## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set( fig.align = "center", fig.alt = "Figure generated by zenplots; see caption and/or surrounding text for details." ) has_rgraphviz <- requireNamespace("Rgraphviz", quietly = TRUE) has_pairviz <- requireNamespace("PairViz", quietly = TRUE) has_qqtest <- requireNamespace("qqtest", quietly = TRUE) rebuild_figs <- identical(Sys.getenv("ZENPLOTS_REBUILD"), "TRUE") && has_rgraphviz note_rgraphviz <- function() { if (!has_rgraphviz) { cat("> **Optional dependency**: All graphs in this vignette can be displayed as Graphviz plots using **Rgraphviz** (Bioconductor).\n", "> To install locally:\n\n", "```r\n", "if (!requireNamespace(\"BiocManager\", quietly=TRUE)) install.packages(\"BiocManager\")\n", "BiocManager::install(\"Rgraphviz\")\n", "```\n\n", sep = "") } } install_rgraphviz <- function() { cat("> **Optional dependency**: This graph, and any other graph, in this vignette can be displayed as a Graphviz plot using the plot() method from **Rgraphviz**.\n", "> To install locally:\n\n", "```r\n", "if (!requireNamespace(\"BiocManager\", quietly=TRUE)) install.packages(\"BiocManager\")\n", "BiocManager::install(\"Rgraphviz\")\n", "```\n\n", sep = "") } ## ----message=FALSE, warning=FALSE--------------------------------------------- library(zenplots) ## ----rgraphviz-note, results='asis', echo=FALSE------------------------------- note_rgraphviz() ## ----message=FALSE, warning=FALSE--------------------------------------------- if (!has_pairviz) { cat("> **Optional dependency**: Examples using the **PairViz** package are skipped if it’s not installed. Install with `install.packages(\"PairViz\")`.\n\n") } else { library(PairViz) } ## ----------------------------------------------------------------------------- head(attenu) ## ----echo=FALSE--------------------------------------------------------------- names(attenu) ## ----message=FALSE, warning=FALSE, eval=has_pairviz--------------------------- ## Since attenu has 5 variates, the complete graph has n=5 nodes ## and an Euler sequence is given as PairViz::eseq(5) ## ----message=FALSE, warning=FALSE, eval=has_pairviz--------------------------- names(attenu)[PairViz::eseq(5)] ## ----echo=FALSE, message=FALSE, warning=FALSE, fig.align="center", out.width="65%", fig.cap="Precomputed complete graph.", fig.alt="Complete graph whose nodes are the five variate names of the attenu dataset."---- knitr::include_graphics("figures/intro_complete_graph.png") ## ----install_rgraphviz, results='asis', echo=FALSE, eval = TRUE--------------- install_rgraphviz() ## ----plot-a-graph, eval = FALSE----------------------------------------------- # mygraph <- PairViz::mk_complete_graph(names(attenu)) # Rgraphviz::plot(mygraph) ## ----message=FALSE, warning=FALSE, fig.align="center"------------------------- zenpath(5) ## ----message=FALSE, warning=FALSE, fig.align="center"------------------------- ## Back loading ensures all pairs appear latest (back) for ## high values of the indices. zenpath(5, method = "back.loaded") ## Frot loading ensures all pairs appear earliest (front) for ## low values of the indices. zenpath(5, method = "front.loaded") ## Balanced loading ensures all pairs appear in groups of all ## indices (Hamiltonian paths -> a Hamiltonian decomposition of the Eulerian) zenpath(5, method = "balanced") ## ----zenpath, echo= FALSE, include=FALSE, fig.align="center", fig.alt="Plot of the three different Euler sequences: back loaded, balanced, and front loaded."---- parOptions <- par(mfrow=c(1,3), mar=c(4, 2,2,0),oma=rep(0,4)) back <- zenpath(15, method = "back.loaded") balanced <- zenpath(15, method = "balanced") front <- zenpath(15, method = "front.loaded") n <- length(zenpath(15)) plot_chars <- letters[1:15] plot(back, n - 1:n,type="b", ylab="", xlab="", main="Back loaded", pch=plot_chars[back], bty="n", xaxt="n", yaxt="n") plot(balanced, n - 1:n,type="b", ylab="", xlab="", main="Balanced", pch=plot_chars[balanced], bty="n", xaxt="n", yaxt="n") plot(front, n - 1:n,type="b", ylab="", xlab="", main="Front loaded", pch=plot_chars[front], bty="n", xaxt="n", yaxt="n") par(parOptions) ## ----message=FALSE, warning=FALSE, fig.align="center", fig.width=7, fig.height=7, fig.alt="Pairs plot of the attenu data."---- ## We remove the space between plots and suppress the axes ## so as to give maximal space to the individual scatterplots. ## We also choose a different plotting character and reduce ## its size to better distinguish points. pairs(attenu, oma=rep(0,4), gap=0, xaxt="n", yaxt="n") ## ----message=FALSE, warning=FALSE, fig.align="center", fig.width=7, fig.height=7, fig.alt = "zenplot showing scatterplots of all pairs of attenu variates"---- ## Plotting character and size are chosen to match that ## of the pairs plot. ## zenpath ensures that all pairs of variates appear ## in the zenplot. ## The last argument, n2dcol, is chosen so that the zenplot ## has the same number of plots across the page as does the ## pairs plot. zenplot(attenu[, zenpath(ncol(attenu))], n2dcol=4) ## ----message=FALSE, warning=FALSE, fig.align="center", fig.width=7, fig.height=7, fig.alt = "zenplot showing directions of the layout path along all pairs of attenu variates"---- ## Call zenplot exactly as before, except that each scatterplot is replaced ## by an arrow that shows the direction of the layout. zenplot(attenu[, zenpath(ncol(attenu))], plot2d="arrow", n2dcol=4) ## ----message=FALSE, warning=FALSE, fig.align="center", fig.width=10, fig.height=6, fig.alt = "Effect of n2dcol parameter of zenplot layout; odd numbers produce tighter layouts."---- ## The default n2dcol is used zenplot(attenu[, zenpath(ncol(attenu))], n2dcol=5) ## ----message=FALSE, warning=FALSE, fig.align="center", fig.width=6, fig.height=10, fig.alt = "Default n2dcol parameter of zenplot layout; not always compact layout."---- ## The default n2dcol is used zenplot(attenu[, zenpath(ncol(attenu))]) ## ----message=FALSE, warning=FALSE, fig.align="center", fig.width=6, fig.height=10, fig.alt = "Default n2dcol parameter with arrows instead of scatterplots."---- ## The directions zenplot(attenu[, zenpath(ncol(attenu))], plot2d="arrow") ## ----------------------------------------------------------------------------- ## Access the German election data from zenplots package data(de_elect) ## ----message=FALSE, warning=FALSE, fig.align="center", fig.width=7, fig.height=7---- ## pairs(de_elect[,1:34], oma=rep(0,4), gap=0, pch=".", xaxt="n", yaxt="n") ## ----message=FALSE, warning=FALSE, fig.align="center", fig.width=7, fig.height=7---- ## Try invoking the plot with the following ## zenplot(de_elect[,zenpath(68)], pch=".", n2dcol="square",col=adjustcolor("black",0.5)) ## ----message=FALSE, warning=FALSE, fig.align="center", fig.width=7, fig.height=7---- ## pairs(de_elect, oma=rep(0,4), gap=0, pch=".", xaxt="n", yaxt="n",col=adjustcolor("black",0.5)) ## ----message=FALSE, warning=FALSE--------------------------------------------- Education <- c("School.finishers", "School.wo.2nd", "School.2nd", "School.Real", "School.UED") Employment <- c("Employed", "FFF", "Industry", "CTT", "OS" ) ## ----message=FALSE, warning=FALSE, fig.align="center", fig.width=5, fig.height=6, fig.alt="German Election data using zenplot to compare pairs within different groups."---- EducationData <- de_elect[, Education] EmploymentData <- de_elect[, Employment] ## Plot all pairs within each group zenplot(list(Educ= EducationData[, zenpath(ncol(EducationData))], Empl= EmploymentData[, zenpath(ncol(EmploymentData))])) ## ----------------------------------------------------------------------------- ## Grouping variates in the German election data Regions <- c("District", "State", "Density") PopDist <- c("Men", "Citizens", "Pop.18.25", "Pop.25.35", "Pop.35.60", "Pop.g.60") PopChange <- c("Births", "Deaths", "Move.in", "Move.out", "Increase") Agriculture <- c("Farms", "Agriculture") Mining <- c("Mining", "Mining.employees") Apt <- c("Apt.new", "Apt") Motorized <- c("Motorized") Education <- c("School.finishers", "School.wo.2nd", "School.2nd", "School.Real", "School.UED") Unemployment <- c("Unemployment.03", "Unemployment.04") Employment <- c("Employed", "FFF", "Industry", "CTT", "OS" ) Voting.05 <- c("Voters.05", "Votes.05", "Invalid.05", "Valid.05") Voting.02 <- c("Voters.02", "Votes.02", "Invalid.02", "Valid.02") Voting <- c(Voting.02, Voting.05) VotesByParty.02 <- c("Votes.SPD.02", "Votes.CDU.CSU.02", "Votes.Gruene.02", "Votes.FDP.02", "Votes.Linke.02") VotesByParty.05 <- c("Votes.SPD.05", "Votes.CDU.CSU.05", "Votes.Gruene.05", "Votes.FDP.05", "Votes.Linke.05") VotesByParty <- c(VotesByParty.02, VotesByParty.05) PercentByParty.02 <- c("SPD.02", "CDU.CSU.02", "Gruene.02", "FDP.02", "Linke.02", "Others.02") PercentByParty.05 <- c("SPD.05", "CDU.CSU.05", "Gruene.05", "FDP.05", "Linke.05", "Others.05") PercentByParty <- c(PercentByParty.02, PercentByParty.05) ## ----message=FALSE, warning=FALSE, fig.align="center", fig.width=11, fig.height=14, fig.alt = "Zenplot of all pairs within each of several groups of variates from the German Election data."---- groups <- list(Regions=Regions, Pop=PopDist, Change = PopChange, Agric=Agriculture, Mining=Mining, Apt=Apt, Cars=Motorized, Educ=Education, Unemployed=Unemployment, Employed=Employment#, # Vote02=Voting.02, Vote05=Voting.05, # Party02=VotesByParty.02, Party05=VotesByParty.05, # Perc02=PercentByParty.02, Perc05=PercentByParty.05 ) group_paths <- lapply(groups, FUN= function(g) g[zenpath(length(g), method = "front.loaded")] ) x <- groupData(de_elect, indices=group_paths) zenplot(x, pch = ".", cex=0.7, col = "grey10") ## ----message=FALSE, warning=FALSE, fig.align="center", fig.width=10, fig.height=10, fig.alt = "As the previous plot but with redefined short names."---- # ## Grouping variates in the German election data RegionsShort <- c("ED", "State", "density") PopDistShort <- c("men", "citizen", "18-25", "25-35", "35-60", "> 60") PopChangeShort <- c("births", "deaths", "in", "out", "up") AgricultureShort <- c("farms", "hectares") MiningShort <- c("firms", "employees") AptShort <- c("new", "all") TransportationShort <- c("cars") EducationShort <- c("finishers", "no.2nd", "2nd", "Real", "UED") UnemploymentShort<- c("03", "04") EmploymentShort <- c("employed", "FFF", "Industry", "CTT", "OS" ) Voting.05Short <- c("eligible", "votes", "invalid", "valid") Voting.02Short <- c("eligible", "votes", "invalid", "valid") VotesByParty.02Short <- c("SPD", "CDU.CSU", "Gruene", "FDP", "Linke") VotesByParty.05Short <- c("SPD", "CDU.CSU", "Gruene", "FDP", "Linke") PercentByParty.02Short <- c("SPD", "CDU.CSU", "Gruene", "FDP", "Linke", "rest") PercentByParty.05Short <- c("SPD", "CDU.CSU", "Gruene", "FDP", "Linke", "rest") shortNames <- list(RegionsShort, PopDistShort, PopChangeShort, AgricultureShort, MiningShort, AptShort, TransportationShort, EducationShort, UnemploymentShort, EmploymentShort, Voting.05Short, Voting.02Short, VotesByParty.02Short, VotesByParty.05Short, PercentByParty.02Short, PercentByParty.05Short) # Now replace the names in x by these. nGroups <- length(x) for (i in 1:nGroups) { longNames <- colnames(x[[i]]) newNames <- shortNames[[i]] oldNames <- groups[[i]] #print(longNames) #print(newNames) for (j in 1:length(longNames)) { for (k in 1:length(newNames)) { if (grepl(oldNames[k], longNames[j])) { longNames[longNames == longNames[j]] <- newNames[k] } } } colnames(x[[i]]) <- longNames } zenplot(x, pch = ".", cex=0.75) ## ----message=FALSE, warning=FALSE, fig.align="center", fig.width=10, fig.height=16, fig.alt="Zenplot where the method eulerian.cross has one group of variates crossed with another."---- crossedGroups <- c(Employment, Education) crossedPaths <- zenpath(c(length(Employment), length(Education)), method="eulerian.cross") zenplot(de_elect[,crossedGroups][crossedPaths]) ## ----message=FALSE, warning=FALSE, fig.align="center", fig.width=5, fig.height=2, fig.alt="Zenplot lays out boxplots in a row across the plot, using the turns argument."---- earthquakes <- attenu[, c(1,2,4,5)] # ignore the station id zenplot(earthquakes, plot1d="boxplot", plot2d=NULL, width1d=5, width2d=1, turns=c("r","r","r","r","r","r","r")) ## ----message=FALSE, warning=FALSE, fig.align="center", fig.width=4, fig.height=4, fig.alt="zenplot showing four boxplots laid out in a cross, using the turns argument."---- zenplot(earthquakes, plot1d="boxplot", plot2d=NULL, width1d=1, width2d=1, # now widths must be the same turns=c("r","d","d","l","l","u","u")) ## ----message=FALSE, warning=FALSE, fig.align="center", fig.width=4, fig.height=4, fig.alt="Arrows laid out using turns argument."---- zenplot(earthquakes, plot1d= "arrow", plot2d="arrow", width1d=1, width2d=2, turns=c("r","d","d","l","l","u","u")) ## ----message=FALSE, warning=FALSE, fig.align="center", fig.width=4, fig.height=4, fig.alt="Zenplot with user defined 1d and 2d plots, laid out using turns."---- zenplot(earthquakes, plot1d = function(zargs, ...) { rect_1d_graphics(zargs, ...) arrow_1d_graphics(zargs, col="firebrick", lwd=3, add=TRUE, ...) }, plot2d = function(zargs, ...) { rect_2d_graphics(zargs, ...) arrow_2d_graphics(zargs, col="steelblue", lwd=3, lty=2, add=TRUE, ...) }, width1d = 1, width2d = 2, turns=c("r","d","d","l","l","u","u")) ## ----message=FALSE, warning=FALSE, results="hide", fig.align="center", fig.width=6, fig.height=3, fig.alt="Novel plot of alternating histograms and qqtest plots via user defined and built in one and two dimensional functions."---- # # qqtest is preferred to the base qqplot so will be used if available. # has_qqtest <- requireNamespace("qqtest", quietly = TRUE) zenplot(earthquakes[, zenpath(ncol(earthquakes))], width1d = 1, width2d = 2, n2dcols = 5, plot1d = function(zargs, ...) { r <- extract_1d(zargs) # extract args for 1d col <- grDevices::adjustcolor( if (r$horizontal) "firebrick" else "steelblue", alpha.f = 0.7) hist_1d_graphics(zargs, col = col, ...) }, plot2d = function(zargs, ...) { r <- extract_2d(zargs) # extract args for 2d x <- as.matrix(r$x) # r$x is a data frame with one named variate # one-column data frame -> matrix xlim <- r$xlim y <- as.matrix(r$y) # r$y is a data frame with one named variate ylim <- r$ylim # use the better quantile-quantile plot (qqtest) if available # qqtest has bands testing whether y comes from the same # distribution as x or not; all points in the band # indicate no evidence against the hypothesis of the # same distribution, if (has_qqtest) { # qqtest draws directly into the current region qqtest::qqtest(y, dataTest = x, xlim = xlim, ylim = ylim, cex = 0.3, col = "black", pch = 19, legend = FALSE, main = "", axes = FALSE, ...) } else { # base fallback: # construct the qqplot by hand # adding the points into our region xx <<- stats::na.omit(as.numeric(x)) yy <<- stats::na.omit(as.numeric(y)) qq <- stats::qqplot(xx, yy, plot.it = FALSE) plot_region(xlim, ylim) box() points(qq$x, qq$y, pch = 19, cex = 0.3) } })