ggplot basics

# ggplot basics
### Intro to data visualization with ggplot2 <a href='https://therbootcamp.github.io'> The R Bootcamp </a> <a href='https://therbootcamp.github.io/SDGDataViz_2021Nov/'> </a>  <a href='https://therbootcamp.github.io'> </a>  <a href='therbootcamp@gmail.com'> </a>  <a href='https://www.linkedin.com/company/27090302'> </a>
### November 2021

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 Intro to data visualization with ggplot2 | Novemeber 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>

]

]

---

# Wrangling

```r
sdg_props = project_sdgs %>% 
 
 # create year variable
 mutate(year = year(start_date)) %>% 
 
 # count projects by system, sdg, and year
 group_by(system, sdg, year) %>% 
 summarize(n = n()) %>% 
 
 # filter year, system 
 filter(year > 2008 & year < 2022,
 system == "elsevier",
 sdg %in% c("SDG-01", "SDG-06")) %>% 
 
 # normalize within year
 group_by(year) %>% 
 mutate(prop = n / sum(n))
```

]

```r
sdg_props
```

```
# A tibble: 26 × 5
# Groups: year [13]
 system sdg year n prop
 <chr> <chr> <dbl> <int> <dbl>
1 elsevier SDG-01 2009 14 0.609
2 elsevier SDG-01 2010 17 0.586
3 elsevier SDG-01 2011 24 0.686
4 elsevier SDG-01 2012 24 0.585
5 elsevier SDG-01 2013 29 0.725
6 elsevier SDG-01 2014 21 0.6 
7 elsevier SDG-01 2015 26 0.765
8 elsevier SDG-01 2016 34 0.829
# … with 18 more rows
```

]

---

# `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
ggplot(data = sdg_props)
```

]

---

# `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 = sdg_props,
       mapping = aes(x = year, 
                     y = prop))
```

]

]

---

# <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 = sdg_props,
       mapping = aes(x = year, 
                     y = prop)) +
  
  # 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>

```r
ggplot(data = sdg_props,
       mapping = aes(x = year, 
                     y = prop)) +
  
  # 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>
</ul>

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

]

]

---

# `aes()`

```r
ggplot(data = sdg_props,
       mapping = aes(x = year, 
                     y = prop,
                     
                     # add color aesthetic
                     col = sdg)) +
  geom_point() 
```

]

]

---

# `geom_*()`

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

```r
ggplot(data = sdg_props,
       mapping = aes(x = year, 
                     y = prop,
                     col = sdg)) +
  
  # 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 = sdg_props,
       mapping = aes(x = year, 
                     y = prop,
                     col = sdg)) +
  
  # 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 = sdg_props,
       mapping = aes(x = year, 
                     y = prop,
                     col = sdg)) +
  
  # Show as points and lines
  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>

]

]

---

# Wrangling

```r
sdg_props = project_sdgs %>% 
 mutate(year = year(start_date)) %>% 
 group_by(system, sdg, year) %>% 
 summarize(n = n()) %>% 
 
 # no filtering of systems
 filter(year > 2008 & year < 2022,
 sdg %in% c("SDG-01", "SDG-06")) %>% 
 
 # group by year and system
 group_by(year, system) %>%
 mutate(prop = n / sum(n))
```

]

```
# A tibble: 130 × 5
# Groups: year, system [65]
 system sdg year n prop
 <chr> <chr> <dbl> <int> <dbl>
 1 aurora SDG-01 2009 8 0.4 
 2 aurora SDG-01 2010 4 0.222
 3 aurora SDG-01 2011 11 0.407
 4 aurora SDG-01 2012 17 0.586
 5 aurora SDG-01 2013 16 0.5 
 6 aurora SDG-01 2014 24 0.632
 7 aurora SDG-01 2015 15 0.577
 8 aurora SDG-01 2016 13 0.542
 9 aurora SDG-01 2017 16 0.516
10 aurora SDG-01 2018 25 0.658
11 aurora SDG-01 2019 21 0.677
12 aurora SDG-01 2020 16 0.533
13 aurora SDG-01 2021 13 0.565
14 aurora SDG-06 2009 12 0.6 
# … with 116 more rows
```

]

---

# `facet_*()`

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

```r
ggplot(data = sdg_props,
       mapping = aes(x = year, 
                     y = prop,
                     col = sdg)) +
  geom_point() +
  geom_line() +
  
  # facet by system
  facet_wrap(~system)
```

]

]

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<h1><a href="https://therbootcamp.github.io/SDGDataViz_2021Nov/">Schedule</a></h1>