</mono> helps define the structure of the <highm>mapping</highm> Argument.</li>
 <li class="m2">Key arguments:</li>
 <ul class="level">
 <li><mono>x, y</mono> | Defines axes</li>
 <li><mono>color,fill</mono> | Defines colors</li>
 <li><mono>alpha</mono> | Defines opacity</li>
 <li><mono>size</mono> | Defines sizes</li>
 <li><mono>shape</mono> | Defines shapes (e.g., circles or squares)</li>
 </ul>
</ul>

]

```r
ggplot(data = basel_avg,
       mapping = aes(x = year, 
                     y = income_mean))
```

]

---

# <mono>+</mono>

<ul>
 <li class="m1">The <mono>+</mono> operator "adds" <high>additional elements</high> to the plot.</li>
 <li class="m1">Not to be confused with the pipe <mono>%>%</mono>.</li>
</ul>

```r
ggplot(data = basel_avg,
       mapping = aes(x = year, 
                     y = income_mean)) +
  
  # Show as points
  geom_point() 
```

]

]

---

# `geom_*()`

<ul>
 <li class="m1"><mono>geom_*()</mono> functions define which geometric objects are used to illustrate the data.</li>
 <li class="m2">A few examples <mono>geoms</mono>:</li>
 <ul class="level">
 <li><mono>geom_point()</mono> | for points</li>
 <li><mono>geom_line()</mono> | for lines</li>
 <li><mono>geom_smooth()</mono> | for smooth curves</li>
 <li><mono>geom_bar()</mono> | for bars</li>
 <li><mono>geom_boxplot()</mono> | for box-plots </li>
 <li><mono>geom_violin()</mono> | for violin-plots </li>
 </ul>
</ul>

]

]

---

# `geom_*()`

<ul>
 <li class="m1"><mono>geom_*()</mono> functions define which geometric objects are used to illustrate the data.</li>
</ul>

```r
ggplot(data = basel_avg,
       mapping = aes(x = year, 
                     y = income_mean)) +
  
  # Show as lines
  geom_line() 
```

]

]

---

# `geom_*()`

<ul>
 <li class="m1"><mono>geom_*()</mono> functions define which geometric objects are used to illustrate the data.</li>
</ul>

```r
ggplot(data = basel_avg,
       mapping = aes(x = year, 
                     y = income_mean)) +
  
  # Show as smoothed curve
  geom_smooth() 
```

]

]

---

# `geom_*()`

<ul>
 <li class="m1"><mono>geom_*()</mono> functions define which geometric objects are used to illustrate the data.</li>
</ul>

```r
ggplot(data = basel_avg,
       mapping = aes(x = year, 
                     y = income_mean)) +
  
  # Show as points and lines
  geom_point() +
  geom_line() 
```

]

]

---

# `geom_*()`

<ul>
 <li class="m1">Most <mono>geom_*()</mono> functions allow specification of <highm>data</highm> and <highm>mapping</highm>.</li>
</ul>

```r
ggplot(data = basel_avg,
       mapping = aes(x = year, 
                     y = income_mean)) +
  geom_point() +
  geom_line() +
  
  # Add points and lines for median
  geom_point(aes(y = income_median)) +
  geom_line(aes(y = income_median))
```

]

]

---

# Wrangling

<ul>
 <li class="m1">Oftentimes, creating the desired plot requires appropriate data wrangling.</li>
 <li class="m2"><mono>ggplot</mono> works best with <high>long data formats</high>.</li>
</ul>

```r
# pivot to long format
basel_avg_long <- basel_avg %>% 
 pivot_longer(-year, 
 names_to = "statistic", 
 values_to = "income")
```

]

```r
basel_avg_long
```

```
# A tibble: 34 x 3
 year statistic income
 <dbl> <chr> <dbl>
 1 2001 income_mean 63027.
 2 2001 income_median 49516.
 3 2002 income_mean 63555.
 4 2002 income_median 50066.
 5 2003 income_mean 63083.
 6 2003 income_median 49717.
 7 2004 income_mean 62298.
 8 2004 income_median 49467.
 9 2005 income_mean 63133.
10 2005 income_median 49192.
# … with 24 more rows
```

]

---

# <mono>aes()</mono>

<ul>
 <li class="m1"><mono>aes()</mono> helps define the structure of the <highm>mapping</highm> Argument.</li>

```r
# use basel_avg_long
ggplot(data = basel_avg_long,
       mapping = aes(
         x = year, 
         y = income,
                     
         # add color dimension
         col = statistic)) +
  geom_point() +
  geom_line() 
```

]

]

---

# <mono>aes()</mono>

<ul>
 <li class="m1"><mono>aes()</mono> helps define the structure of the <highm>mapping</highm> Argument.</li>

```r
# use basel_avg_long
ggplot(data = basel_avg_long,
       mapping = aes(
         x = year, 
         y = income,
                     
         # add shape dimension
         shape = statistic)) +
  geom_point() +
  geom_line() 
```

]

]

---

# `facet_*()`

<ul>
 <li class="m1">Facetting creates the <high>same plot for groups</high> defined by another variable.</li>
 <li class="m2">Key functions:</li>
 <ul class="level">
 <li><mono>facet_wrap()</mono></li>
 <li><mono>facet_grid()</mono></li>
 </ul>
</ul>

```r
basel_long <- basel %>% 
 pivot_longer(c(income_mean, income_median),
 names_to = 'statistic',
 values_to = 'income')
```

]

]

---

# `facet_*()`

<ul>
 <li class="m1">Facetting creates the <high>same plot for groups</high> defined by another variable.</li>
</ul>

```r
# use basel_long
ggplot(data = basel_long,
       mapping = aes(
         x = year, 
         y = income,
         col = statistic)) +
  geom_point() +
  geom_line() +
  
  # facet by quarter
  facet_wrap(~quarter)
```

]

]

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<h1><a href="https://correlaidswitzerland.github.io/DataViz4Good/">Schedule</a></h1>