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ggplot2: An Extensible Platform for Publication-quality Graphics

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Claus Wilke
Claus Wilke

Talk given at the Symposium on Data Science and Statistics in Bellevue, Washington, May 29 - June 1, 2019, organized by the American Statistical Association and Interface Foundation of North America.

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ggplot2: An extensible platform for publication-quality graphics Claus O. Wilke The University of Texas at Austin @clauswilke clauswilke
Hadley Wickham Thomas Lin Pederson Edzer Pebesma Kamil Slowikowski
ggplot2: An Extensible Platform for Publication-quality Graphics
ggplot2: An Extensible Platform for Publication-quality Graphics
serialmentor.com/dataviz
What do we need? • Powerful styling options • Broad selection of plot types • Sophisticated text annotations • Plot composition
What do we need? • Powerful styling options • Broad selection of plot types • Sophisticated text annotations • Plot composition
ggplot2: A grammar of graphics in R ggplot(iris) + aes(x = Sepal.Length, y = Sepal.Width, color = Species) + geom_point()
ggplot2: A grammar of graphics in R ggplot(iris) + aes(x = Sepal.Length, y = Sepal.Width, color = Species) + geom_point() + scale_color_viridis_d()
ggplot2: A grammar of graphics in R ggplot(iris) + aes(x = Sepal.Length, y = Sepal.Width, color = Species) + geom_point() + scale_color_viridis_d() + theme_bw()
What do we need? • Powerful styling options • Broad selection of plot types • Sophisticated text annotations • Plot composition
ggplot2-exts.org
Example 1: Ridgeline plots
Ridgeline plots are a variation of density plots ggplot(iris) + aes(x = Sepal.Length, fill = Species) + geom_density(alpha = 0.5)
Ridgeline plots are a variation of density plots library(ggridges) ggplot(iris) + aes(x = Sepal.Length, y = Species) + geom_density_ridges(alpha = 0.5)
Ridgeline plots are a variation of density plots library(ggridges) ggplot(iris) + aes(x = Sepal.Length, y = Species) + geom_density_ridges(alpha = 0.5)
Example 2: Simple features
sf: Manipulating and plotting simple features ggplot(TX_income, aes(fill = median_income)) + geom_sf() Edzer Pebesma
TX_income[1:3,] #> Simple feature collection with 3 features and 15 fields #> geometry type: MULTIPOLYGON #> dimension: XY #> bbox: xmin: -579099 ymin: -958812.5 xmax: -168152.2 ymax: -94284.87 #> epsg (SRID): NA #> proj4string: +proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=37.5 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs #> STATEFP COUNTYFP COUNTYNS AFFGEOID GEOID NAME LSAD ALAND #> 1 48 421 01383996 0500000US48421 48421 Sherman 06 2390651189 #> 2 48 493 01384032 0500000US48493 48493 Wilson 06 2081662847 #> 3 48 115 01383843 0500000US48115 48115 Dawson 06 2331781556 #> AWATER name median_income median_income_moe #> 1 428754 Sherman County, Texas 51987 4386 #> 2 12111367 Wilson County, Texas 68100 3328 #> 3 4720730 Dawson County, Texas 41095 2591 #> population area popdens #> 1 3066 2387929738 [m^2] 1.283957e-06 [1/m^2] #> 2 45509 2086919506 [m^2] 2.180678e-05 [1/m^2] #> 3 13542 2336898468 [m^2] 5.794860e-06 [1/m^2] #> geometry #> 1 MULTIPOLYGON (((-546533.1 -... #> 2 MULTIPOLYGON (((-234487.5 -... #> 3 MULTIPOLYGON (((-576468.3 -...
sf: Manipulating and plotting simple features ggplot(TX_income, aes(fill = median_income)) + geom_sf() + coord_sf(crs = 4326) # Cartesian longitude and latitude Edzer Pebesma
sf: Manipulating and plotting simple features ggplot(TX_income, aes(fill = median_income)) + geom_sf() + coord_sf(crs = 3338) # NAD83, Alaska Albers Edzer Pebesma
sf: Manipulating and plotting simple features # with formatting tweaks ggplot(TX_income, aes(fill = median_income)) + geom_sf(color = "black", size = 0.2) + scale_fill_viridis_c( name = "income", labels = dollar ) + theme_minimal() Edzer Pebesma
What do we need? • Powerful styling options • Broad selection of plot types • Sophisticated text annotations • Plot composition
ggplot2: An Extensible Platform for Publication-quality Graphics
ggrepel: Smart annotation of plot elements library(ggrepel) mtcars$car <- rownames(mtcars) # add column of car names mtcars %>% filter(disp < 200) %>% ggplot(aes(disp, mpg, label = car)) + geom_point() + geom_text_repel() Kamil Slowikowski
ggrepel: Smart annotation of plot elements library(ggrepel) mtcars$car <- rownames(mtcars) mtcars %>% filter(disp < 200) %>% ggplot(aes(disp, mpg, label = car)) + geom_point() + geom_text_repel() Kamil Slowikowski
ggrepel: Smart annotation of plot elements mtcars %>% filter(disp < 200) %>% ggplot(aes(disp, mpg, label = car)) + geom_point() + geom_text_repel(xlim = c(180, 250), hjust = 0) + xlim(70, 240) Kamil Slowikowski
ggrepel: Smart annotation of plot elements mtcars %>% filter(disp < 200) %>% ggplot(aes(disp, mpg, label = car)) + geom_point() + geom_text_repel(xlim = c(180, 250), hjust = 0) + xlim(70, 240) Kamil Slowikowski
ggrepel: Smart annotation of plot elements mtcars %>% filter(disp < 200) %>% ggplot(aes(disp, mpg)) + geom_point(aes(color = factor(am))) + geom_text_repel(aes(label = ifelse(am == 0, car, ""))) + scale_color_manual( name = "transmission", labels = c("manual", "automatic"), values = c("red", "gray50") ) Kamil Slowikowski
ggrepel: Smart annotation of plot elements mtcars %>% filter(disp < 200) %>% ggplot(aes(disp, mpg)) + geom_point(aes(color = factor(am))) + geom_text_repel(aes(label = ifelse(am == 0, car, ""))) + scale_color_manual( name = "transmission", labels = c("manual", "automatic"), values = c("red", "gray50") ) Kamil Slowikowski
What do we need? • Powerful styling options • Broad selection of plot types • Sophisticated text annotations • Plot composition
ggplot2: An Extensible Platform for Publication-quality Graphics
patchwork: A grammar of plot composition p1 <- ggplot(iris, aes(Sepal.Length, Sepal.Width)) + geom_point() p2 <- ggplot(iris, aes(Species, Sepal.Length)) + geom_boxplot() # add plots to place them side-by-side p1 + p2 Thomas Lin Pederson
Thomas Lin Pederson patchwork: A grammar of plot composition p1 <- ggplot(iris, aes(Sepal.Length, Sepal.Width)) + geom_point() p2 <- ggplot(iris, aes(Species, Sepal.Length)) + geom_boxplot() # add plots to place them side-by-side p1 + p2
patchwork: A grammar of plot composition p1 <- ggplot(iris, aes(Sepal.Length, Sepal.Width)) + geom_point() p2 <- ggplot(iris, aes(Species, Sepal.Length)) + geom_boxplot() # divide plots to place them on top of each other p1 / p2 Thomas Lin Pederson
Thomas Lin Pederson patchwork: A grammar of plot composition p1 <- ggplot(iris, aes(Sepal.Length, Sepal.Width)) + geom_point() p2 <- ggplot(iris, aes(Species, Sepal.Length)) + geom_boxplot() # divide plots to place them on top of each other p1 / p2
patchwork: A grammar of plot composition p3 <- ggplot(iris, aes(Sepal.Length)) + geom_density(fill = "gray60") + facet_wrap(~Species) # nested arrangements are possible p3 / (p1 + p2) Thomas Lin Pederson
Thomas Lin Pederson patchwork: A grammar of plot composition p3 <- ggplot(iris, aes(Sepal.Length)) + geom_density(fill = "gray60") + facet_wrap(~Species) # nested arrangements are possible p3 / (p1 + p2)
patchwork: A grammar of plot composition p3 <- ggplot(iris, aes(Sepal.Length)) + geom_density(fill = "gray60") + facet_wrap(~Species) # apply theme recursively to all plots p3 / (p1 + p2) & theme_minimal() Thomas Lin Pederson
Thomas Lin Pederson patchwork: A grammar of plot composition p3 <- ggplot(iris, aes(Sepal.Length)) + geom_density(fill = "gray60") + facet_wrap(~Species) # apply theme recursively to all plots p3 / (p1 + p2) & theme_minimal()
Read the source, Luke
Acknowledgments ggplot2 team Hadley Wickham Winston Chang Lionel Henry Thomas Lin Pedersen Kohske Takahashi Hiroaki Yutani Kara Woo Specific package authors Edzer Pebesma (sf) Kamil Slowikowski (ggrepel) Thomas Lin Pedersen (patchwork) The broader R community Mara Averick Yihui Xie RStudio many others Claus O. Wilke receives funding from: UT Austin, R Consortium, NIH, NSF

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ggplot2: An Extensible Platform for Publication-quality Graphics