[edit]
1 蠏碁９覲 plot #
colour <- c("red", "black")
plot(tmp$seq, tmp$up, col=c(colour[tmp$choice]))
plot(tmp$seq, tmp$up, pch=tmp$choice)
[edit]
2 譴 れ 殊語姶 #
lg <- c("覓", "豕螻")
cl <- c("blue", "red")
plot(scale(x$gift), type="l", col=cl[1])
lines(scale(x$mcu), col=cl[2])
legend(1.5, 2.4,lg, lty="solid", col=cl)
out3 <- survfit(Surv(time, status==1) ~ , data=x1)
plot(out3, col=c(1:7), lty=1:7)
legend.txt <- c("","","","","覈","蠍","")
legend("topright", legend=legend.txt, col=1:7, lty=1:7)
summary(out3)
[edit]
3 mclust #
install.packages("mclust")
library("mclust")
education <- read.csv("http://datasets.flowingdata.com/education.csv", header=T)
head(education)
> head(education)
state reading math writing percent_graduates_sat pupil_staff_ratio dropout_rate
1 United States 501 515 493 46 7.9 4.4
2 Alabama 557 552 549 7 6.7 2.3
3 Alaska 520 516 492 46 7.9 7.3
4 Arizona 516 521 497 26 10.4 7.6
5 Arkansas 572 572 556 5 6.8 4.6
6 California 500 513 498 49 10.9 5.5
#2~7願讌襯 伎 企Μ 蟇磯Μ襯 蟲.
ed.dis <- dist(education[,2:7])
#cmdscale -> 蟇磯Μ ル, ル 蟇磯Μ 襷蟆 覦一.
ed.mds <- cmdscale(ed.dis)
x<- ed.mds[,1]
y<- ed.mds[,2]
plot(x,y)
plot(x,y, type='n')
text(x,y, label=education$state)
ed.mclust <- Mclust(ed.mds)
par(mfrow=c(2,2))
plot(ed.mclust, data=ed.mds)
[edit]
4 scotter plot & smooth line #
require(graphics)
with(cars, scatter.smooth(speed, dist))
## or with dotted thick smoothed line results :
with(cars, scatter.smooth(speed, dist, lpars =
list(col = "red", lwd = 3, lty = 3)))
[edit]
5 treemap #
豢豌: 觜譯殊朱殊伎 , 伎
install.packages("portfolio")
library("portfolio")
posts <- read.csv("http://datasets.flowingdata.com/post-data.txt")
head(posts)
> head(posts)
id views comments category
1 5019 148896 28 Artistic Visualization
2 1416 81374 26 Visualization
3 1416 81374 26 Featured
4 3485 80819 37 Featured
5 3485 80819 37 Mapping
6 3485 80819 37 Data Sources
with(posts, map.market(
id=id, area=views,
group=category, color=comments,
main="FlowsingData Map"))
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