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## Recently Published

##### Load Balancing - DM

Implementation and evaluation of multiple dispatching algorithms.

##### plotggPlot

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)

##### Flex Dashboard Example

Demo for interactive ploting class

##### Exploratory Factor Analsys - on the base of TIMSS2015 data

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.

##### surf3DPlot

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")

##### image3DPlot

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)

##### image2DPlot

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"))