Data visualization, focusing on ggplot and multilevel data
Ben Bolker
08:36 30 July 2018
Licensed under the Creative Commons attribution-noncommercial license. Please share & remix noncommercially, mentioning its origin.
Basic criteria for data presentation
Keynote talk by John Rauser (2016) is strongly recommended.
Visual perception of quantitative information: Cleveland hierarchy (W. S. Cleveland and McGill 1984,W. S. Cleveland and McGill (1987),W. Cleveland (1993))
cleveland
Techniques for multilevel data
- faceting (= trellis plots = small multiples) vs grouping (“spaghetti plots”)
- join data within a group by lines (perhaps thin/transparent)
- colour lines by group (more useful for explanatory than presentation graphics)
- dynamic graphics (hovertext)
- other grouping techniques:
ggalt::geom_encircle,stat_centseg(from../R/geom_cstar.R) - depends on context: how many groups, what kind of predictors?
ggplot2 makes it fairly easy to do a simple two-stage analysis on the fly, e.g. with the CBPP data discussed below:
geom_smooth(aes(colour=herd,weight=size),
method="glm",
method.args=list(family=binomial),
se=FALSE)(ignore glm.fit warnings if you try this)
ggplot
- newest
- based on Wilkinson’s “Grammar of Graphics”
- documented in a book (see below) and on a web site, as well as an active mailing list
- explicit mapping from variables to “aesthetics”: e.g. x, y, colour, size, shape
- implements faceting
- some data summaries etc. built in
- easier to overlay multiple data sets, data summaries, model predictions etc.
- no 3D plots (although see the gg3D package)
- rendering can be slow
ggalt,gridExtra,ggExtra,cowplot,directlabelspackages useful- ggplot gallery
ggplot intro
mappings + geoms
See Karthik Ram’s ggplot intro or my intro for disease ecologists, among many others.
Example/exercise
library("ggplot2"); theme_set(theme_bw())
library(ggalt)
source("../R/geom_cstar.R")cbpp data set
Contagious bovine pleuropneumonia (CBPP): from Lesnoff et al. (2004), via the lme4 package. See ?lme4::cbpp for details.
data("cbpp",package="lme4")
## make period *numeric* so lines will be connected/grouping won't happen
cbpp2 <- transform(cbpp,period=as.numeric(as.character(period)))
g0 <- ggplot(cbpp2,aes(period,incidence/size))spaghetti plot
g1 <- g0+geom_line(aes(colour=herd))+geom_point(aes(size=size,colour=herd))Do we need the colours?
g2 <- g0+geom_line(aes(group=herd))+geom_point(aes(size=size,group=herd))Facet instead:
g4 <- g1+facet_wrap(~herd)Order by average prop. incidence, using the %+% trick:
cbpp2R <- transform(cbpp2,herd=reorder(herd,incidence/size))
g4 %+% cbpp2R## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?gopher tortoise mycoplasma data
Gopher tortoise data (from Ozgul et al. (2009), see ecostats chapter)
Plot density of shells from freshly dead tortoises (shells/Area) as a function of mycoplasmal prevalence (%, prev): you may want to consider site, year of collection, or population density as well.
load("../data/gopherdat2.RData")
g5 <- ggplot(Gdat,aes(prev,shells/Area))+geom_point()g5+geom_encircle(aes(group=Site))
g5+stat_centseg(aes(group=Site),cfun=mean)clipping data
Data from
load("../data/Banta.RData")
dat.tf$ltf1 <- log(dat.tf$total.fruits+1)
ggplot(dat.tf,aes(nutrient,ltf1))+geom_point()+
facet_wrap(~amd)
## calc mean by group
aggdat <- aggregate(ltf1~popu+gen+amd+nutrient,FUN=mean,data=dat.tf)sessionInfo()## R Under development (unstable) (2018-07-26 r75007)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.5 LTS
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## Matrix products: default
## BLAS: /usr/local/lib/R/lib/libRblas.so
## LAPACK: /usr/local/lib/R/lib/libRlapack.so
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## locale:
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## [5] LC_MONETARY=en_CA.UTF8 LC_MESSAGES=en_CA.UTF8
## [7] LC_PAPER=en_CA.UTF8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
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## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggalt_0.4.0 ggplot2_3.0.0 knitr_1.20
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.18 RColorBrewer_1.1-2 pillar_1.3.0
## [4] compiler_3.6.0 plyr_1.8.4 bindr_0.1.1
## [7] tools_3.6.0 extrafont_0.17 digest_0.6.15
## [10] evaluate_0.11 tibble_1.4.2 gtable_0.2.0
## [13] pkgconfig_2.0.1 rlang_0.2.1 yaml_2.2.0
## [16] bindrcpp_0.2.2 Rttf2pt1_1.3.7 withr_2.1.2
## [19] dplyr_0.7.6 stringr_1.3.1 maps_3.3.0
## [22] rprojroot_1.3-2 grid_3.6.0 tidyselect_0.2.4
## [25] glue_1.3.0 R6_2.2.2 rmarkdown_1.10
## [28] extrafontdb_1.0 purrr_0.2.5 magrittr_1.5
## [31] backports_1.1.2 scales_0.5.0.9000 htmltools_0.3.6
## [34] MASS_7.3-50 proj4_1.0-8 assertthat_0.2.0
## [37] colorspace_1.3-2 labeling_0.3 KernSmooth_2.23-15
## [40] ash_1.0-15 stringi_1.2.4 lazyeval_0.2.1
## [43] munsell_0.5.0 crayon_1.3.4References
Cleveland, William. 1993. Visualizing Data. Summit, NJ: Hobart Press.
Cleveland, William S., and Robert McGill. 1984. “Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods.” Journal of the American Statistical Association 79 (387): 531–54. doi:10.2307/2288400.
———. 1987. “Graphical Perception: The Visual Decoding of Quantitative Information on Graphical Displays of Data.” Journal of the Royal Statistical Society. Series A (General) 150 (3): 192–229. doi:10.2307/2981473.
John Rauser. 2016. “How Humans See Data.” https://www.youtube.com/watch?v=fSgEeI2Xpdc.
Lesnoff, Matthieu, Géraud Laval, Pascal Bonnet, Sintayehu Abdicho, Asseguid Workalemahu, Daniel Kifle, Armelle Peyraud, Renaud Lancelot, and François Thiaucourt. 2004. “Within-Herd Spread of Contagious Bovine Pleuropneumonia in Ethiopian Highlands.” Preventive Veterinary Medicine 64 (1): 27–40. doi:10.1016/j.prevetmed.2004.03.005.
Ozgul, Arpat, Madan K Oli, Benjamin M Bolker, and Carolina Perez-Heydrich. 2009. “Upper Respiratory Tract Disease, Force of Infection, and Effects on Survival of Gopher Tortoises.” Ecological Applications 19 (3): 786–98. http://www.ncbi.nlm.nih.gov/pubmed/19425439.