1.3 — Data Visualization with ggplot2

# 1.3 — Data Visualization with ggplot2
## ECON 480 • Econometrics • Fall 2020
### Ryan Safner Assistant Professor of Economics <a href="mailto:safner@hood.edu">safner@hood.edu</a> <a href="https://github.com/ryansafner/metricsF20">ryansafner/metricsF20</a> <a href="https://metricsF20.classes.ryansafner.com"> metricsF20.classes.ryansafner.com</a>

---

# Outline

## [Plotting in Base R](#4)

## [ggplot2 and the tidyverse](#11)

## [Plot Layers](#23)

## [Some Troubleshooting](#64)

---

# Graphics and Statistics

- Admittedly, we still need to cover basic descriptive statistics and data fundamentals
    - continuous, discrete, cross-sectional, time series, panel data
    - mean, median, variance, standard deviation
    - random variables, distributions, PDFs, Z-scores
    - bargraphs, boxplots, histograms, scatterplots

- All of this is coming in 2 weeks as we return to statistics and econometric theory

- But let's start with the fun stuff right away, even if you don't fully know the *reasons*: .hi[data visualiation]

---

# Plotting in Base R

---

# Our Data Source

- For our examples, we'll use a dataset `mpg` from the `ggplot2` library

```r
library(ggplot2)

head(mpg)
```

```
## # A tibble: 6 x 11
## manufacturer model displ year cyl trans drv cty hwy fl class 
## <chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr> 
## 1 audi a4 1.8 1999 4 auto(l5) f 18 29 p compa…
## 2 audi a4 1.8 1999 4 manual(m5) f 21 29 p compa…
## 3 audi a4 2 2008 4 manual(m6) f 20 31 p compa…
## 4 audi a4 2 2008 4 auto(av) f 21 30 p compa…
## 5 audi a4 2.8 1999 6 auto(l5) f 16 26 p compa…
## 6 audi a4 2.8 1999 6 manual(m5) f 18 26 p compa…
```

]

---

# Plotting in Base R

- Base `R` is very powerful and intuitive to plot, but not very sexy

- Basic syntax for most types of plots:

```r
plot_type(my_df$variable)
```

- If using multiple variables, you can avoid typing `$` by just typing the variable names and then in another argument to the plotting function, specify `data = my_df`

```r
plot_type(my_df$variable1, my_df$variable2, data = my_df)
```

---

# Plotting in Base R: Histogram

```r
hist(mpg$hwy)
```

]
]

.pull-right[
.center[
<img src="1.3-slides_files/figure-html/unnamed-chunk-4-1.png" width="504" />
]
]

---

# Plotting in Base R: Boxplot

```r
boxplot(mpg$hwy)
```

]

.pull-right[
.center[
<img src="1.3-slides_files/figure-html/unnamed-chunk-5-1.png" width="504" />
]
]

---

# Plotting in Base R: Boxplot by Category

```r
boxplot(mpg$hwy ~ mpg$class)
```

```r
# second method
boxplot(mpg ~ class, data = mtcars)
```

- The `~` is part of `R`'s .hi-purple[“formula notation”]: 
    - Dependent variable goes to left
    - Independent variable(s) to right, separated with `+`'s
    - Think `y~x+z` means "`y` is explained by `x` and `z`"

]
]
.pull-right[
.center[
<img src="1.3-slides_files/figure-html/unnamed-chunk-7-1.png" width="504" />
]
]

---

# Plotting in Base R: Scatterplot

```r
plot(mpg$hwy ~ mpg$displ)
```

```r
# second method
plot(hwy ~ displ, data = mpg)
```

]

.pull-right[
.center[
<img src="1.3-slides_files/figure-html/unnamed-chunk-9-1.png" width="504" />
]
]

---

# ggplot2 and the tidyverse

---

background-image: url(https://www.dropbox.com/s/np8v4ihjotkckc1/tidyverse1.png?raw=1)
background-size: cover

---

# The tidyverse

> "The tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures.

- Largely (but not only) created by Hadley Wickham

- We will look at this much more extensively next week!

- This "flavor" of `R` will make your coding life *so much easier!*

![](https://www.dropbox.com/s/81ijxswbwqor0up/tidyverse.png?raw=1)

---

# ggplot

- `ggplot2` is perhaps the most popular package in `R` and a core element of the `tidyverse`

- `gg` stands for a .hi-purple[grammar of graphics]

- Very powerful and beautiful graphics, very customizable and reproducible, but requires a bit of a learning curve

- All those "cool graphics" you've seen in the New York Times, fivethirtyeight, the Economist, Vox, etc use the grammar of graphics
]

.pull-right[
.center[
![:scale 65%](https://www.dropbox.com/s/4bxo0ac3gwwgd9y/hadleyplot2.png?raw=1)
]
]

---

# ggplot: All Your Figure are Belong to Us

Source: [fivethirtyeight](https://fivethirtyeight.com/features/fastest-airlines-fastest-airports/)
]
]
.pull-left[
.center[
![:scale 80%](https://www.dropbox.com/s/2bkklsrsl0ep902/538gg.png?raw=1)

Source: [fivethirtyeight](https://fivethirtyeight.com/features/how-to-tell-someones-age-when-all-you-know-is-her-name/)
]
]

---

# ggplot: All Your Figure are Belong to Us

Source: [BBC's bbplot](https://github.com/bbc/bbplot)
]

---

# Why Go gg?

Hadley Wickham

Chief Scientist, R Studio
]
]

> "The transferrable skills from ggplot2 are not the idiosyncracies of plotting syntax, but a powerful way of thinking about visualisation, as a way of **mapping between variables and the visual properties of geometric objects** that you can perceive."

]

---

# The Grammar of Graphics (gg)

- This is a true *grammar*

- We *don’t* talk about specific chart .hi-purple[types]
  - That you have to hunt through in Excel and reshape your data to fit it

- Instead we talk about specific chart .hi[components]
]

---

# The Grammar of Graphics (gg) I

.pull-left[
.smallest[
- Any graphic can be built from the same components:
  1. .hi-purple[Data] to be drawn from
  2. .hi-purple[Aesthetic mappings] from data to some visual marking
  3. .hi-purple[Geometric objects] on the plot
  4. .hi-purple[Scales] define the range of values
  5. .hi-purple[Coordinates] to organize location
  6. .hi-purple[Labels] describe the scale and markings
  7. .hi-purple[Facets] group into subplots
  8. .hi-purple[Themes] style the plot elements
    
- Not every plot needs *every* component, but all plots *must* have the first 3!
]
]

---

# The Grammar of Graphics (gg) II

.pull-left[
.smallest[
- Any graphic can be built from the same components:
  1. `data` to be drawn from
  2. `aes`.hi-purple[thetic mappings] from data to some visual marking
  3. `geom`.hi-purple[metric objects] on the plot
  4. `scale` define the range of values
  5. `coord`.hi-purple[inates] to organize location
  6. `labels` describe the scale and markings
  7. `facet` group into subplots
  8. `theme` style the plot elements
    
- Not every plot needs *every* component, but all plots *must* have the first 3!
]
]
.pull-right[
.center[
![](../images/gglayers.png)
]
]

---

# The Grammar of Graphics (gg): All at Once

## All in one command

- Produces plot output in viewer

- Does not save plot
    - Save with `Export` menu in viewer

- Adding layers requires whole code for new plot

]

```r
ggplot(data = mpg)+
  aes(x = displ,
        y = hwy)+
  geom_point()+
  geom_smooth()
```
]

---

# The Grammar of Graphics (gg): As R Objects

- Saves your plot as an `R` object

- Does *not* show in viewer
  - Execute the name of your object to see it

- Can add layers by calling the original plot name
]

```r
# make and save plot
p <- ggplot(data = mpg)+
 aes(x = displ,
 y = hwy)+
 geom_point()

p # view plot

# add a layer

p + geom_smooth() # shows new plot

p <- p + geom_smooth() # saves and overwrites p
p2 <- p + geom_smooth() # saves as different object
```
]

---

# Plot Layers

---

# The Grammar of Graphics

---

# The Grammar of Graphics (gg): Data

`ggplot(data = mpg)`

]

**Data** is the source of our data. As part of the `tidyverse`, `ggplot2` requires data to be .hi-purple["tidy"].red[1]:

1. Each variable forms a .hl[column]

2. Each observation forms a .hl[row]

3. Each observational unit forms a table

]

.footnote[.red[1] Data "tidyness" is the core element of all `tidyverse` packages. Much more on all of this next class.]

---

# The Grammar of Graphics (gg): Adding Layers

- Style recommendation: start a new line after each `+` to improve legibility!

- We will build a plot layer-by-layer
]

---

# The Grammar of Graphics (gg): Aesthetics I

---

# The Grammar of Graphics (gg): Aesthetics II

---

# The Grammar of Graphics (gg): Aesthetics III

- `displ`

- `hwy`

- `class`

]

---

# The Grammar of Graphics (gg): Aesthetics III

- `displ` → **x**

- `hwy` → **y**

- `class` → *shape*, *size*, **color**, etc.

]

---

# The Grammar of Graphics (gg): Aesthetics IV

# The Grammar of Graphics (gg): Aesthetics IV

```r
aes(x = displ,
    y = hwy,
    color = class)
```
]

---

# The Grammar of Graphics (gg): Geoms I

---

# The Grammar of Graphics (gg): Geoms II

- What `geom`s you should use depends on what you want to show:

| Type | `geom` |
|------|--------|
| Point | `geom_point()` |
| Line  | `geom_line()`, `geom_path()`  |
| Bar   | `geom_bar()`, `geom_col()` |
| Histogram | `geom_histogram()` |
| Regression | `geom_smooth()` |
| Boxplot | `geom_boxplot()` |
| Text | `geom_text()` |
| Density | `geom_density()` |
]

---

# The Grammar of Graphics (gg): Geoms III

```r
##  [1] "geom_abline"     "geom_area"       "geom_bar"        "geom_bin2d"     
##  [5] "geom_blank"      "geom_boxplot"    "geom_col"        "geom_contour"   
##  [9] "geom_count"      "geom_crossbar"   "geom_curve"      "geom_density"   
## [13] "geom_density_2d" "geom_density2d"  "geom_dotplot"    "geom_errorbar"  
## [17] "geom_errorbarh"  "geom_freqpoly"   "geom_hex"        "geom_histogram" 
## [21] "geom_hline"      "geom_jitter"     "geom_label"      "geom_line"      
## [25] "geom_linerange"  "geom_map"        "geom_path"       "geom_point"     
## [29] "geom_pointrange" "geom_polygon"    "geom_qq"         "geom_qq_line"   
## [33] "geom_quantile"   "geom_raster"     "geom_rect"       "geom_ribbon"    
## [37] "geom_rug"        "geom_segment"    "geom_sf"         "geom_sf_label"  
## [41] "geom_sf_text"    "geom_smooth"     "geom_spoke"      "geom_step"      
## [45] "geom_text"       "geom_tile"       "geom_violin"     "geom_vline"
```

See [http://ggplot2.tidyverse.org/reference](http://ggplot2.tidyverse.org/reference) for many more options

]

---

# The Grammar of Graphics (gg): Geoms IV

Or just start typing `geom_` in R Studio!

---

# Let's Make a Plot!

```r
ggplot(data = mpg)
```
]

.right-plot[
<img src="1.3-slides_files/figure-html/unnamed-chunk-14-1.png" width="504" style="display: block; margin: auto;" />

]

---

# Let's Make a Plot!

```r
ggplot(data = mpg)+
* aes(x = displ,
*       y = hwy)
```
]

.right-plot[
<img src="1.3-slides_files/figure-html/unnamed-chunk-15-1.png" width="504" style="display: block; margin: auto;" />

]

---

# Let's Make a Plot!

```r
ggplot(data = mpg)+
  aes(x = displ,
        y = hwy)+
* geom_point()
```
]

.right-plot[
<img src="1.3-slides_files/figure-html/unnamed-chunk-16-1.png" width="504" style="display: block; margin: auto;" />

]

---

# Let's Make a Plot!

```r
ggplot(data = mpg)+
  aes(x = displ,
        y = hwy)+
* geom_point(aes(color = class))
```
]

.right-plot[
<img src="1.3-slides_files/figure-html/unnamed-chunk-17-1.png" width="504" style="display: block; margin: auto;" />

]

---

# Let's Make a Plot!

```r
ggplot(data = mpg)+
  aes(x = displ,
        y = hwy)+
  geom_point(aes(color = class))+
* geom_smooth()
```
]

.right-plot[
<img src="1.3-slides_files/figure-html/unnamed-chunk-18-1.png" width="504" style="display: block; margin: auto;" />

]

---

# More Geoms

- `data`: geoms can have their own data
    - has to map onto global coordinates

- `aes`: geoms can have their own aesthetics
    - inherits global aesthetics by default
    - different geoms have different available aesthetics
]

---

# Change Our Plot

```r
ggplot(data = mpg)+
  aes(x = displ,
        y = hwy)+
* geom_point(aes(color = class))+
  geom_smooth()
```
]

.right-plot[
<img src="1.3-slides_files/figure-html/unnamed-chunk-19-1.png" width="504" style="display: block; margin: auto;" />

]

---

# More Geoms II

- `stat`: some geoms statistically transform data
    - `geom_histogram()` uses `stat_bin()` to group observations into bins

- `position`: some adjust location of objects
    - `dodge`, `stack`, `jitter`
]

---

# Let's Change Our Plot

```r
ggplot(data = mpg)+
* aes(x = class,
*     y = hwy)+
* geom_boxplot()
```
]

.right-plot[
<img src="1.3-slides_files/figure-html/unnamed-chunk-20-1.png" width="504" style="display: block; margin: auto;" />

]

---

# Let's Change Our Plot

```r
ggplot(data = mpg)+
* aes(x = class)+
* geom_bar()
```
]

.right-plot[
<img src="1.3-slides_files/figure-html/unnamed-chunk-21-1.png" width="504" style="display: block; margin: auto;" />

]

---

# Let's Change Our Plot

```r
ggplot(data = mpg)+
  aes(x = class,
*     fill = drv)+
  geom_bar() 
```
]

.right-plot[
<img src="1.3-slides_files/figure-html/unnamed-chunk-22-1.png" width="504" style="display: block; margin: auto;" />

]

---

# Let's Change Our Plot

```r
ggplot(data = mpg)+
  aes(x = class,
      fill = drv)+ 
* geom_bar(position = "dodge")
```
]

.right-plot[
<img src="1.3-slides_files/figure-html/unnamed-chunk-23-1.png" width="504" style="display: block; margin: auto;" />

]

---

# Back to the Original (and saving it)

```r
*p <- ggplot(data = mpg)+
 aes(x = displ,
 y = hwy)+
 geom_point(aes(color = class))+
 geom_smooth()

p # show plot
```
]

.right-plot[
<img src="1.3-slides_files/figure-html/unnamed-chunk-24-1.png" width="504" style="display: block; margin: auto;" />

]

---

# The Grammar of Graphics (gg): Facets I

`+ facet_grid()`
]

```r
*p + facet_wrap(~year)
```

<img src="1.3-slides_files/figure-html/unnamed-chunk-25-1.png" width="504" style="display: block; margin: auto;" />
]

---

# The Grammar of Graphics (gg): Facets II

`+ facet_grid()`
]

```r
*p + facet_grid(cyl~year)
```

<img src="1.3-slides_files/figure-html/unnamed-chunk-26-1.png" width="504" style="display: block; margin: auto;" />
]

---

# The Grammar of Graphics (gg): Labels

```r
p + facet_wrap(~year)+
* labs(x = "Engine Displacement (Liters)",
*      y = "Highway MPG",
*      title = "Car Mileage and Displacement",
*      subtitle = "More Displacement Lowers Highway MPG",
*      caption = "Source: EPA",
*      color = "Vehicle Class")
```

<img src="1.3-slides_files/figure-html/unnamed-chunk-27-1.png" width="504" style="display: block; margin: auto;" />
]

---

# The Grammar of Graphics (gg): Scales

`scale`+`_`+`<aes>`+`_`+`<type>`+`()`

- `<aes>`: parameter you want to adjust
- `<type`: type of parameter

- I want to change my discrete x-axis: `scale_x_discrete()`
- I want to change my continuous y-axis: `scale_y_continuous()`
- I want to rescale x-axis to log: `scale_x_log10()`
- I want to use a different color palette: `scale_fill_discrete()`, `scale_color_manual()`
]
---

# The Grammar of Graphics (gg): Scales

```r
ggplot(data = mpg)+
  aes(x = displ,
        y = hwy)+
  geom_point(aes(color = class))+
  geom_smooth()+
  facet_wrap(~year)+
  labs(x = "Engine Displacement (Liters)",
       y = "Highway MPG",
       title = "Car Mileage and Displacement",
       subtitle = "More Displacement Lowers Highway MPG",
       caption = "Source: EPA",
       color = "Vehicle Class")+
* scale_color_viridis_d()
```
]

<img src="1.3-slides_files/figure-html/unnamed-chunk-28-1.png" width="504" style="display: block; margin: auto;" />
]

---

# The Grammar of Graphics (gg): Themes

.left-code[
### Data
### Aesthetics
### Geoms
### Facets
### Labels
### Scales
### Theme
`+ theme_*()`
]

**Theme** changes appearance of plot decorations (things not mapped to data)

- Some themes that come with `ggplot2`:

- `+ theme_bw()`
- `+ theme_dark()`
- `+ theme_gray()`
- `+ theme_minimal()`
- `+ theme_light()`
- `+ theme_classic()`
]
---

# The Grammar of Graphics (gg): Themes

.left-code[
### Data
### Aesthetics
### Geoms
### Facets
### Labels
### Scales
### Theme
`+ theme_*()`
]

**Theme** changes appearance of plot decorations (things not mapped to data)

- Many parameters we could change

- Global options: `line`, `rect`, `text`, `title`
- `axis`: x-, y-, or other axis title, ticks, lines
- `legend`: plot legends for fill or color
- `panel`: actual plot area
- `plot`: whole image
- `strip`: facet labels
]

---

# The Grammar of Graphics (gg): Themes

<img src="1.3-slides_files/figure-html/unnamed-chunk-29-1.png" width="504" style="display: block; margin: auto;" />
]

---

# The Grammar of Graphics (gg): Themes II

<img src="1.3-slides_files/figure-html/unnamed-chunk-30-1.png" width="504" style="display: block; margin: auto;" />
]

---

# The Grammar of Graphics (gg): Themes III

<img src="1.3-slides_files/figure-html/unnamed-chunk-31-1.png" width="504" style="display: block; margin: auto;" />
]

---

# The Grammar of Graphics (gg): Themes III

<img src="1.3-slides_files/figure-html/unnamed-chunk-32-1.png" width="504" style="display: block; margin: auto;" />
]

---

# The Grammar of Graphics (gg): Themes (ggthemes)

.left-code[
### Data
### Aesthetics
### Geoms
### Facets
### Labels
### Scales
### Theme
`+ theme_*()`
]

- `ggthemes` package adds some other nice themes

```r
# install if you don't have it
# install.packages("ggthemes")
library("ggthemes") # load package
```

]

---

# The Grammar of Graphics (gg): Themes IV

```r
*library("ggthemes")
ggplot(data = mpg)+
  aes(x = displ,
        y = hwy)+
  geom_point(aes(color = class))+
  geom_smooth()+
  facet_wrap(~year)+
  labs(x = "Engine Displacement (Liters)",
       y = "Highway MPG",
       title = "Car Mileage and Displacement",
       subtitle = "More Displacement Lowers Highway MPG",
       caption = "Source: EPA",
       color = "Vehicle Class")+
  scale_color_viridis_d()+
* theme_economist()+
  theme(text = element_text(family = "Fira Sans"),
        legend.position="bottom") 
```
]

<img src="1.3-slides_files/figure-html/unnamed-chunk-34-1.png" width="504" style="display: block; margin: auto;" />
]

---

# The Grammar of Graphics (gg): Themes V

```r
*library("ggthemes")
ggplot(data = mpg)+
  aes(x = displ,
        y = hwy)+
  geom_point(aes(color = class))+
  geom_smooth()+
  facet_wrap(~year)+
  labs(x = "Engine Displacement (Liters)",
       y = "Highway MPG",
       title = "Car Mileage and Displacement",
       subtitle = "More Displacement Lowers Highway MPG",
       caption = "Source: EPA",
       color = "Vehicle Class")+
  scale_color_viridis_d()+
* theme_fivethirtyeight()+
  theme(text = element_text(family = "Fira Sans"),
        legend.position="bottom") 
```
]

<img src="1.3-slides_files/figure-html/unnamed-chunk-35-1.png" width="504" style="display: block; margin: auto;" />
]

---

# Some Troubleshooting

---

# Global vs. Local Aesthetics

- `aes()` can go in base (`data`) layer and/or in individual `geom()` layers
- All `geoms` will inherit global `aes` from `data` layer unless overridden

```r
# ALL GEOMS will map data to colors
ggplot(data = mpg, aes(x = displ,
                       y = hwy,
                       color = class))+
  geom_point()+
  geom_smooth()
```

]

```r
# ONLY points will map data to colors
ggplot(data = mpg, aes(x = displ,
                       y = hwy))+
  geom_point(aes(color = class))+
  geom_smooth()
```

<img src="1.3-slides_files/figure-html/unnamed-chunk-37-1.png" width="504" style="display: block; margin: auto;" />
]

---

# Mapped vs. Set Aesthetics

- `aes`thetics such as `size` and `color` can be mapped from data or set to a single value
- Map *inside* of `aes()`, set *outside* of `aes()`

```r
# Point colors are mapped from class data
ggplot(data = mpg, aes(x = displ,
                       y = hwy))+
  geom_point(aes(color = class))+
  geom_smooth()
```

<img src="1.3-slides_files/figure-html/unnamed-chunk-38-1.png" width="504" style="display: block; margin: auto;" />
]

```r
# Point colors are all set to blue
ggplot(data = mpg, aes(x = displ,
                       y = hwy))+
  geom_point(aes(), color = "red")+
  geom_smooth(aes(), color = "blue")
```

<img src="1.3-slides_files/figure-html/unnamed-chunk-39-1.png" width="504" style="display: block; margin: auto;" />
]

---

# Go Crazy I

```r
# I did some (hidden) data work before this! 
ggplot(data = county_full,
            mapping = aes(x = long, y = lat,
                          fill = pop_dens, 
                          group = group))+ 
  geom_polygon(color = "gray90", size = 0.05)+
  coord_equal()+
  scale_fill_brewer(palette="Blues",
                             labels = c("0-10", "10-50", "50-100", "100-500",
                                        "500-1,000", "1,000-5,000", ">5,000"))+
  labs(fill = "Population per\nsquare mile") +
    theme_map() +
    guides(fill = guide_legend(nrow = 1)) + 
    theme(legend.position = "bottom")
```
]

.right-plot[
<img src="1.3-slides_files/figure-html/unnamed-chunk-40-1.png" width="504" style="display: block; margin: auto;" />
]

---

# Go Crazy II

```r
library("gapminder")
library("gganimate")
ggplot(gapminder) +
  aes(x = gdpPercap, y = lifeExp, size = pop, color = country) +
  geom_point() +
  guides(color = FALSE, size = FALSE) +
  scale_x_log10(
    breaks = c(10^3, 10^4, 10^5), 
    labels = c("$1k", "$10k", "$100k")) +
  scale_color_manual(values = gapminder::country_colors) +
  scale_size(range = c(0.5, 12)) +
  labs(
    x = "GDP per capita",
    y = "Life Expectancy",
    caption = "Source: Hans Rosling's gapminder.org") +
  theme_minimal(14, base_family = "Fira Sans") +
  theme(
    strip.text = element_text(size = 16, face = "bold"),
    panel.border = element_rect(fill = NA, color = "grey40"),
    panel.grid.minor = element_blank())+
  transition_states(year, 1, 0)+
  ggtitle("Income and Life Expectancy - {closest_state}")
```
]

.right-plot[
<img src="1.3-slides_files/figure-html/unnamed-chunk-41-1.gif" style="display: block; margin: auto;" />
]

---

# Data Visualization and Graphic Design Principles

- We will return to various graphics as we cover descriptive statistics and regression

- I hope to cover some basic principles of good graphic design for figures and plots
    - If not in class, I will make a page on the website, and/or a video
]

Remember:

---

# Less is More

"Shoot me"
![](https://www.dropbox.com/s/azh5zxzqkmyuxoh/badfigure.png?raw=1)
]

]

![](http://stat545.com/img/less-is-more-darkhorse-analytics.gif)
]
]
---

# Try to Show One Trend Really Clearly

.source[New York Times: ["How Stable Are Democracies? ‘Warning Signs Are Flashing Red’"](https://www.nytimes.com/2016/11/29/world/americas/western-liberal-democracy.html), Nov 29, 2016]

---

# Reference: R Studio Makes Great "Cheat Sheet"s!

.source[[RStudio: ggplot2 Cheat Sheet](https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf)]
---

# Reference

On `ggplot2`

- **R Studio's [ggplot2 Cheat Sheet](https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf)**
- **`ggplot2`'s website [reference section](https://ggplot2.tidyverse.org/reference/index.html)**
- Hadley Wickham's[ R for Data Science book chapter on ggplot2](http://r4ds.had.co.nz/data-visualisation.html)
- STHDA's [be awesome in ggplot2](http://www.sthda.com/english/wiki/be-awesome-in-ggplot2-a-practical-guide-to-be-highly-effective-r-software-and-data-visualization)
- r-statistic's [top 50 ggplot2 visualizations](http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html)

On data visualization

- **Kieran Healy's [Data Visualization: A Practical Guide](http://socviz.co/)**
- **Claus Wilke's [Fundamentals of Data Visualization](https://serialmentor.com/dataviz)**
- PolicyViz [Better Presentations](https://policyviz.com/) 
- Karl Broman's [How to Display Data Badly](https://www.biostat.wisc.edu/~kbroman/presentations/IowaState2013/graphs_combined.pdf)
- [I Want Hue](http://tools.medialab.sciences-po.fr/iwanthue/)