advanced data visualization in r- somes examples

Prepared by Volkan OBANAdvanced Data Visualization in R- Somes Examples.

geomorph package in R....Example:

Code:

>library(geomorph)

> data(scallopPLY)> ply <- scallopPLY$ply> digitdat <- scallopPLY$coords> plotspec(spec=ply,digitspec=digitdat,fixed=16, centered =TRUE)

Example:> data(scallops)> Y.gpa<-gpagen(A=scallops$coorddata, curves=scallops$curvslide,surfaces=scallops$surfslide)> ref<-mshape(Y.gpa$coords)> plotRefToTarget(ref,Y.gpa$coords[,,1],method="TPS", mag=3)

Reference: http://www.public.iastate.edu/~dcadams/PDFPubs/Quick%20Guide%20to%20Geomorph%20v2.0.pdf

Example:> boxplot.ej <- function(y, xloc = 1, width.box = 0.25, lwd.box = 2, width.hor = 0.25, + lwd.hor = 2, range.wisk = 1.5, lwd.wisk = 2, pch.box = 16, cex.boxpoint = 2, + plot.outliers = FALSE, pch.out = 1, cex.out = 1, color = "black") {+ + # makes boxplot with dot as median and solid whisker Interquartile range =+ # (.75 quantile) - (.25 quantile). Note: Wiskers are not always symmetrical;+ # top wisker extends up to max(y) constrained by y <= (.75 quantile) ++ # range.wisk*Interquartile range bottom whisker is determined by min(y)+ # constrained by y >= (.25 quantile) - range.wisk*Interquartile range

+ + Q <- quantile(y, c(0.25, 0.5, 0.75))+ names(Q) <- NULL # gets rid of percentages+ IQ.range <- Q[3] - Q[1]+ low <- Q[1] - range.wisk * IQ.range+ high <- Q[3] + range.wisk * IQ.range+ index <- which((y >= low) & (y <= high))+ wisk.low <- min(y[index])+ wisk.high <- max(y[index])+ outliers <- y[which((y < low) | (y > high))]+ + # plot median:+ points(xloc, Q[2], pch = pch.box, cex = cex.boxpoint, col = color)+ + # plot box:+ xleft <- xloc - width.box/2+ xright <- xloc + width.box/2+ ybottom <- Q[1]+ ytop <- Q[3]+ rect(xleft, ybottom, xright, ytop, lwd = lwd.box, border = color)+ + # plot whiskers:+ segments(xloc, wisk.low, xloc, Q[1], lwd = lwd.wisk, col = color)+ segments(xloc, Q[3], xloc, wisk.high, lwd = lwd.wisk, col = color)+ + # plot horizontal segments:+ x0 <- xloc - width.hor/2+ x1 <- xloc + width.hor/2+ segments(x0, wisk.low, x1, wisk.low, lwd = lwd.hor, col = color)+ segments(x0, wisk.high, x1, wisk.high, lwd = lwd.hor, col = color)+ + # plot outliers:+ if (plot.outliers == TRUE) {+ xloc.p <- rep(xloc, length(outliers))+ points(xloc.p, outliers, pch = pch.out, cex = cex.out, col = color)+ }+ }> > RT.hf.sp <- rnorm(1000, mean = 0.41, sd = 0.008)> RT.lf.sp <- rnorm(1000, mean = 0.43, sd = 0.01)> RT.vlf.sp <- rnorm(1000, mean = 0.425, sd = 0.012)> RT.hf.ac <- rnorm(1000, mean = 0.46, sd = 0.008)> RT.lf.ac <- rnorm(1000, mean = 0.51, sd = 0.01)> RT.vlf.ac <- rnorm(1000, mean = 0.52, sd = 0.012)> > ps <- 1 # size of boxpoint> par(cex.main = 1.5, mar = c(5, 6, 4, 5) + 0.1, mgp = c(3.5, 1, 0), cex.lab = 1.5, + font.lab = 2, cex.axis = 1.3, bty = "n", las = 1)> x <- c(1, 2, 3, 4)> plot(x, c(-10, -10, -10, -10), type = "p", ylab = " ", xlab = " ", cex = 1.5, + ylim = c(0.3, 0.6), xlim = c(1, 4), lwd = 2, pch = 5, axes = FALSE, main = " ")> axis(1, at = c(1.5, 2.5, 3.5), labels = c("HF", "LF", "VLF"))> mtext("Word Frequency", side = 1, line = 3, cex = 1.5, font = 2)> axis(2, pos = 1.1)> par(las = 0)> mtext("Group Mean M", side = 2, line = 2.9, cex = 1.5, font = 2)>

> x <- c(1.5, 2.5, 3.5)> boxplot.ej(RT.hf.sp, xloc = 1.5, cex.boxpoint = ps)> boxplot.ej(RT.hf.ac, xloc = 1.5, cex.boxpoint = ps, color = "grey")> boxplot.ej(RT.lf.sp, xloc = 2.5, cex.boxpoint = ps)> boxplot.ej(RT.lf.ac, xloc = 2.5, cex.boxpoint = ps, color = "grey")> boxplot.ej(RT.vlf.sp, xloc = 3.5, cex.boxpoint = ps)> boxplot.ej(RT.vlf.ac, xloc = 3.5, cex.boxpoint = ps, color = "grey")> > text(2.5, 0.35, "Speed", cex = 1.4, font = 1, adj = 0.5)> text(2.5, 0.57, "Accuracy", cex = 1.4, font = 1, col = "grey", adj = 0.5)> > "> RT.hf.sp <- rnorm(1000, mean = 0.41, sd = 0.008)> RT.lf.sp <- rnorm(1000, mean = 0.43, sd = 0.01)> RT.vlf.sp <- rnorm(1000, mean = 0.425, sd = 0.012)> RT.hf.ac <- rnorm(1000, mean = 0.46, sd = 0.008)> RT.lf.ac <- rnorm(1000, mean = 0.51, sd = 0.01)> RT.vlf.ac <- rnorm(1000, mean = 0.52, sd = 0.012)> > library(sm)

> # by Henrik Singmann customized violinplot function (singmann.org) the> # original violinplot function stems from the 'vioplot' package Copyright (c)> # 2004, Daniel Adler. All rights reserved. Redistribution and use in source> # and binary forms, with or without modification, are permitted provided that> # the following conditions are met: * Redistributions of source code must> # retain the above copyright notice, this list of conditions and the> # following disclaimer. * Redistributions in binary form must reproduce the> # above copyright notice, this list of conditions and the following> # disclaimer in the documentation and/or other materials provided with the> # distribution. * Neither the name of the University of Goettingen nor the> # names of its contributors may be used to endorse or promote products> # derived from this software without specific prior written permission. THIS> # SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 'AS IS' AND> # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE> # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE> # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE> # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR> # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF> # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS> # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN> # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)> # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE> # POSSIBILITY OF SUCH DAMAGE.> > vioplot.singmann <- function(x, ..., range = 1.5, h = NULL, ylim = NULL, names = NULL, + horizontal = FALSE, col = NULL, border = "black", lty = 1, lwd = 1, rectCol = "black",

+ colMed = "white", pchMed = 19, at, add = FALSE, wex = 1, mark.outlier = TRUE, + pch.mean = 4, ids = NULL, drawRect = TRUE, yaxt = "s") {+ + # process multiple datas+ datas <- list(x, ...)+ n <- length(datas)+ if (missing(at)) + at <- 1:n+ # pass 1 - calculate base range - estimate density setup parameters for+ # density estimation+ upper <- vector(mode = "numeric", length = n)+ lower <- vector(mode = "numeric", length = n)+ q1 <- vector(mode = "numeric", length = n)+ q3 <- vector(mode = "numeric", length = n)+ med <- vector(mode = "numeric", length = n)+ base <- vector(mode = "list", length = n)+ height <- vector(mode = "list", length = n)+ outliers <- vector(mode = "list", length = n)+ baserange <- c(Inf, -Inf)+ + # global args for sm.density function-call+ args <- list(display = "none")+ + if (!(is.null(h))) + args <- c(args, h = h)+ for (i in 1:n) {+ data <- datas[[i]]+ if (!is.null(ids)) + names(data) <- ids+ if (is.null(names(data))) + names(data) <- as.character(1:(length(data)))+ + # calculate plot parameters 1- and 3-quantile, median, IQR, upper- and+ # lower-adjacent+ data.min <- min(data)+ data.max <- max(data)+ q1[i] <- quantile(data, 0.25)+ q3[i] <- quantile(data, 0.75)+ med[i] <- median(data)+ iqd <- q3[i] - q1[i]+ upper[i] <- min(q3[i] + range * iqd, data.max)+ lower[i] <- max(q1[i] - range * iqd, data.min)+ + # strategy: xmin = min(lower, data.min)) ymax = max(upper, data.max))+ est.xlim <- c(min(lower[i], data.min), max(upper[i], data.max))+ + # estimate density curve+ smout <- do.call("sm.density", c(list(data, xlim = est.xlim), args))+ + # calculate stretch factor the plots density heights is defined in range 0.0+ # ... 0.5 we scale maximum estimated point to 0.4 per data+ hscale <- 0.4/max(smout$estimate) * wex+ + # add density curve x,y pair to lists+ base[[i]] <- smout$eval.points+ height[[i]] <- smout$estimate * hscale

+ t <- range(base[[i]])+ baserange[1] <- min(baserange[1], t[1])+ baserange[2] <- max(baserange[2], t[2])+ min.d <- boxplot.stats(data)[["stats"]][1]+ max.d <- boxplot.stats(data)[["stats"]][5]+ height[[i]] <- height[[i]][(base[[i]] > min.d) & (base[[i]] < max.d)]+ height[[i]] <- c(height[[i]][1], height[[i]], height[[i]][length(height[[i]])])+ base[[i]] <- base[[i]][(base[[i]] > min.d) & (base[[i]] < max.d)]+ base[[i]] <- c(min.d, base[[i]], max.d)+ outliers[[i]] <- list(data[(data < min.d) | (data > max.d)], names(data[(data < + min.d) | (data > max.d)]))+ + # calculate min,max base ranges+ }+ # pass 2 - plot graphics setup parameters for plot+ if (!add) {+ xlim <- if (n == 1) + at + c(-0.5, 0.5) else range(at) + min(diff(at))/2 * c(-1, 1)+ + if (is.null(ylim)) {+ ylim <- baserange+ }+ }+ if (is.null(names)) {+ label <- 1:n+ } else {+ label <- names+ }+ boxwidth <- 0.05 * wex+ + # setup plot+ if (!add) + plot.new()+ if (!horizontal) {+ if (!add) {+ plot.window(xlim = xlim, ylim = ylim)+ axis(2)+ axis(1, at = at, label = label)+ }+ + box()+ for (i in 1:n) {+ # plot left/right density curve+ polygon(c(at[i] - height[[i]], rev(at[i] + height[[i]])), c(base[[i]], + rev(base[[i]])), col = col, border = border, lty = lty, lwd = lwd)+ + if (drawRect) {+ # browser() plot IQR+ boxplot(datas[[i]], at = at[i], add = TRUE, yaxt = yaxt, pars = list(boxwex = 0.6 * + wex, outpch = if (mark.outlier) "" else 1))+ if ((length(outliers[[i]][[1]]) > 0) & mark.outlier) + text(rep(at[i], length(outliers[[i]][[1]])), outliers[[i]][[1]], + labels = outliers[[i]][[2]])+ # lines( at[c( i, i)], c(lower[i], upper[i]) ,lwd=lwd, lty=lty) plot 50%

KI+ # box rect( at[i]-boxwidth/2, q1[i], at[i]+boxwidth/2, q3[i], col=rectCol)+ # plot median point points( at[i], med[i], pch=pchMed, col=colMed )+ }+ points(at[i], mean(datas[[i]]), pch = pch.mean, cex = 1.3)+ }+ } else {+ if (!add) {+ plot.window(xlim = ylim, ylim = xlim)+ axis(1)+ axis(2, at = at, label = label)+ }+ + box()+ for (i in 1:n) {+ # plot left/right density curve+ polygon(c(base[[i]], rev(base[[i]])), c(at[i] - height[[i]], rev(at[i] + + height[[i]])), col = col, border = border, lty = lty, lwd = lwd)+ + if (drawRect) {+ # plot IQR+ boxplot(datas[[i]], yaxt = yaxt, at = at[i], add = TRUE, pars = list(boxwex = 0.8 * + wex, outpch = if (mark.outlier) "" else 1))+ if ((length(outliers[[i]][[1]]) > 0) & mark.outlier) + text(rep(at[i], length(outliers[[i]][[1]])), outliers[[i]][[1]], + labels = outliers[[i]][[2]])+ # lines( at[c( i, i)], c(lower[i], upper[i]) ,lwd=lwd, lty=lty)+ }+ points(at[i], mean(datas[[i]]), pch = pch.mean, cex = 1.3)+ }+ }+ invisible(list(upper = upper, lower = lower, median = med, q1 = q1, q3 = q3))+ }> > # plot> par(cex.main = 1.5, mar = c(5, 6, 4, 5) + 0.1, mgp = c(3.5, 1, 0), cex.lab = 1.5, + font.lab = 2, cex.axis = 1.3, bty = "n", las = 1)> x <- c(1, 2, 3, 4)> plot(x, c(-10, -10, -10, -10), type = "p", ylab = " ", xlab = " ", cex = 1.5, + ylim = c(0.3, 0.6), xlim = c(1, 4), lwd = 2, pch = 5, axes = F, main = " ")> axis(1, at = c(1.5, 2.5, 3.5), labels = c("HF", "LF", "VLF"))> axis(2, pos = 1.1)> mtext("Word Frequency", side = 1, line = 3, cex = 1.5, font = 2)> > par(las = 0)> mtext("Group Mean M", side = 2, line = 2.9, cex = 1.5, font = 2)> > x <- c(1.5, 2.5, 3.5)> > vioplot.singmann(RT.hf.sp, RT.lf.sp, RT.vlf.sp, add = TRUE, mark.outlier = FALSE, + at = c(1.5, 2.5, 3.5), wex = 0.4, yaxt = "n")

> vioplot.singmann(RT.hf.ac, RT.lf.ac, RT.vlf.ac, add = TRUE, mark.outlier = FALSE, + at = c(1.5, 2.5, 3.5), wex = 0.4, col = "grey", border = "grey", rectCol = "grey", + colMed = "grey", yaxt = "n")> > text(2.5, 0.35, "Speed", cex = 1.4, font = 1, adj = 0.5)> text(2.5, 0.58, "Accuracy", cex = 1.4, font = 1, col = "grey", adj = 0.5)

Example:

plotsebargraph = function(loc, value, sterr, wiskwidth, color = "grey", linewidth = 2) {

w = wiskwidth/2

segments(x0 = loc, x1 = loc, y0 = value, y1 = value + sterr, col = color,

lwd = linewidth)

segments(x0 = loc - w, x1 = loc + w, y0 = value + sterr, y1 = value + sterr,

col = color, lwd = linewidth) # upper whiskers

}

plotsegraph = function(loc, value, sterr, wiskwidth, color = "grey", linewidth = 2) {

w = wiskwidth/2

segments(x0 = loc, x1 = loc, y0 = value - sterr, y1 = value + sterr, col = color,

lwd = linewidth)

segments(x0 = loc - w, x1 = loc + w, y0 = value + sterr, y1 = value + sterr,

col = color, lwd = linewidth) # upper whiskers

segments(x0 = loc - w, x1 = loc + w, y0 = value - sterr, y1 = value - sterr,

col = color, lwd = linewidth) # lower whiskers

}

# =======================================================

# Data; order = Speed, neutral, accuracy

MRT <- c(429, 515, 555)

MRT.se <- c(25, 25, 30)

Er <- c(0.23, 0.14, 0.13)

Er.se <- c(0.022, 0.021, 0.021)

# ======================================================

par(cex.main = 1.5, mar = c(5, 6, 4, 5) + 0.1, mgp = c(3.5, 1, 0), cex.lab = 1.5,

font.lab = 2, cex.axis = 1.3, bty = "n", las = 1)

# mpg = c(3, 1, 0) is default. first = axis labels!; middle = tick labels mar

# = c(5, 4, 4, 2) + 0.1 is default

digitsize <- 1.2

x <- c(1, 2, 3, 4)

plot(x, c(-10, -10, -10, -10), type = "p", ylab = " Mean Response Time (ms.)",

xlab = " ", cex = 1.5, ylim = c(200, 800), xlim = c(1, 4), lwd = 2, pch = 5,

axes = F, main = " ")

axis(1, at = c(1.5, 2.5, 3.5), labels = c("Speed", "Neutral", "Accuracy"))

mtext("Cue", side = 1, line = 3, cex = 1.5, font = 2)

axis(2, at = c(300, 400, 500, 600, 700))

x = c(1.5, 2.5, 3.5)

points(x, MRT, cex = 1.5, lwd = 2, pch = 19)

plot.errbars = plotsegraph(x, MRT, MRT.se, 0.1, color = "black") #0.1 = wiskwidth

lines(c(1.5, 2.5, 3.5), MRT, lwd = 2, type = "c")

text(1.5, MRT[1] + 60, "429", adj = 0.5, cex = digitsize)



par(new = TRUE)

x <- c(1, 2, 3, 4)

plot(x, c(-10, -10, -10, -10), type = "p", ylab = " ", xlab = " ", cex = 1.5,

ylim = c(0, 1), xlim = c(1, 4), lwd = 2, axes = FALSE, main = " ")

axis(4, at = c(0, 0.1, 0.2, 0.3, 0.4), las = 1)

grid::grid.text("Mean Proportion of Errors", 0.97, 0.5, rot = 270, gp = grid::gpar(cex = 1.5,

font = 2))

width <- 0.25

linewidth <- 2

x0 <- 1.5 - width

x1 <- 1.5 + width

y0 <- 0

y1 <- Er[1]

segments(x0, y0, x0, y1, lwd = linewidth)




x0 <- 2.5 - width

x1 <- 2.5 + width

y0 <- 0

y1 <- Er[2]





x0 <- 3.5 - width

x1 <- 3.5 + width

y0 <- 0

y1 <- Er[3]





loc.errbars <- c(1.5, 2.5, 3.5)

plot.errbars <- plotsebargraph(loc.errbars, Er, Er.se, 0.2, color = "black") # 0.2 = wiskwidth

text(1.5, 0.9, "Behavioral Data", font = 2, cex = 2, pos = 4)

text(1.5, 0.05, "0.23", adj = 0.5, cex = digitsize)



Example:

xbar.therapy <- 92

s.therapy <- 8.5

xbar.placebo <- 85

s.placebo <- 9.1

n <- 15

xdiff <- xbar.therapy - xbar.placebo

sdiff <- sqrt((s.therapy^2 + s.placebo^2)/2) * sqrt(2/n)

sdiff <- sqrt(s.therapy^2 + s.placebo^2)/sqrt(n)

muH0 <- 0

muH1 <- 8

t0 <- (xdiff - muH0)/sdiff

par(cex.main = 1.5, mar = c(4, 4.5, 4.5, 1), mgp = c(3.5, 1, 0), cex.lab = 1.5,


par(mar = c(4, 4.5, 4.5, 1)

x <- seq(-15, 30, by = 0.001)

y <- dt(x/sdiff, df = 28)

y3 <- dt((x - 9)/sdiff, df = 28)

plot(x, y, type = "l", axes = FALSE, xlab = NA, ylab = NA, xlim = c(-15, 25),

lwd = 2)

lines(x, y3, lwd = 2)

axis(side = 1, at = seq(-15, 30, by = 5), pos = 0, lwd = 2, cex.axis = 1.7)

axis(side = 1, at = 7, pos = 0, col = "red4", col.axis = "red4", lwd = 2, padj = 0.1)

abline(v = xdiff, col = "red4", lwd = 2)

L0 <- dt((xdiff/sdiff), df = 28)

L2 <- dt(((xdiff - 9)/sdiff), df = 28)

lines(c(6.7, 7.3), y = rep(L0, 2), col = "red4", lwd = 2)

lines(c(6.7, 7.3), y = rep(L2, 2), col = "red4", lwd = 2)

text(8, L0, expression(paste(italic("L"), " = .04")), adj = 0, col = "red4",

cex = 1.8)

text(7.5, L2, expression(paste(italic("L"), " = .32")), adj = 0, col = "red4",

cex = 1.8)

text(-16, 0.35, expression(paste(H[0], " : ", mu[diff], " = 0", sep = "")), adj = 0,

cex = 1.8)

text(-16, 0.3, expression(paste(H[1], " : ", mu[diff], " = 9", sep = "")), adj = 0,

cex = 1.8)

mtext(expression(bar(x)[diff]), side = 1, line = 2, at = 6.5, adj = 0, col = "red4",

cex = 1.8, padj = 0.1)

text(14, 0.2, expression(paste("LR = ", frac(".32", ".04") %~~% 8, sep = "")),

adj = 0, col = "red4", cex = 1.8)

Example:

Max.BF10 = function(p) {

# Computes the upper bound on the Bayes factor As in Sellke, Bayarri, &

# Berger, 2001

Max.BF10 <- -1/(exp(1) * p * log(p))

return(Max.BF10)

}

# Plot this function for p in .001 to .1

xlow <- 0.001

xhigh <- 0.1

p1 <- 0.0373

p2 <- 0.00752

p3 <- 0.001968

par(cex.main = 1.5, mar = c(5, 6, 4, 5) + 0.1, mgp = c(3.5, 1, 0), cex.lab = 1.5,


plot(function(p) Max.BF10(p), xlow, xhigh, xlim = c(xlow, xhigh), lwd = 2, xlab = " ",

ylab = " ")

mtext("Two-sided p value", 1, line = 2.5, cex = 1.5, font = 2)

mtext("Maximum Bayes factor for H1", 2, line = 2.8, cex = 1.5, font = 2, las = 0)

lines(c(0, p1), c(3, 3), lwd = 2, col = "grey")



lines(c(p1, p1), c(0, 3), lwd = 2, col = "grey")



cexsize <- 1.2

text(0.005, 31, expression(max((BF[10])) == 30 %<->% p %~~% 0.002), cex = cexsize,

pos = 4)

text(0.01, 11, expression(max((BF[10])) == 10 %<->% p %~~% 0.008), cex = cexsize,

pos = 4)

text(p1 - 0.005, 5, expression(max((BF[10])) == 3 %<->% p %~~% 0.037), cex = cexsize,

pos = 4)

Example:

# rm(list = ls())

IndividualPerformance <- function(choice, lo, show.losses = FALSE) {

# Plots the choice profile Args: choice: A vector containing the choices on

# each trial lo: A vector containing the losses on each trial show.losses:

# logical: Should the losses be indicated by filled dots?

par(mar = c(4, 4.5, 0.5, 1))

plot(choice, type = "b", axes = FALSE, xlab = "Trial", ylab = "Deck", cex.lab = 2)

axis(1, seq(0, 100, length = 6), cex.axis = 1.8)

axis(2, 1:4, labels = c("A", "B", "C", "D"), cex.axis = 1.8, las = 1)

if (show.losses == TRUE) {

index.losses <- which(lo < 0)

points(matrix(c(index.losses, choice[index.losses]), byrow = FALSE, nrow = length(index.losses)),

pch = 19, lwd = 1.5)

}

}

# Synthetic data

choice <- sample(1:4, 100, replace = TRUE)

lo <- sample(c(-1250, -250, -50, 0), 100, replace = TRUE)

# postscript('DiversePerformance.eps', width = 7, height = 7)

IndividualPerformance(choice, lo, show.losses = TRUE)

# dev.off()

Example:

library(plotrix)

# mix of 2 normal distributions

mixedNorm <- function(x) {

return(0.5 * dnorm(x, 0.25, 0.13) + 0.5 * dnorm(x, 0.7, 0.082))

}

### normalize so that area [0,1] integrates to 1; k = normalizing constant

k <- 1/integrate(mixedNorm, 0, 1)$value

# normalized

pdfmix <- function(x, k) {

return(k * (0.5 * dnorm(x, 0.25, 0.13) + 0.5 * dnorm(x, 0.7, 0.082)))

}

# integrate(pdfmix, 0.0790321,0.4048)$value # 0.4

op <- par(mfrow = c(1, 2), mar = c(5.9, 6, 4, 2) + 0.1)

barplot(height = c(0.2, 0.25, 0.1, 0.05, 0.35, 0.05), names.arg = c(1,

2, 3, 4, 5, 6), axes = FALSE, ylim = c(0, 1), width = 1, cex.names = 1.5)

arrows(x0 = 0.6, x1 = 0.6, y0 = 0.38, y1 = 0.23, length = c(0.2, 0.2),

lwd = 2)

text(0.6, 0.41, "0.2", cex = 1.3)

ablineclip(v = 1.9, y1 = 0.28, y2 = 0.375, lwd = 2)


ablineclip(h = 0.375, x1 = 1.9, x2 = 4.2, lwd = 2)


lwd = 2)

text(3.05, 0.555, "0.4", cex = 1.3)



lwd = 2)


text(6.1, 0.46, "7 x", cex = 1.3)

par(las = 1)

axis(2, at = seq(0, 1, 0.1), labels = seq(0, 1, 0.1), lwd = 2, cex.axis = 1.3)

par(las = 0)

mtext("Probability Mass", side = 2, line = 3.7, cex = 2)

mtext("Value", side = 1, line = 3.7, cex = 2)

par(mar = c(4.6, 6, 3.3, 2) + 0.1)

xx <- c(0.0790321, 0.079031, seq(0.08, 0.4, 0.01), 0.4084, 0.4084)

yy <- c(0, pdfmix(0.079031, k = k), pdfmix(seq(0.08, 0.4, 0.01), k = k), pdfmix(0.4084, k = k),

0)

plot(1, type = "n", axes = FALSE, ylab = "", xlab = "", xlim = c(0, 1),

ylim = c(0, 3))

polygon(xx, yy, col = "grey", border = NA)

curve(pdfmix(x, k = k), from = 0, to = 1, lwd = 2, ylab = "", xlab = "", xlim = c(0,

1), ylim = c(0, 3), add = TRUE)

text(0.25, 0.7, "0.4", cex = 1.3)

par(las = 1)

axis(2, at = seq(0, 3, 0.5), labels = seq(0, 3, 0.5), lwd = 2, cex.axis = 1.3)

points(0.539580297, pdfmix(0.539580297, k = k), pch = 21, bg = "white", cex = 1.4,

lwd = 2.7)

points(uniroot(function(x) pdfmix(x, k = k) - 5 * pdfmix(0.539580297, k = k), interval = c(0.56,

0.7))$root, pdfmix(uniroot(function(x) pdfmix(x, k = k) - 5 * pdfmix(0.539580297, k = k),

interval = c(0.56, 0.7))$root, k = k), pch = 21, bg = "white", cex = 1.4,

lwd = 2.7)

arrows(x0 = 0.539580297, x1 = 0.539580297, y0 = 2.7, y1 = 0.7, length = c(0.17,

0.17), angle = 19, lwd = 2)



text(0.6194593, 2.79, "5 x", cex = 1.3)

axis(1, at = seq(0, 1, 0.1), labels = c("0", ".1", ".2", ".3", ".4", ".5",

".6", ".7", ".8", ".9", "1"), line = -1.2, lwd = 2, cex.axis = 1.37)

par(las = 0)

mtext("Probability Density", side = 2, line = 3.7, cex = 2)

mtext("Value", side = 1, line = 2.4, cex = 2)

par(op)

Example:

library("psych")

library("qgraph")

# Load BFI data:

data(bfi)

bfi <- bfi[, 1:25]

# Groups and names object (not needed really, but make the plots easier to

# interpret):

Names <- scan("http://sachaepskamp.com/files/BFIitems.txt", what = "character", sep = "\n")

# Create groups object:

Groups <- rep(c("A", "C", "E", "N", "O"), each = 5)

# Compute correlations:

cor_bfi <- cor_auto(bfi)

# Plot correlation network:

graph_cor <- qgraph(cor_bfi, layout = "spring", nodeNames = Names, groups = Groups, legend.cex = 0.6,

DoNotPlot = TRUE)

# Plot partial correlation network:

graph_pcor <- qgraph(cor_bfi, graph = "concentration", layout = "spring", nodeNames = Names,

groups = Groups, legend.cex = 0.6, DoNotPlot = TRUE)

# Plot glasso network:

graph_glas <- qgraph(cor_bfi, graph = "glasso", sampleSize = nrow(bfi), layout = "spring",

nodeNames = Names, legend.cex = 0.6, groups = Groups, legend.cex = 0.7, GLratio = 2,

DoNotPlot = TRUE)

# centrality plot (all graphs):

centralityPlot(list(r = graph_cor, `Partial r` = graph_pcor, glasso = graph_glas),

labels = Names) + labs(colour = "") + theme_bw() + theme(legend.position = "bottom")

Example:

### prior & posterior parameters

mean.prior <- 75

sd.prior <- 12

mean.posterior <- 73.33644

sd.posterior <- 4.831067

### plot settings

xlim <- c(40, 115)

ylim <- c(0, 0.117)

lwd <- 2

lwd.points <- 2

lwd.axis <- 1.2

cex.points <- 1.4

cex.axis <- 1.2

cex.text <- 1.1

cex.labels <- 1.3

cexLegend <- 1.2

op <- par(mar = c(5.1, 4.1, 4.1, 2.1))

### create empty canvas

plot(1, xlim = xlim, ylim = ylim, axes = FALSE, xlab = "", ylab = "")

### shade prior area < 70

greycol1 <- rgb(0, 0, 0, alpha = 0.2)

greycol2 <- rgb(0, 0, 0, alpha = 0.4)

polPrior <- seq(xlim[1], 70, length.out = 400)

xx <- c(polPrior, polPrior[length(polPrior)], polPrior[1])

yy <- c(dnorm(polPrior, mean.prior, sd.prior), 0, 0)

polygon(xx, yy, col = greycol1, border = NA)

### shade posterior area < 70

polPosterior <- seq(xlim[1], 70, length.out = 400)

xx <- c(polPosterior, polPosterior[length(polPosterior)], polPosterior[1])

yy <- c(dnorm(polPosterior, mean.posterior, sd.posterior), 0, 0)


### shade posterior area on interval (81, 84)

polPosterior2 <- seq(81, 84, length.out = 400)

xx <- c(polPosterior2, polPosterior2[length(polPosterior2)], polPosterior2[1])

yy <- c(dnorm(polPosterior2, mean.posterior, sd.posterior), 0, 0)


### grey dashed lines to prior mean, posterior mean and posterior at 77

lines(rep(mean.prior, 2), c(0, dnorm(mean.prior, mean.prior, sd.prior)), lty = 2, col = "grey",

lwd = lwd)

lines(rep(mean.posterior, 2), c(0, dnorm(mean.posterior, mean.posterior, sd.posterior)),

lty = 2, col = "grey", lwd = lwd)

lines(rep(mean.posterior + (mean.posterior - 70), 2), c(0, dnorm(mean.posterior + (mean.posterior -

70), mean.posterior, sd.posterior)), lty = 2, col = "grey", lwd = lwd)

### axes

axis(1, at = seq(xlim[1], xlim[2], 5), cex.axis = cex.axis, lwd = lwd.axis)

axis(2, labels = FALSE, tck = 0, lwd = lwd.axis, line = -0.5)

### axes labels

mtext("IQ Bob", side = 1, cex = 1.6, line = 2.4)

mtext("Density", side = 2, cex = 1.5, line = 0)

### plot prior and posterior

# prior

plot(function(x) dnorm(x, mean.prior, sd.prior), xlim = xlim, ylim = ylim, xlab = "",

ylab = "", lwd = lwd, lty = 3, add = TRUE)

# posterior

plot(function(x) dnorm(x, mean.posterior, sd.posterior), xlim = xlim, ylim = ylim, add = TRUE,

lwd = lwd)

### add points

# posterior density at 70

points(70, dnorm(70, mean.posterior, sd.posterior), pch = 21, bg = "white", cex = cex.points,

lwd = lwd.points)

# posterior density at 76.67

points(mean.posterior + (mean.posterior - 70), dnorm(mean.posterior + (mean.posterior -

70), mean.posterior, sd.posterior), pch = 21, bg = "white", cex = cex.points, lwd = lwd.points)

# maximum a posteriori value

points(mean.posterior, dnorm(mean.posterior, mean.posterior, sd.posterior), pch = 21,

bg = "white", cex = cex.points, lwd = lwd.points)

### credible interval

CIlow <- qnorm(0.025, mean.posterior, sd.posterior)

CIhigh <- qnorm(0.975, mean.posterior, sd.posterior)

yCI <- 0.11

arrows(CIlow, yCI, CIhigh, yCI, angle = 90, code = 3, length = 0.1, lwd = lwd)

text(mean.posterior, yCI + 0.0042, labels = "95%", cex = cex.text)

text(CIlow, yCI, labels = paste(round(CIlow, 2)), cex = cex.text, pos = 2, offset = 0.3)

text(CIhigh, yCI, labels = paste(round(CIhigh, 2)), cex = cex.text, pos = 4, offset = 0.3)

### legend

legendPosition <- 115

legend(legendPosition, ylim[2] + 0.002, legend = c("Posterior", "Prior"), lty = c(1,

3), bty = "n", lwd = c(lwd, lwd), cex = cexLegend, xjust = 1, yjust = 1, x.intersp = 0.6,

seg.len = 1.2)

### draw labels

# A

arrows(x0 = 57, x1 = 61, y0 = dnorm(62, mean.prior, sd.prior) + 0.0003, y1 = dnorm(62,

mean.prior, sd.prior) - 0.007, length = c(0.08, 0.08), lwd = lwd, code = 2)

text(55.94, dnorm(5, mean.prior, sd.prior) + 0.0205, labels = "A", cex = cex.labels)

# B

arrows(x0 = 64.5, x1 = 69, y0 = dnorm(68, mean.posterior, sd.posterior) + 0.003, y1 = dnorm(68,

mean.posterior, sd.posterior) - 0.005, length = c(0.08, 0.08), lwd = lwd, code = 2)

text(63.5, dnorm(68, mean.posterior, sd.posterior) + 0.0042, labels = "B", cex = cex.labels)

# C

arrows(x0 = mean.posterior + 1, x1 = mean.posterior + 6, y0 = dnorm(mean.posterior, mean.posterior,

sd.posterior) + 0.001, y1 = dnorm(mean.posterior, mean.posterior, sd.posterior) +

0.008, length = c(0.08, 0.08), lwd = lwd, code = 1)

text(mean.posterior + 7, dnorm(mean.posterior, mean.posterior, sd.posterior) + 0.0093,

labels = "C", cex = cex.labels)

# D

arrows(x0 = 70 - 0.25, x1 = 70 - 0.25, y0 = dnorm(70, mean.posterior, sd.posterior) +

0.005, y1 = 0.092, length = c(0.08, 0.08), lwd = lwd, code = 1)

lines(c(70 - 0.25, mean.posterior), rep(0.092, 2), lwd = lwd)

arrows(x0 = mean.posterior, x1 = mean.posterior, y0 = 0.092, y1 = dnorm(mean.posterior,

mean.posterior, sd.posterior) + 0.003, length = c(0.08, 0.08), lwd = lwd, code = 2)

ratio <- dnorm(mean.posterior, mean.posterior, sd.posterior)/dnorm(70, mean.posterior,

sd.posterior)

text(mean(c(70 - 0.255, mean.posterior)), 0.096, labels = paste(round(ratio, 2), "x"),

cex = cex.text)

text(70 - 1.5, dnorm(70, mean.posterior, sd.posterior) + 0.02, labels = "D", cex = cex.labels)

# E

arrows(x0 = 70 + 1, x1 = mean.posterior + (mean.posterior - 70) - 1, y0 = dnorm(70, mean.posterior,

sd.posterior), y1 = dnorm(mean.posterior + (mean.posterior - 70), mean.posterior,

sd.posterior), length = c(0.08, 0.08), lwd = lwd, code = 3)

text(74.9, dnorm(mean.posterior + (mean.posterior - 70), mean.posterior, sd.posterior) -

0.005, labels = "E", cex = cex.labels)

# F

arrows(x0 = 82.5, x1 = 87, y0 = dnorm(82, mean.posterior, sd.posterior) - 0.012, y1 = dnorm(82,

mean.posterior, sd.posterior) - 0.005, length = c(0.08, 0.08), lwd = lwd, code = 1)

text(88, dnorm(82, mean.posterior, sd.posterior) - 0.0034, labels = "F", cex = cex.labels)

# G

arrows(x0 = CIhigh + 6, x1 = CIhigh + 8.2, y0 = yCI, y1 = yCI, length = c(0.08, 0.08),

lwd = lwd, code = 1)

text(CIhigh + 9.5, yCI, labels = "G", cex = cex.labels)

### additional information

scores <- "Bob's IQ scores: {73, 67, 79}"

priorText1 <- "Prior distribution:"

priorText2 <- expression(paste("IQ Bob ~ N(", 75, ", ", 12^2, ")"))

posteriorText1 <- "Posterior distribution:"

posteriorText2 <- expression(paste("IQ Bob ~ N(", 73.34, ", ", 4.83^2, ")"))

xx <- 87

yCI2 <- 0.12

text(xx, yCI2 - 0.033, labels = priorText1, cex = cexLegend, pos = 4, offset = 0.3)

text(xx, yCI2 - 0.042, labels = priorText2, cex = cexLegend, pos = 4, offset = 0.3)

text(xx, yCI2 - 0.059, labels = scores, cex = cexLegend, pos = 4, offset = 0.3)

text(xx, yCI2 - 0.074, labels = posteriorText1, cex = cexLegend, pos = 4, offset = 0.3)

text(xx, yCI2 - 0.083, labels = posteriorText2, cex = cexLegend, pos = 4, offset = 0.3)

par(op)

Example:> library(metafor)Zorunlu paket yükleniyor: MatrixLoading 'metafor' package (version 1.9-9). > g <- c(-0.35, -0.67, -0.25, -0.22, -0.22, -0.36, -0.67, -0.25, -0.22, -0.22, -0.36, -0.22, + -0.67, -0.25, -0.22, -0.36, -0.67, -0.25, -0.22, -0.22, -0.36)> > gSE <- c(0.469041575982343, 0.469041575982343, 0.458257569495584, 0.458257569495584, + 0.458257569495584, 0.458257569495584, 0.469041575982343, 0.458257569495584, 0.458257569495584, + 0.458257569495584, 0.458257569495584, 0.458257569495584, 0.469041575982343, 0.458257569495584, + 0.458257569495584, 0.458257569495584, 0.469041575982343, 0.458257569495584, 0.458257569495584, + 0.458257569495584, 0.458257569495584)> > cMeanSmile <- c("3.48", "3.75", "3.48", "3.67", "3.60", "3.58", "3.75", "3.48", "3.67", + "3.60", "3.58", "3.67", "3.75", "3.48", "3.60", "3.58", "3.75", "3.48", "3.67", "3.60", + "3.58")

> cMeanPout <- c("3.96", "4.81", "3.81", "3.94", "3.88", "4.03", "4.81", "3.81", "3.94", + "3.88", "4.03", "3.94", "4.81", "3.81", "3.88", "4.03", "4.81", "3.81", "3.94", "3.88", "4.03")> forest(x = g, sei = gSE, xlab = "Hedges' g", cex.lab = 1.4, ilab = cbind(cMeanSmile, + cMeanPout), ilab.xpos = c(-3.2, -2.5), cex.axis = 1.1, mlab = "Meta-Analytic Effect:",

+ lwd = 1.4, rows = 22:2, addfit = FALSE, atransf = FALSE, ylim = c(-2, 25))There were 50 or more warnings (use warnings() to see the first 50)> > text(-4.05, 24, "Study", cex = 1.3)> text(-3.2, 24, "Smile", cex = 1.3)> text(-2.5, 24, "Pout", cex = 1.3)> text(2.75, 24, "Hedges' g [95% CI]", cex = 1.3)> > abline(h = 1, lwd = 1.4)> addpoly(metaG, atransf = FALSE, row = -1, cex = 1.3, mlab = "Meta-Analytic Effect Size:")

Reference: http://shinyapps.org/apps/RGraphCompendium/index.php

Example:> library(tidyr)

> library(plotly)> s <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/school_earnings.csv")> s <- s[order(s$Men), ]> gather(s, Sex, value, Women, Men) %>%+ plot_ly(x = value, y = School, mode = "markers",+ color = Sex, colors = c("pink", "blue")) %>%+ add_trace(x = value, y = School, mode = "lines",+ group = School, showlegend = F, line = list(color = "gray")) %>%+ layout(+ title = "Gender earnings disparity",+ xaxis = list(title = "Annual Salary (in thousands)"),+ margin = list(l = 65)

+ )

Example:

> library(lattice)> library(ggplot2)> quakes$Magnitude <- equal.count(quakes$mag, 4)> pl <- cloud(depth ~ lat * long | Magnitude, data = quakes,+ zlim = rev(range(quakes$depth)), screen = list(z = 105,x = -70), panel.aspect = 0.75, xlab = "Longitude",ylab = "Latitude", zlab = "Depth")> print(pl)> pl

Example:

kx <- function(u, v) cos(u) * (r + cos(u/2) * sin(t *v) - sin(u/2) * sin(2 * t * v))

ky <- function(u, v) sin(u) * (r + cos(u/2) * sin(t *v) - sin(u/2) * sin(2 * t * v))

kz <- function(u, v) sin(u/2) * sin(t * v) + cos(u/2) *sin(t * v)

n <- 50

u <- seq(0.3, 1.25, length = n) * 2 * pi

v <- seq(0, 1, length = n) * 2 * pi

um <- matrix(u, length(u), length(u))

vm <- matrix(v, length(v), length(v), byrow = TRUE)

r <- 2

t <- 1

pl <- wireframe(kz(um, vm) ~ kx(um, vm) + ky(um, vm),shade = TRUE, screen = list(z = 170, x = -60), alpha = 0.75,panel.aspect = 0.6, aspect = c(1, 0.4))

print(pl)

advanced data visualization in r- somes examples

Data & Analytics