我正在扩展一个类，它有很多library(reshape2)
library(ggplot2)
library(scales)
library(gridExtra)
library(ggdendro)
library(zoo)
library(plyr)
#data process
mm8<-read.csv("mm8.csv",header=TRUE)
rownames(mm8)<-mm8$X
mm8<-mm8[,-2]
mm8[1:4,2:5]
#cluster from http://stackoverflow.com/questions/21474388/colorize-clusters-in-dendogram-with-ggplot2
df<-t(mm8)
df<-df[-1,]
cut <- 4 # Number of clusters
hc <- hclust(dist(df), "ave") # heirarchal clustering
dendr <- dendro_data(hc, type = "rectangle")
clust <- cutree(hc, k = cut) # find 'cut' clusters
clust.df <- data.frame(label = names(clust), cluster = clust)
# Split dendrogram into upper grey section and lower coloured section
height <- unique(dendr$segments$y)[order(unique(dendr$segments$y), decreasing = TRUE)]
cut.height <- mean(c(height[cut], height[cut-1]))
dendr$segments$line <- ifelse(dendr$segments$y == dendr$segments$yend &
dendr$segments$y > cut.height, 1, 2)
dendr$segments$line <- ifelse(dendr$segments$yend > cut.height, 1, dendr$segments$line)
# Number the clusters
dendr$segments$cluster <- c(-1, diff(dendr$segments$line))
change <- which(dendr$segments$cluster == 1)
for (i in 1:cut) dendr$segments$cluster[change[i]] = i + 1
dendr$segments$cluster <- ifelse(dendr$segments$line == 1, 1,
ifelse(dendr$segments$cluster == 0, NA, dendr$segments$cluster))
dendr$segments$cluster <- na.locf(dendr$segments$cluster)
# Consistent numbering between segment$cluster and label$cluster
clust.df$label <- factor(clust.df$label, levels = levels(dendr$labels$label))
clust.df <- arrange(clust.df, label)
clust.df$cluster <- factor((clust.df$cluster), levels = unique(clust.df$cluster), labels = (1:cut) + 1)
dendr[["labels"]] <- merge(dendr[["labels"]], clust.df, by = "label")
# Positions for cluster labels
n.rle <- rle(dendr$segments$cluster)
N <- cumsum(n.rle$lengths)
N <- N[seq(1, length(N), 2)] + 1
N.df <- dendr$segments[N, ]
N.df$cluster <- N.df$cluster - 1
# Plot the dendrogram
# Plot the dendrogram
p3<-ggplot() +
geom_segment(data = segment(dendr),
aes(x=x, y=y, xend=xend, yend=yend, size=factor(line), colour=factor(cluster)),
lineend = "square", show_guide = FALSE) +
scale_colour_manual(values = c("grey60", rainbow(cut))) +
scale_size_manual(values = c(.1, 1)) +
labs(x = NULL, y = NULL) +
theme(axis.line.y = element_blank(),
axis.ticks.y = element_blank(),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
panel.background = element_blank(),
panel.grid = element_blank()) +
guides(fill = FALSE)+
theme(axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
plot.background = element_blank())
#priparing a bar???
p4<-ggplot(clust.df,aes(x=label,y=1,fill=cluster))+geom_raster()+
theme(axis.line.y = element_blank(),
axis.ticks.y = element_blank(),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
panel.background = element_blank(),
panel.grid = element_blank()) +
guides(fill = FALSE)+
theme(axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
plot.background = element_blank())
#data for ggplot2 geom_raster
data.m = melt(mm8)
colnames(data.m)<-c("Var1", "Var2", "value")
head(data.m)
#plotting
p1 <- ggplot(data.m, aes(Var2, Var1)) + geom_raster(aes(fill = value),colour ="white")
p1<-p1 + theme(axis.ticks = element_blank(), axis.text = element_blank(),axis.title=element_blank(),plot.background = element_blank())
p2<-ggplot(data.m,aes(Var1,value*(-1)))+geom_bar(data.m, aes(fill=Var2),position="stack",stat="identity")+coord_flip()
p2<-ggplot(data.m,aes(Var1,value*(-1)))+geom_bar(data.m, aes(fill=Var2),position="stack",stat="identity")+coord_flip()+guides(fill = FALSE)+theme(axis.ticks.x = element_blank(), axis.text.x = element_blank(),axis.title.x = element_blank(),plot.background = element_blank())
#plotting 4 panels on a page
vplayout <- function(x, y) viewport(layout.pos.row = x, layout.pos.col = y)
#open graphic device
win.graph(width=860/72, height=450/72,pointsize = 12)
#plotting
grid.newpage()
pushViewport(viewport(layout = grid.layout(24, 50))) # 1 rows, 8 columns
#plotting
print(p2, vp = vplayout(5:24, 1:10))
print(p1, vp = vplayout(5:24, 10:50),newpage=FALSE)
print(p3, vp = vplayout(1:3, 9:47),newpage=FALSE)
print(p4, vp = vplayout(3:5, 10:46),newpage=FALSE)
#save
savePlot(filename="complex", type="emf")
dev.off()
方法。但扩展我的类的客户端代码通常只使用这些方法中的一个或两个。有没有一种方法可以不编译未使用的方法？
答案 0 :(得分：1)
这是不可能的。编译器没有运行时信息。
答案 1 :(得分：0)
不，编译器不会排除任何未使用的代码片段进入最终编译。无论如何，编译器无法在运行时之前推断出那种信息;它不知道这种特殊方法是否会以某种方式使用。
但是，作为开发人员，你肯定可以防止这种情况发生。
有一些工具，例如PMD，FindBugs和Sonarqube，可以对您的代码运行静态分析，以确定代码中是否存在任何未使用的方法。如果您有单元测试套件，那些工具也可以帮助向您展示未覆盖/取消代码分支（例如if语句）。像IntelliJ IDEA这样的现代IDE也可以在开发时做同样的事情。
但是对于一般情况，会有一些不必要的继承;如果你实际上只需要使用3种方法，那么选择将它们作为静态导入导入，而不是依靠继承来完成工作。这样，您的代码只使用它所需的部分，并且没有任何不需要的超重行李。