one R chart example

R统计分析（72页）。

图示结果：

 

过程：

> #-- Create a scatterplot with marginal histograms --
> x <- pmin(3, pmax(-3, stats::rnorm(50))) # 取（-3,3）中的随机数50个
> y <- pmin(3, pmax(-3, stats::rnorm(50)))
> xhist <- hist(x, breaks = seq(-3, 3, 0.5), plot = FALSE) # 直方图向量，具体的变量值见后面
> yhist <- hist(y, breaks = seq(-3, 3, 0.5), plot = FALSE)
> top <- max(c(xhist$counts, yhist$counts))   #此时值17
> xrange <- c(-3, 3)   #x轴取值范围
> yrange <- c(-3, 3)
> layout(matrix(c(2,0,1,3), 2, 2, byrow = TRUE), c(3,1), c(1,3), TRUE)  #把画图区域分块
> layout.show(3)
>
> par(mar=c(3,3,1,1))

#mar  A numerical vector of the form c(bottom, left, top, right) which gives the number of lines of margin to be specified on the four sides of the plot. The default is c(5, 4, 4, 2) + 0.1.

> plot(x,y, xlim=xrange, ylim=yrange, xlab="", ylab="")
>
> par(mar=c(0, 3, 1, 1))
> barplot(xhist$counts, axes = FALSE, ylim = c(0, top), space = 0 )
>
> par(mar=c(3, 0, 1, 1))
> barplot(yhist$counts, axes = FALSE, xlim = c(0, top), space = 0, horiz = TRUE )
>
> x
 [1]  0.02243276 -0.18802935  0.96635157 -0.06337091 -0.05320779 -0.25764274 -0.25904247 -0.16897359  0.99286435 -0.55518188  1.13752121  1.56176444  0.14995988 -0.60777977
[15]  0.02268866 -0.15339282  0.41942717  1.23640912  1.45951744 -0.08922257 -0.36184747  0.54985445 -0.74640503  0.31641000 -0.18868209 -0.38435261  0.59866851  0.37183951
[29]  0.15795068  0.05646891 -0.98648389 -0.90242599 -0.12530699 -0.97079945  0.14046142  0.37242721  0.40159553 -1.27605505 -0.88641508 -0.19047387 -1.46677651 -0.70993679
[43] -0.47961001  1.29435831 -0.90633420 -1.11204818 -0.25549535 -0.10933170  0.78662451 -0.29346732
> y
 [1]  0.01929088  0.83942150 -2.29704677  0.99042037  0.42855322 -0.38710509  0.04268235 -0.94027175 -0.42339996  0.63979947 -0.96425531 -1.27256363  0.11811349  1.02663524
[15] -1.42605569 -0.78948625  0.40814836 -1.06689005  0.09741875  0.64487464 -0.70083433  0.77933314 -0.63003075 -1.21861028  0.98110932  1.75623956  1.47086212  0.47224524
[29]  0.31256526  0.96391536 -0.63679697 -0.26752053  1.85726370 -1.26925445  0.19747309  0.70264365 -0.88407886 -1.62868142 -0.39982539  1.40772579  1.14357730 -1.12897447
[43]  0.62227522  1.74595794 -0.73986816  0.92358474 -0.95305925 -0.72233704 -1.43089255 -0.47765949
> xhist
$breaks
 [1] -3.0 -2.5 -2.0 -1.5 -1.0 -0.5  0.0  0.5  1.0  1.5  2.0  2.5  3.0

$counts
 [1]  0  0  0  3  9 17 11  5  4  1  0  0

$intensities
 [1] 0.00 0.00 0.00 0.12 0.36 0.68 0.44 0.20 0.16 0.04 0.00 0.00

$density
 [1] 0.00 0.00 0.00 0.12 0.36 0.68 0.44 0.20 0.16 0.04 0.00 0.00

$mids
 [1] -2.75 -2.25 -1.75 -1.25 -0.75 -0.25  0.25  0.75  1.25  1.75  2.25  2.75

$xname
[1] "x"

$equidist
[1] TRUE

attr(,"class")
[1] "histogram"
> yhist
$breaks
 [1] -3.0 -2.5 -2.0 -1.5 -1.0 -0.5  0.0  0.5  1.0  1.5  2.0  2.5  3.0

$counts
 [1]  0  1  1  7 10  5  9 10  4  3  0  0

$intensities
 [1] 0.00 0.04 0.04 0.28 0.40 0.20 0.36 0.40 0.16 0.12 0.00 0.00

$density
 [1] 0.00 0.04 0.04 0.28 0.40 0.20 0.36 0.40 0.16 0.12 0.00 0.00

$mids
 [1] -2.75 -2.25 -1.75 -1.25 -0.75 -0.25  0.25  0.75  1.25  1.75  2.25  2.75

$xname
[1] "y"

$equidist
[1] TRUE

attr(,"class")
[1] "histogram"
> xrange
[1] -3  3
> yrange
[1] -3  3