#SCATTER PLOTS
#Simple scatterplot
#We attach pre-stored data
attach(mtcars)
mtcars
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
plot(wt, mpg, main = "Simple Scatter", xlab = "Car weight", ylab = "Miles per Gallon", pch =19)
#Add fit lines
abline(lm(mpg ~ wt),col = "red") #regression line
lines(lowess(wt,mpg),col = "blue") #lowess line
#enhanced scatter plot
#by number of cylinders
library(car)
## Loading required package: carData
scatterplot(mpg ~ wt | cyl, data = mtcars, xlab = "Weight of car",
ylab = "Miles per gallon", main = "Enhanced scatterplot",
label = row.names(mtcars) )
## Warning in plot.window(...): "label" is not a graphical parameter
## Warning in plot.xy(xy, type, ...): "label" is not a graphical parameter
## Warning in axis(side = side, at = at, labels = labels, ...): "label" is not a
## graphical parameter
## Warning in axis(side = side, at = at, labels = labels, ...): "label" is not a
## graphical parameter
## Warning in box(...): "label" is not a graphical parameter
## Warning in title(...): "label" is not a graphical parameter
#Scatter plot matrix
pairs(~mpg+disp+drat+wt, data = mtcars, main = "Scatter plot matrix")
#Scatter plot from lattice package
library(lattice)
splom(mtcars[c(1,3,5,6)], groups=cyl, data=mtcars,
panel=panel.superpose,
key=list(title="Three Cylinder Options",
columns=3,
points=list(mpg =mtcars$mpg[1:3],
wt = mtcars$wt[1:3]),
text=list(c("4 Cylinder","6 Cylinder","8 Cylinder"))))
## Warning in splom.data.frame(mtcars[c(1, 3, 5, 6)], groups = cyl, data =
## mtcars, : explicit 'data' specification ignored
#Scatter plot matrix using gclus package
library(gclus)
## Loading required package: cluster
dta <- mtcars[c(1,3,5,6)] # get data
dta.r <- abs(cor(dta)) # get correlations
dta.col <- dmat.color(dta.r) # get colors
# reorder variables so those with highest correlation
# are closest to the diagonal
dta.o <- order.single(dta.r)
cpairs(dta, dta.o, panel.colors=dta.col, gap=.5,
main="Variables Ordered and Colored by Correlation" )
#3D scatter plot
library(scatterplot3d)
attach(mtcars)
## The following objects are masked from mtcars (pos = 9):
##
## am, carb, cyl, disp, drat, gear, hp, mpg, qsec, vs, wt
scatterplot3d(wt,disp,mpg, main="3D Scatterplot")
# 3D plot with coloring and drop lines
scatterplot3d(wt,disp,mpg, pch=16, highlight.3d=TRUE,
type="h", main="3D Scatterplot")
# 3D plot with coloring and drop lines & regression plane
s3d <-scatterplot3d(wt,disp,mpg, pch=16, highlight.3d=TRUE,
type="h", main="3D Scatterplot")
fit <- lm(mpg ~ wt+disp)
s3d$plane3d(fit)
#Spinning 3D scatter plot
library(rgl)
library(Rcmdr)
## Loading required package: splines
## Loading required package: RcmdrMisc
## Loading required package: sandwich
## Loading required package: effects
## Registered S3 methods overwritten by 'lme4':
## method from
## cooks.distance.influence.merMod car
## influence.merMod car
## dfbeta.influence.merMod car
## dfbetas.influence.merMod car
## Use the command
## lattice::trellis.par.set(effectsTheme())
## to customize lattice options for effects plots.
## See ?effectsTheme for details.
## The Commander GUI is launched only in interactive sessions
##
## Attaching package: 'Rcmdr'
## The following object is masked from 'package:base':
##
## errorCondition
plot3d(wt, disp, mpg, col="red", size=3)
scatter3d(wt, disp, mpg)
## Loading required namespace: mgcv