ggplot2: An Extensible Platform for Publication-quality Graphics
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The document discusses ggplot2, an R package that enables the creation of publication-quality graphics through a grammar of graphics, highlighting its powerful styling options, wide variety of plot types, and sophisticated text annotations. It includes examples of different plot types such as ridgeline plots and simple features with geographic data, as well as mentions of additional packages like ggrepel and patchwork for enhanced annotations and plot composition. Acknowledgments are given to the contributors and supporters of the ggplot2 project and its related extensions.
![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 -...](https://image.slidesharecdn.com/sdss2019-190601162653/85/ggplot2-An-Extensible-Platform-for-Publication-quality-Graphics-19-320.jpg)
ggplot2: An Extensible Platform for Publication-quality Graphics
- 2. Hadley Wickham Thomas Lin Pederson Edzer Pebesma Kamil Slowikowski
- 6. What do we need? • Powerful styling options • Broad selection of plot types • Sophisticated text annotations • Plot composition
- 7. What do we need? • Powerful styling options • Broad selection of plot types • Sophisticated text annotations • Plot composition
- 8. ggplot2: A grammar of graphics in R ggplot(iris) + aes(x = Sepal.Length, y = Sepal.Width, color = Species) + geom_point()
- 9. ggplot2: A grammar of graphics in R ggplot(iris) + aes(x = Sepal.Length, y = Sepal.Width, color = Species) + geom_point() + scale_color_viridis_d()
- 10. 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()
- 11. What do we need? • Powerful styling options • Broad selection of plot types • Sophisticated text annotations • Plot composition
- 12. ggplot2-exts.org
- 14. Ridgeline plots are a variation of density plots ggplot(iris) + aes(x = Sepal.Length, fill = Species) + geom_density(alpha = 0.5)
- 15. Ridgeline plots are a variation of density plots library(ggridges) ggplot(iris) + aes(x = Sepal.Length, y = Species) + geom_density_ridges(alpha = 0.5)
- 16. Ridgeline plots are a variation of density plots library(ggridges) ggplot(iris) + aes(x = Sepal.Length, y = Species) + geom_density_ridges(alpha = 0.5)
- 18. sf: Manipulating and plotting simple features ggplot(TX_income, aes(fill = median_income)) + geom_sf() Edzer Pebesma
- 19. 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 -...
- 20. 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
- 21. sf: Manipulating and plotting simple features ggplot(TX_income, aes(fill = median_income)) + geom_sf() + coord_sf(crs = 3338) # NAD83, Alaska Albers Edzer Pebesma
- 22. 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
- 23. What do we need? • Powerful styling options • Broad selection of plot types • Sophisticated text annotations • Plot composition
- 25. ggrepel: Smart annotation of plot elements library(ggrepel) mtcars$car <- rownames(mtcars) # add column of car names mtcars %>% filter(disp < 200) %>% ggplot(aes(disp, mpg, label = car)) + geom_point() + geom_text_repel() Kamil Slowikowski
- 26. ggrepel: Smart annotation of plot elements library(ggrepel) mtcars$car <- rownames(mtcars) mtcars %>% filter(disp < 200) %>% ggplot(aes(disp, mpg, label = car)) + geom_point() + geom_text_repel() Kamil Slowikowski
- 27. 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
- 28. 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
- 29. 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
- 30. 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
- 31. What do we need? • Powerful styling options • Broad selection of plot types • Sophisticated text annotations • Plot composition
- 33. 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
- 34. 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
- 35. 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
- 36. 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
- 37. 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
- 38. 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)
- 39. 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
- 40. 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()
- 42. 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