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1.3 — Data Visualization with ggplot2

ECON 480 • Econometrics • Fall 2020

Ryan Safner
Assistant Professor of Economics
safner@hood.edu
ryansafner/metricsF20
metricsF20.classes.ryansafner.com

Outline

Plotting in Base R

ggplot2 and the tidyverse

Plot Layers

Some Troubleshooting

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: data visualiation

Plotting in Base R

Our Data Source

  • For our examples, we'll use a dataset mpg from the ggplot2 library
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:

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

Plotting in Base R: Histogram

  • Using the mpg data, plotting a histogram of hwy
hist(mpg$hwy)

Plotting in Base R: Boxplot

  • Using the mpg data, plotting a boxplot of hwy
boxplot(mpg$hwy)

Plotting in Base R: Boxplot by Category

  • Using the mpg data, plotting a boxplot of hwy by class
boxplot(mpg$hwy ~ mpg$class)
# second method
boxplot(mpg ~ class, data = mtcars)
  • The ~ is part of R's “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"

Plotting in Base R: Scatterplot

  • Using the mpg data, plotting a scatterplot of hwy against displ
plot(mpg$hwy ~ mpg$displ)
# second method
plot(hwy ~ displ, data = mpg)

ggplot2 and the tidyverse

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!

ggplot

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

  • gg stands for a 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

ggplot: All Your Figure are Belong to Us

Source: fivethirtyeight

Source: fivethirtyeight

ggplot: All Your Figure are Belong to Us

Source: BBC's 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."

http://disq.us/p/sv640d

The Grammar of Graphics (gg)

  • This is a true grammar

  • We don’t talk about specific chart types

    • That you have to hunt through in Excel and reshape your data to fit it

  • Instead we talk about specific chart components

The Grammar of Graphics (gg) I

  • Any graphic can be built from the same components:

    1. Data to be drawn from
    2. Aesthetic mappings from data to some visual marking
    3. Geometric objects on the plot
    4. Scales define the range of values
    5. Coordinates to organize location
    6. Labels describe the scale and markings
    7. Facets group into subplots
    8. 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

  • Any graphic can be built from the same components:

    1. data to be drawn from
    2. aesthetic mappings from data to some visual marking
    3. geommetric objects on the plot
    4. scale define the range of values
    5. coordinates 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!

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

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

The Grammar of Graphics (gg): As R Objects

Saving as an object

  • 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

# 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

Data

ggplot(data = mpg)

Data is the source of our data. As part of the tidyverse, ggplot2 requires data to be "tidy"1:

  1. Each variable forms a column

  2. Each observation forms a row

  3. Each observational unit forms a table

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

Data

  • Add a layer with + at the end of a line (never at the beginning!)

  • 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

Data

Aesthetics

+ aes()

Aesthetics map data to visual elements or parameters

The Grammar of Graphics (gg): Aesthetics II

Data

Aesthetics

+ aes()

Aesthetics map data to visual elements or parameters

The Grammar of Graphics (gg): Aesthetics III

Data

Aesthetics

+ aes()

Aesthetics map data to visual elements or parameters

  • displ

  • hwy

  • class

The Grammar of Graphics (gg): Aesthetics III

Data

Aesthetics

+ aes()

Aesthetics map data to visual elements or parameters

  • displ → x

  • hwy → y

  • class → shape, size, color, etc.

The Grammar of Graphics (gg): Aesthetics IV

Data

Aesthetics

+ aes()

Aesthetics map data to visual elements or parameters

The Grammar of Graphics (gg): Aesthetics IV

Data

Aesthetics

+ aes()

Aesthetics map data to visual elements or parameters

aes(x = displ,
    y = hwy,
    color = class)

The Grammar of Graphics (gg): Geoms I

Data

Aesthetics

Geoms

+ geom_*()

Geometric objects displayed on the plot

The Grammar of Graphics (gg): Geoms II

Data

Aesthetics

Geoms

+ geom_*()

Geometric objects displayed on the plot

  • What geoms 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

Data

Aesthetics

Geoms

+ geom_*()

Geometric objects displayed on the plot

##  [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 for many more options

The Grammar of Graphics (gg): Geoms IV

Data

Aesthetics

Geoms

+ geom_*()

Geometric objects displayed on the plot

Or just start typing geom_ in R Studio!

Let's Make a Plot!

ggplot(data = mpg)

Let's Make a Plot!

ggplot(data = mpg)+
  aes(x = displ,
        y = hwy)

Let's Make a Plot!

ggplot(data = mpg)+
  aes(x = displ,
        y = hwy)+
  geom_point()

Let's Make a Plot!

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

Let's Make a Plot!

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

More Geoms

Data

Aesthetics

Geoms

+ geom_*()

geom_*(aes, data, stat, position)

  • 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

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

More Geoms II

Data

Aesthetics

Geoms

+ geom_*()

geom_*(aes, data, stat, position)

  • 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

ggplot(data = mpg)+
  aes(x = class,
      y = hwy)+
  geom_boxplot()

Let's Change Our Plot

ggplot(data = mpg)+
  aes(x = class)+
  geom_bar()

Let's Change Our Plot

ggplot(data = mpg)+
  aes(x = class,
      fill = drv)+
  geom_bar()

Let's Change Our Plot

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

Back to the Original (and saving it)

p <- ggplot(data = mpg)+
  aes(x = displ,
        y = hwy)+
  geom_point(aes(color = class))+
  geom_smooth()
p # show plot

The Grammar of Graphics (gg): Facets I

Data

Aesthetics

Geoms

Facets

+ facet_wrap()

+ facet_grid()

p + facet_wrap(~year)

The Grammar of Graphics (gg): Facets II

Data

Aesthetics

Geoms

Facets

+ facet_wrap()

+ facet_grid()

p + facet_grid(cyl~year)

The Grammar of Graphics (gg): Labels

Data

Aesthetics

Geoms

Facets

Labels

+ labs()

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")

The Grammar of Graphics (gg): Scales

Data

Aesthetics

Geoms

Facets

Labels

Scales

+ scale_*_*()

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

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()

The Grammar of Graphics (gg): Themes

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

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

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_bw()

The Grammar of Graphics (gg): Themes II

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_minimal()

The Grammar of Graphics (gg): Themes III

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_minimal()+
  theme(text = element_text(family = "Fira Sans"))

The Grammar of Graphics (gg): Themes III

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_minimal()+
  theme(text = element_text(family = "Fira Sans"),
        legend.position="bottom")

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

Data

Aesthetics

Geoms

Facets

Labels

Scales

Theme

+ theme_*()

  • ggthemes package adds some other nice themes
# install if you don't have it
# install.packages("ggthemes")
library("ggthemes") # load package

The Grammar of Graphics (gg): Themes IV

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")

The Grammar of Graphics (gg): Themes V

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")

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

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

Mapped vs. Set Aesthetics

  • aesthetics such as size and color can be mapped from data or set to a single value
  • Map inside of aes(), set outside of aes() 
# Point colors are mapped from class data
ggplot(data = mpg, aes(x = displ,
                       y = hwy))+
  geom_point(aes(color = class))+
  geom_smooth()

# 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")

Go Crazy I

# 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")

Go Crazy II

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}")

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

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"

Less is More

"Shoot me"

Less is More:

Try to Show One Trend Really Clearly

New York Times: "How Stable Are Democracies? ‘Warning Signs Are Flashing Red’", Nov 29, 2016

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

RStudio: ggplot2 Cheat Sheet

Reference

On ggplot2

On data visualization

Outline

Plotting in Base R

ggplot2 and the tidyverse

Plot Layers

Some Troubleshooting

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