如何在Lattice中更改点的大小？

Question

如何在Lattice中更改点的大小？

3

如何根据给定的变量比例修改“点”的大小？

例如，使用ggplot可以这样做：

ggplot(mydata,aes(x=x, y=y))+geom_point(aes(size=mysize))+geom_line(aes(group=id, color=id))

我的size变量代表了我想要的每个点的大小。但是当数据集很大时，这个过程非常缓慢。

我已经尝试使用lattice：

xyplot(y~x , type="b", col="black", col.line="blue", data=mydata, pch=16, lwd=1 )

但是我无法找到一种使用lattice的size=选项的有效方法。

我尝试了cex选项，但它产生了奇怪的结果。

这是我的数据的简化版本：（它是一个data.table，但如果您愿意，可以使用data.frame）

structure(list(id = c(3059L, 1161L, 3996L, 6330L, 6675L, 1511L, 
3678L, 294L, 596L, 2440L, 446L, 2635L, 6073L, 3709L, 744L, 4997L, 
3495L, 6447L, 6693L, 1040L, 1031L, 690L, 352L, 6311L, 2599L, 
6425L, 3758L, 690L, 6742L, 3025L, 6348L, 214L, 222L, 8192L, 615L, 
2939L, 5351L, 255L, 1531L, 6426L, 1686L, 2677L, 1919L, 3665L, 
6514L, 630L, 820L, 2138L, 6695L, 1323L, 6246L, 2102L, 2600L, 
3663L, 3851L, 970L, 1124L, 4071L, 1806L, 4579L, 3395L, 4371L, 
1466L, 201L, 2112L, 8653L, 4407L, 1959L, 6341L, 2214L, 6515L, 
1390L, 6346L, 5373L, 662L, 2198L, 1971L, 6177L, 4652L, 4420L, 
6527L, 2704L, 6366L, 1111L, 6156L, 151L, 734L, 4286L, 5085L, 
2359L, 2818L, 339L, 8486L, 5303L, 5076L, 8490L, 1230L, 1884L, 
5204L, 2880L, 8463L, 215L, 6778L, 6329L, 5797L, 584L, 4831L, 
4806L, 2581L, 3972L, 2298L, 3136L, 335L, 5538L, 1528L, 518L, 
3552L, 3874L, 1967L, 4333L, 3035L, 4112L, 215L, 1768L, 866L, 
3545L, 8085L, 8622L, 2844L, 2663L, 1356L, 4902L, 880L, 8219L, 
1486L, 3086L, 685L, 417L, 5966L, 221L, 8401L, 8378L, 1542L, 1815L, 
515L, 6262L, 3522L, 2440L, 182L, 946L, 3924L, 6219L, 2282L, 4165L, 
4969L, 5829L, 6707L, 4467L, 8645L, 8267L, 3960L, 1432L, 8203L, 
1691L, 5515L, 2005L, 5578L, 6732L, 103L, 740L, 8239L, 2037L, 
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0.120477433793361, 0.0982578397212544, 0.196195449459157, 0.282229965156794, 
0.282229965156794, 0.0728862973760933, 0.5, 0.198433420365535, 
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0.282229965156794, 0.21606648199446, 0.0699708454810496, 0.307479224376731, 
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0.282229965156794, 0.282229965156794, 0.196195449459157, 0.0481163744871317, 
0.0982578397212544, 0.0481163744871317, 0.0522648083623693, 0.196195449459157, 
0.05, 0.196195449459157, 0.198433420365535, 0.0982578397212544, 
0.198433420365535, 0.322981366459627, 0.259604625139873, 0.05, 
0.34402332361516, 0.061917195076464, 0.198433420365535, 0.0655052264808362, 
0.054016620498615, 0.0481163744871317, 0.0662020905923345, 0.198433420365535, 
0.061917195076464, 0.282229965156794, 0.259604625139873, 0.05, 
0.0481163744871317, 0.21606648199446, 0.043731778425656, 0.198433420365535, 
0.212543554006969, 0.21606648199446, 0.196195449459157, 0.062111801242236, 
0.0982578397212544, 0.196195449459157, 0.0132404181184669, 0.0982578397212544, 
0.212543554006969, 0.196195449459157, 0.196195449459157, 0.0354345393509884, 
0.259604625139873)), .Names = c("id", "cars", "numb", "mysize"
), class = c("data.table", "data.frame"), row.names = c(NA, -500L
), .internal.selfref = <pointer: 0x0000000000120788>)

以下是使用ggplot绘制的图表

ggplot(mydata,aes(x=numb, y=cars))+geom_point(aes(size=mysize))+geom_line(aes(group=id, color="blue"),  show.legend = FALSE)+theme_bw()

以及使用Lattice的绘图

xyplot(cars~numb , type="b", col="black", col.line="blue",  data=mydata, pch=16, cex= mydata$mysize*3,  lwd=1 , groups= mydata$id)

从上面的图片可以看出这些圆并不相等。我不知道哪一个是错误的。

PD2: 我已经汇总了数据，只保留了唯一的对。

poi <- unique(mydata, by=c("cars","numb"))

structure(list(id = c(3059L, 1161L, 1511L, 294L, 596L, 2440L, 
446L, 2635L, 744L, 3495L, 6447L, 1040L, 1031L, 690L, 352L, 6311L, 
3758L, 6348L, 214L, 8192L, 615L, 6426L, 1686L, 2677L, 630L, 820L, 
1806L, 201L, 662L, 4420L, 2704L, 1111L, 734L, 3136L, 335L, 1967L, 
866L, 2844L, 685L, 221L, 1542L, 6707L, 4467L, 630L, 1691L, 201L, 
1259L, 5918L, 3545L, 3029L, 1939L, 1461L, 8150L, 866L, 4804L, 
4581L, 630L, 6378L, 6438L, 675L, 6205L, 4683L, 1699L, 8304L, 
1381L, 6348L, 8197L, 2386L, 1053L, 8197L, 4104L, 5202L), cars = structure(c(11L, 
12L, 11L, 9L, 1L, 9L, 8L, 13L, 12L, 12L, 10L, 10L, 9L, 12L, 13L, 
8L, 7L, 9L, 3L, 13L, 13L, 8L, 7L, 13L, 12L, 7L, 6L, 2L, 7L, 5L, 
10L, 13L, 7L, 7L, 6L, 4L, 12L, 12L, 13L, 8L, 13L, 6L, 11L, 12L, 
4L, 11L, 5L, 3L, 6L, 10L, 1L, 2L, 4L, 12L, 5L, 5L, 13L, 2L, 1L, 
4L, 3L, 10L, 5L, 9L, 1L, 12L, 3L, 7L, 5L, 6L, 10L, 8L), .Label = c("FORD", 
"VW", "PEUGEOT", "RENAULT", "TOYOTA", "BMW", "NISSAN", "MB", 
"AUDI", "HONDA", "FIAT", "LR", "SKODA", "MAZDA", "MINI", "KIA", 
"VOLVO", "SEAT", "SUZUKI", "MITSU", "JAGUAR", "ROVER", "SAAB", 
"LEXUS", "CHEVRO", "MG", "PORSCHE"), class = "factor"), numb = c(1L, 
1L, 2L, 3L, 2L, 2L, 2L, 1L, 5L, 2L, 1L, 3L, 1L, 4L, 3L, 3L, 1L, 
5L, 1L, 4L, 6L, 1L, 2L, 2L, 12L, 9L, 2L, 6L, 4L, 1L, 2L, 7L, 
6L, 5L, 3L, 2L, 10L, 3L, 8L, 4L, 5L, 1L, 3L, 8L, 6L, 4L, 2L, 
4L, 5L, 6L, 3L, 3L, 3L, 7L, 3L, 4L, 13L, 2L, 1L, 1L, 2L, 5L, 
5L, 4L, 7L, 16L, 5L, 3L, 6L, 6L, 4L, 5L), mysize = c(0.196195449459157, 
0.259604625139873, 0.0982578397212544, 0.0761772853185596, 0.00627177700348432, 
0.0759581881533101, 0.0655052264808362, 0.198433420365535, 0.167701863354037, 
0.212543554006969, 0.120477433793361, 0.0664819944598338, 0.061917195076464, 
0.183673469387755, 0.307479224376731, 0.0512465373961219, 0.0484893696381947, 
0.142857142857143, 0.0115628496829541, 0.34402332361516, 0.375, 
0.0481163744871317, 0.0522648083623693, 0.282229965156794, 0.5, 
0.1, 0.0662020905923345, 0.0625, 0.0699708454810496, 0.00857888847444983, 
0.0682926829268293, 0.444444444444444, 0.0875, 0.0496894409937888, 
0.054016620498615, 0.0229965156794425, 0.4, 0.21606648199446, 
0.5, 0.0728862973760933, 0.322981366459627, 0.0354345393509884, 
0.0775623268698061, 0.1, 0.0125, 0.032069970845481, 0.0229965156794425, 
0.0204081632653061, 0.0683229813664596, 0.0375, 0.0138504155124654, 
0.0152354570637119, 0.0193905817174515, 0.2, 0.0318559556786704, 
0.0291545189504373, 0.5, 0.0132404181184669, 0.00484893696381947, 
0.00484893696381947, 0.0132404181184669, 0.031055900621118, 0.0248447204968944, 
0.0787172011661808, 0.0222222222222222, 1, 0.0124223602484472, 
0.0595567867036011, 0.05, 0.05, 0.043731778425656, 0.062111801242236
)), class = c("data.table", "data.frame"), row.names = c(NA, 
-72L), .Names = c("id", "cars", "numb", "mysize"))

Lattice 对于这个去重后的数据集并没有产生完全相同的结果，但至少接近我们想要的结果。

p1 <- xyplot(cars~numb , type="p", col="black",  data=poi, pch=16, cex= poi$mysize*3)
p2 <- xyplot(cars ~ numb , type = "l",  col.line = "blue",  data = mydata,   lwd = 1 , groups = mydata$id)
p1+as.layer(p2)

- skan

1

我看到了区别。我会运行代码并查看一下。 - KoenV

我注意到图中的一个点覆盖了多个“数据行”。例如，如果您键入以下内容：library(dplyr) ; nrow(mydata %>% filter(cars=="LR" & numb<2) %>% droplevels())，您将获得61行。我想知道这是否在起作用。 - KoenV

另一个相关的问题是...如何在点（alpha=1）和线（alpha=0.3）中使用不同的alpha值？ - skan

1

相同行的数量在 lattice 绘图中起到一定作用，但在 ggplot 中没有起作用。我将使用代码和图形更新我的答案。您应该考虑这是否是您想要的：多次（每个点不同）重叠一个点并且看不到它（ggplot），或者看到点的大小增加（lattice）。也许您需要一种不同的可视化方式。 - KoenV

我已经更新了带有唯一对数据集的帖子。 ggplot 仍然产生相同的结果。格子现在似乎对点很好，但对线不行。SKODA 13 应该与 LR 12 连接。 - skan

1个回答

网页内容由stack overflow 提供, 点击上面的

可以查看英文原文，
原文链接

- KoenV · Accepted Answer

您可以使用cex参数来设置lattice中点的大小。

代码可能看起来像这样，其中包含一些虚构的数据：

library(lattice)

## some data invented on the spot
mydata <- data.frame(x = 1:5,
                     y = 6:10,
                     mysize =  1:5,
                     id = c(1,1,1,2,2))

xyplot(y ~ x , type = c("b"), col = c("black"), col.line = c("blue"), 
       data = mydata, pch = 21, cex = mydata$mysize,  lwd = 1 )

这将产生以下图表：

如果您想使用分组（就像您的 ggplot 示例中一样），请添加一个 groups 参数：

xyplot(y~x , type=c("b"), col=c("black"), col.line=c("blue"), 
       data=mydata, pch=21, cex= mydata$mysize,  lwd=1 , groups= mydata$id)

请告诉我这是否是您想要的内容。

更新

我们可以看到一个点是由多个数据点叠加而成的结果：例如，汽车“LR”和编号“1”出现了61次。

library(dplyr) ; nrow(mydata %>% filter(cars=="LR" & numb<2))
# 61

让我们移除这些LR--1组合（保存一个以备后用），确保只有一个存在。存储在mydata2中。

OneRow <- head(mydata %>% filter(cars=="LR" & numb<2), 1)
mydata2 <- mydata %>% filter( !(cars=="LR" & numb<2))
mydata2 <- rbind(mydata2, OneRow)

现在使用ggplot绘制图表。

ggplot(mydata2, aes(x = numb, y = cars)) + 
  geom_point(aes(size = mysize)) +
  geom_line(aes(group=id, color="blue"),  show.legend = FALSE) + 
  theme_bw()

使用 lattice 的 xyplot() 函数

xyplot(cars ~ numb , type = "b", col = "black", col.line = "blue",  
       data = mydata2, pch = 16, 
       cex = mydata$mysize*3,  lwd = 1 , groups = mydata$id)

比较这两个格子图，可以清楚地看到，LR-1组合的重复性在点的大小方面起着作用。如果我们想要 - **并且我们需要知道我们是否想要这个** - 获得与ggplot相同的结果，我们需要有唯一的行。