Implementation and evaluation of multiple dispatching algorithms.

gg = ggplot(diamonds, aes(x, depth)) + stat_density_2d(aes(fill = stat(nlevel)), geom = "polygon", n = 100,bins = 10, contour = TRUE) + facet_wrap(clarity~.) + scale_fill_viridis_c(option = "A") plot_gg(gg,multicore=TRUE,width=5, height=5,scale=250)

Demo for interactive ploting class

Note! Comment received from a teacher was as follows: EDA: the describe function is only suitable for interval variables hetcor should be done before converting variables to numeric, so it just counts the same Pearson correlations fa: you should either use cormatrix or argument cor="mixed". You cannot simply run fa on your dataframe Not a very accurate interpretation of interaction in regression And avoid using causal language such as influence, depend, etc.

par(mar = c(2, 2, 2, 2)) par(mfrow = c(1, 1)) R <- 3 r <- 2 x <- seq(0, 2*pi,length.out=50) y <- seq(0, pi,length.out=50) M <- mesh(x, y) alpha <- M$x beta <- M$y surf3D(x = (R + r*cos(alpha)) * cos(beta), y = (R + r*cos(alpha)) * sin(beta), z = r * sin(alpha), colkey=FALSE, bty="b2", main="Half of a Torus")

Vx <- volcano[-1, ] - volcano[-nrow(volcano), ] image3D(z = -60, colvar = Vx/10, add = TRUE, colkey = list(length = 0.2, width = 0.4, shift = -0.15, cex.axis = 0.8, cex.clab = 0.85), clab = c("","gradient","m/m"), plot = FALSE) contour3D(z = -60+0.01, colvar = Vx/10, add = TRUE, col = "black", plot = TRUE)

image2D(z = Oxsat$val, subset = sub, x = Oxsat$lon, y = Oxsat$lat, margin = c(1, 2), NAcol = "cyan", colkey = FALSE, xlab = "longitude", ylab = "latitude", main = paste("depth ", Oxsat$depth[sub], " m"), clim = c(0, 115), mfrow = c(2, 2)) colkey(clim = c(0, 115), clab = c("O2 saturation", "percent"))