R使用viewPorts将数据表添加到ggplot图:缩放Grob

Question

我正在尝试将一个数据表添加到在ggplot中制作的图形中(类似于excel功能，但可以灵活地更改它的轴)。

我已经尝试过几次了，并且不断遇到缩放问题，所以尝试1)

library(grid)
library(gridExtra)
library(ggplot2)
xta=data.frame(f=rnorm(37,mean=400,sd=50))
xta$n=0
for(i in 1:37){xta$n[i]<-paste(sample(letters,4),collapse='')}
xta$c=0
for(i in 1:37){xta$c[i]<-sample((1:6),1)}
rect=data.frame(xmi=seq(0.5,36.5,1),xma=seq(1.5,37.5,1),ymi=0,yma=10)
xta=cbind(xta,rect)
a = ggplot(data=xta,aes(x=n,y=f,fill=c)) + geom_bar(stat='identity')
b = ggplot(data=xta,aes(x=n,y=5,label=round(f,1))) + geom_text(size=4) + geom_rect(aes(xmin=xmi,xmax=xma,ymin=ymi,ymax=yma),alpha=0,color='black')
z = theme(axis.text=element_blank(),panel.background=element_rect(fill='white'),axis.ticks=element_blank(),axis.title=element_blank())
b=b+z
la=grid.layout(nrow=2,ncol=1,heights=c(0.15,2),default.units=c('null','null'))
grid.show.layout(la)
grid.newpage()
pushViewport(viewport(layout=la))
print(a,vp=viewport(layout.pos.row=2,layout.pos.col=1))
print(b,vp=viewport(layout.pos.row=1,layout.pos.col=1))

产生了

​

​

第二次尝试2)是

xta1=data.frame(t(round(xta$f,1)))
xtb=tableGrob(xta1,show.rownames=F,show.colnames=F,show.vlines=T,gpar.corefill=gpar(fill='white',col='black'),gp=gpar(fontsize=12),vp=viewport(layout.pos.row=1,layout.pos.col=1))
grid.newpage()
la=grid.layout(nrow=2,ncol=1,heights=c(0.15,2),default.units=c('null','null'))
grid.show.layout(la)
grid.newpage()
pushViewport(viewport(layout=la))
print(a,vp=viewport(layout.pos.row=2,layout.pos.col=1))
grid.draw(xtb)

产生了

​

​

最后3)是

grid.newpage()
print(a + annotation_custom(grob=xtb,xmin=0,xmax=37,ymin=450,ymax=460))

产生了

​

​

如果我可以将tableGrob缩放到与绘图相同的大小，那么选项2将是最好的，但我不知道如何做到这一点。你有什么建议吗？-谢谢

Answer 1

您可以尝试新版本的tableGrob；生成的gtable宽度/高度可以设置为特定的大小(这里是均匀分布的npc单元)。

library(ggplot2)
library(gridExtra)
library(grid)
tg <- tableGrob(head(iris), rows=NULL)
tg$widths <- unit(rep(1/ncol(tg),ncol(tg)),"npc")
tg$heights <- unit(rep(1/nrow(tg),nrow(tg)),"npc")

qplot(colnames(iris), geom="bar")+ theme_bw() +
  scale_x_discrete(expand=c(0,0)) +
  scale_y_continuous(lim=c(0,2), expand=c(0,0)) +
  annotation_custom(ymin=1, ymax=2, xmin=-Inf, xmax=Inf, tg)

​

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Answer 2

例如，您可以使用ggplot创建的表，并在此blog中将它们与like结合使用。我在这里做了一个简单有效的例子：

首先制作你的图：

library(ggplot2)
library(reshape2)
library(grid)

 df <- structure(list(City = structure(c(2L,
     3L, 1L), .Label = c("Minneapolis", "Phoenix",
     "Raleigh"), class = "factor"), January = c(52.1,
     40.5, 12.2), February = c(55.1, 42.2, 16.5),
     March = c(59.7, 49.2, 28.3), April = c(67.7,
         59.5, 45.1), May = c(76.3, 67.4, 57.1),
     June = c(84.6, 74.4, 66.9), July = c(91.2,
         77.5, 71.9), August = c(89.1, 76.5,
         70.2), September = c(83.8, 70.6, 60),
     October = c(72.2, 60.2, 50), November = c(59.8,
         50, 32.4), December = c(52.5, 41.2,
         18.6)), .Names = c("City", "January",
     "February", "March", "April", "May", "June",
     "July", "August", "September", "October",
     "November", "December"), class = "data.frame",
     row.names = c(NA, -3L))

dfm <- melt(df, variable = "month")

 levels(dfm$month) <- month.abb
 p <- ggplot(dfm, aes(month, value, group = City,
     colour = City))
 p1 <- p + geom_line(size = 1) + theme(legend.position = "top") + xlab("")

接下来，在ggplot中生成数据表。使用与绘图相同的x轴：

none <- element_blank()
data_table <- ggplot(dfm, aes(x = month, y = factor(City),
     label = format(value, nsmall = 1), colour = City)) +
     geom_text(size = 3.5) +
  scale_y_discrete(labels = abbreviate)+ theme_bw()  +
     theme(panel.grid.major = none, legend.position = "none",
         panel.border = none, axis.text.x = none,
         axis.ticks = none) + theme(plot.margin = unit(c(-0.5,
     1, 0, 0.5), "lines")) + xlab(NULL) + ylab(NULL)

将两者与视口结合使用：

Layout <- grid.layout(nrow = 2, ncol = 1, heights = unit(c(2,
     0.25), c("null", "null")))
grid.show.layout(Layout)
vplayout <- function(...) {
     grid.newpage()
     pushViewport(viewport(layout = Layout))
 }

subplot <- function(x, y) viewport(layout.pos.row = x,
     layout.pos.col = y)

mmplot <- function(a, b) {
     vplayout()
     print(a, vp = subplot(1, 1))
     print(b, vp = subplot(2, 1))
 }

mmplot(p1, data_table)

请注意，仍然需要进行一些调整，如图中图例的位置和表中城市名称的缩写，但结果看起来很好：

​

适用于您的示例：

library(grid)
library(gridExtra)
library(ggplot2)
xta=data.frame(f=rnorm(37,mean=400,sd=50))
xta$n=0
for(i in 1:37){xta$n[i]<-paste(sample(letters,4),collapse='')}
xta$c=0
for(i in 1:37){xta$c[i]<-sample((1:6),1)}
rect=data.frame(xmi=seq(0.5,36.5,1),xma=seq(1.5,37.5,1),ymi=0,yma=10)
xta=cbind(xta,rect)
a = ggplot(data=xta,aes(x=n,y=f,fill=c)) + geom_bar(stat='identity')+ theme(legend.position = "top")+xlab("")

none <- element_blank()
z=ggplot(xta, aes(x = n, y = "fvalues",
     label = round(f,1)) )+
     geom_text(size = 3)+ theme_bw()  +
     theme(panel.grid.major = none, legend.position = "none",
         panel.border = none, axis.text.x = none,
         axis.ticks = none) + theme(plot.margin = unit(c(-0.5,
     1, 0, 0.5), "lines")) + xlab(NULL) + ylab(NULL)

Layout <- grid.layout(nrow = 2, ncol = 1, heights = unit(c(2,
     0.25), c("null", "null")))
grid.show.layout(Layout)
vplayout <- function(...) {
     grid.newpage()
     pushViewport(viewport(layout = Layout))
 }

subplot <- function(x, y) viewport(layout.pos.row = x,
     layout.pos.col = y)

mmplot <- function(a, b) {
     vplayout()
     print(a, vp = subplot(1, 1))
     print(b, vp = subplot(2, 1))
 }

mmplot(a, z)

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​

编辑：

类似于丹尼斯，他的解决方案，但不是一个条形图和+ coord_flip()。如果你不想翻转它，你可以删除后者，但它增加了可读性：

ggplot(xta, aes(x=n,y=f,fill=c)) +
   geom_bar() +
   labs(color = "c") +
   geom_text(aes(y = max(f)+30, label = round(f, 1)), size = 3, color = "black") + coord_flip()

Answer 3

我的意思是这不是一个设计良好的图形。首先，我不明白为什么在值从300到500的范围内需要零原点，这是一种伪装的方式，表明我不喜欢条形图的比喻。您还尝试使用条形填充来表示c的值之间的差异，其中只有6个。下面是我认为解决这个问题的更简单的方法。根据您的xta数据，

# Convert the categories to a factor
xta$N <- factor(xta$n, levels = xta$n)

# Simple approach:
ggplot(xta, aes(x = f, y = N, color = factor(c))) +
   geom_point() +
   labs(color = "c") +
   geom_text(aes(x = 575, label = round(f, 1)), size = 4, color = "black")

这对我来说不是一个有趣的图形。根据问题的背景，可能会增加一些洞察力的是，按升序对响应进行排序，并添加尺寸美学以突出c级别之间的差异。(您也可以使用不带颜色的尺寸。)最后，因为我们将因子级别放在垂直轴上，以便它们的标签清晰可见，所以我们还可以通过将水平轴稍微延伸一点来将f值作为文本插入。

ggplot(xta, aes(x = f, y = reorder(N, f), color = factor(c), size = c)) + 
   geom_point() +
   labs(color = "c") +
   geom_text(aes(x = 575, label = round(f, 1)), size = 4, color = "black")

这段代码中有足够的提示让您将其引向不同的方向。我把这事留给你了。