Question

如何按比例修改给定变量的“点”大小？

例如使用ggplot我可以用

来完成

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

其中mysize是我想要的每个点的大小。但是当数据集很大时，它会很慢。

我试过格子：

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

但是我找不到一种工作方式来使用带格子的size =选项。

我尝试过cex选项，但会产生奇怪的结果。

这是我的数据的简化版本:(它是一个data.table但你可以使用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, 
630L, 8286L, 4005L, 3874L, 1691L, 201L, 4191L, 1259L, 4476L, 
5210L, 798L, 5829L, 6499L, 120L, 1725L, 2185L, 3795L, 5918L, 
3474L, 936L, 4625L, 4449L, 5579L, 5906L, 2050L, 3376L, 6140L, 
8021L, 5009L, 3545L, 346L, 8513L, 1529L, 4155L, 3029L, 5785L, 
397L, 1448L, 1804L, 6209L, 3043L, 2448L, 8371L, 5729L, 1602L, 
5600L, 1488L, 280L, 1986L, 2858L, 1742L, 1939L, 4869L, 751L, 
2967L, 6350L, 612L, 3935L, 3972L, 8321L, 8225L, 8117L, 5181L, 
2214L, 1461L, 8150L, 3218L, 3699L, 4906L, 4488L, 1317L, 866L, 
6832L, 8394L, 527L, 1384L, 3356L, 3320L, 1951L, 2979L, 3884L, 
1664L, 8446L, 1851L, 1105L, 816L, 6323L, 4490L, 1020L, 3888L, 
1086L, 4804L, 1203L, 3464L, 836L, 1591L, 755L, 6332L, 221L, 3519L, 
1565L, 1169L, 2841L, 3992L, 5296L, 6187L, 3001L, 3711L, 1394L, 
3333L, 1728L, 4320L, 6670L, 4653L, 6409L, 2369L, 8428L, 3714L, 
8383L, 2142L, 1803L, 589L, 1986L, 866L, 866L, 4558L, 8139L, 3996L, 
3667L, 2939L, 4626L, 3950L, 4371L, 6036L, 3212L, 8534L, 1599L, 
1801L, 5015L, 121L, 8258L, 3811L, 1277L, 1161L, 4854L, 4581L, 
8150L, 3223L, 2939L, 8548L, 1691L, 2969L, 1705L, 3477L, 5351L, 
1120L, 3921L, 3915L, 2599L, 3457L, 630L, 2296L, 1569L, 6378L, 
3447L, 6438L, 2697L, 2236L, 675L, 6147L, 4842L, 6205L, 4453L, 
4478L, 2394L, 2115L, 5799L, 4683L, 1699L, 4757L, 8566L, 2125L, 
3365L, 5194L, 1448L, 2043L, 2549L, 8647L, 1542L, 6179L, 1706L, 
4099L, 5996L, 5289L, 6383L, 5102L, 488L, 889L, 3323L, 8461L, 
1673L, 919L, 661L, 6505L, 1624L, 8261L, 6118L, 3935L, 2288L, 
1230L, 1221L, 8359L, 8611L, 5710L, 6064L, 5238L, 2251L, 72L, 
678L, 1649L, 8304L, 6056L, 8294L, 1967L, 5914L, 536L, 6330L, 
8548L, 1111L, 3773L, 1381L, 3522L, 8461L, 1163L, 3111L, 3035L, 
8101L, 1705L, 1690L, 2988L, 2243L, 3921L, 6348L, 4683L, 8424L, 
3117L, 2015L, 559L, 8596L, 4235L, 280L, 8197L, 6096L, 3130L, 
2880L, 1224L, 3636L, 8291L, 108L, 3213L, 2386L, 3350L, 3526L, 
8116L, 2213L, 3519L, 1387L, 3672L, 4096L, 4288L, 905L, 3348L, 
380L, 876L, 1685L, 8636L, 5559L, 1630L, 4696L, 5068L, 2351L, 
757L, 3842L, 3517L, 8270L, 2880L, 8572L, 3384L, 6056L, 3884L, 
1259L, 1053L, 6262L, 3965L, 4401L, 5295L, 4467L, 8216L, 8197L, 
3500L, 5475L, 6662L, 2117L, 1092L, 5500L, 1447L, 6283L, 5930L, 
2486L, 3258L, 4272L, 1616L, 3024L, 4104L, 3760L, 4431L, 5830L, 
6366L, 5202L, 2868L, 5097L, 8647L, 6701L, 1167L, 5521L, 1209L, 
5979L, 6178L), cars = structure(c(11L, 12L, 12L, 11L, 12L, 11L, 
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13L, 12L, 11L, 13L, 13L, 8L, 7L, 13L, 13L, 13L, 13L, 12L, 7L, 
9L, 11L, 10L, 11L, 9L, 12L, 12L, 12L, 13L, 13L, 11L, 6L, 12L, 
13L, 9L, 9L, 2L, 11L, 11L, 13L, 12L, 13L, 8L, 11L, 9L, 11L, 11L, 
7L, 12L, 10L, 7L, 13L, 5L, 12L, 10L, 7L, 13L, 13L, 12L, 7L, 9L, 
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8L, 13L, 6L, 12L, 13L, 12L, 13L, 6L, 12L, 12L, 11L, 13L, 10L, 
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7L, 8L, 11L, 1L, 11L, 12L, 7L, 12L, 13L, 11L, 7L, 13L, 12L, 9L, 
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9L, 13L, 12L, 10L, 11L, 13L, 12L, 12L, 12L, 11L, 7L, 13L, 12L, 
11L, 7L, 8L, 13L, 12L, 13L, 12L, 13L, 13L, 13L, 13L, 11L, 13L, 
13L, 6L, 5L, 9L, 13L, 10L, 8L, 13L, 8L, 10L, 12L, 10L, 11L, 11L, 
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11L, 13L, 13L, 13L, 12L, 6L, 12L, 11L, 13L, 9L, 8L, 13L, 7L, 
4L, 13L, 9L, 9L, 10L, 12L, 1L, 12L, 12L, 13L, 9L, 12L, 7L, 6L, 
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13L, 13L, 13L, 11L, 13L, 13L, 12L, 10L, 7L, 13L, 12L, 6L, 13L, 
8L, 11L, 8L, 13L, 10L, 13L, 13L, 11L, 8L, 11L, 8L, 7L, 11L, 5L, 
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11L, 11L, 2L, 11L, 12L, 11L, 11L, 6L, 12L), .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, 1L, 1L, 1L, 2L, 1L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 5L, 2L, 2L, 
1L, 2L, 3L, 1L, 4L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 1L, 5L, 1L, 1L, 
4L, 6L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 4L, 12L, 9L, 1L, 
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2L, 2L, 2L, 7L, 1L, 2L, 6L, 1L, 1L, 1L, 3L, 2L, 2L, 6L, 1L, 1L, 
2L, 2L, 3L, 2L, 2L, 1L, 2L, 2L, 3L, 3L, 4L, 1L, 2L, 2L, 1L, 5L, 
3L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 3L, 1L, 2L, 1L, 10L, 4L, 1L, 
2L, 3L, 1L, 2L, 3L, 1L, 1L, 1L, 2L, 8L, 1L, 1L, 4L, 2L, 1L, 5L, 
1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 1L, 2L, 1L, 3L, 1L, 
1L, 2L, 1L, 2L, 3L, 4L, 2L, 1L, 2L, 1L, 3L, 1L, 2L, 8L, 1L, 1L, 
3L, 6L, 4L, 1L, 2L, 1L, 1L, 3L, 1L, 2L, 1L, 1L, 1L, 1L, 4L, 1L, 
1L, 2L, 2L, 2L, 1L, 3L, 1L, 2L, 2L, 1L, 5L, 1L, 1L, 1L, 2L, 6L, 
2L, 1L, 4L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 2L, 2L, 2L, 1L, 
3L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 3L, 1L, 2L, 2L, 3L, 3L, 3L, 1L, 
4L, 1L, 2L, 2L, 7L, 2L, 1L, 1L, 3L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 
3L, 6L, 3L, 2L, 8L, 3L, 2L, 1L, 3L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 
1L, 4L, 2L, 2L, 3L, 3L, 2L, 1L, 2L, 2L, 1L, 3L, 1L, 1L, 1L, 3L, 
2L, 3L, 1L, 2L, 1L, 1L, 1L, 3L, 1L, 4L, 1L, 2L, 2L, 2L, 2L, 1L, 
2L, 3L, 4L, 2L, 1L, 1L, 1L, 2L, 1L, 4L, 1L, 3L, 2L, 1L, 4L, 5L, 
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4L, 2L, 1L, 1L, 1L, 4L, 1L, 1L, 1L, 2L, 1L, 2L, 3L, 1L, 1L, 5L, 
5L, 1L, 2L, 1L, 1L, 1L, 6L, 3L, 3L, 1L, 4L, 1L, 3L, 2L, 1L, 2L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 5L, 2L, 1L, 3L, 2L, 2L, 3L, 2L, 4L, 4L, 2L, 1L, 2L, 2L, 
2L, 1L, 2L, 1L, 7L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 4L, 2L, 
16L, 4L, 2L, 6L, 1L, 2L, 3L, 4L, 3L, 5L, 1L, 1L, 6L, 3L, 5L, 
3L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 3L, 3L, 1L, 1L, 
2L, 1L, 1L, 2L, 3L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 
2L, 1L, 6L, 1L, 1L, 2L, 1L, 5L, 1L, 6L, 4L, 1L, 1L, 2L, 3L, 1L, 
2L, 1L, 1L, 2L, 1L, 6L, 1L, 3L, 4L, 1L, 2L, 3L, 1L, 5L, 2L, 1L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L), mysize = c(0.196195449459157, 0.259604625139873, 
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0.282229965156794, 0.5, 0.307479224376731, 0.212543554006969, 
0.0481163744871317, 0.0318559556786704, 0.0481163744871317, 0.198433420365535, 
0.0354345393509884, 0.282229965156794, 0.259604625139873, 0.0655052264808362, 
0.212543554006969, 0.0354345393509884, 0.0728862973760933, 0.0662020905923345, 
0.282229965156794, 0.21606648199446, 0.0512465373961219, 0.0662020905923345, 
0.196195449459157, 0.0655052264808362, 0.0229965156794425, 0.259604625139873, 
0.0664819944598338, 0.259604625139873, 0.259604625139873, 0.196195449459157, 
0.0512465373961219, 0.212543554006969, 0.0664819944598338, 0.061917195076464, 
0.282229965156794, 0.259604625139873, 0.120477433793361, 0.196195449459157, 
0.307479224376731, 0.259604625139873, 0.183673469387755, 0.259604625139873, 
0.0982578397212544, 0.0522648083623693, 0.282229965156794, 0.212543554006969, 
0.196195449459157, 0.0522648083623693, 0.0512465373961219, 0.34402332361516, 
0.212543554006969, 0.198433420365535, 0.259604625139873, 0.198433420365535, 
0.282229965156794, 0.198433420365535, 0.34402332361516, 0.196195449459157, 
0.307479224376731, 0.282229965156794, 0.0354345393509884, 0.0291545189504373, 
0.142857142857143, 0.322981366459627, 0.0664819944598338, 0.0655052264808362, 
0.5, 0.0481163744871317, 0.0664819944598338, 0.183673469387755, 
0.120477433793361, 0.0982578397212544, 0.196195449459157, 0.282229965156794, 
0.282229965156794, 0.0728862973760933, 0.5, 0.198433420365535, 
0.183673469387755, 0.0132404181184669, 0.259604625139873, 0.00484893696381947, 
0.00857888847444983, 0.34402332361516, 0.00484893696381947, 0.259604625139873, 
0.0115628496829541, 0.0132404181184669, 0.198433420365535, 0.0655052264808362, 
0.307479224376731, 0.198433420365535, 0.120477433793361, 0.031055900621118, 
0.0248447204968944, 0.196195449459157, 0.0982578397212544, 0.259604625139873, 
0.120477433793361, 0.0481163744871317, 0.375, 0.0318559556786704, 
0.307479224376731, 0.196195449459157, 0.0699708454810496, 0.259604625139873, 
0.0761772853185596, 0.282229965156794, 0.259604625139873, 0.0229965156794425, 
0.259604625139873, 0.259604625139873, 0.0484893696381947, 0.120477433793361, 
0.0115628496829541, 0.259604625139873, 0.196195449459157, 0.0354345393509884, 
0.196195449459157, 0.212543554006969, 0.259604625139873, 0.259604625139873, 
0.196195449459157, 0.0484893696381947, 0.0481163744871317, 0.196195449459157, 
0.120477433793361, 0.196195449459157, 0.322981366459627, 0.282229965156794, 
0.198433420365535, 0.21606648199446, 0.0662020905923345, 0.212543554006969, 
0.0775623268698061, 0.282229965156794, 0.0787172011661808, 0.0728862973760933, 
0.282229965156794, 0.0484893696381947, 0.0229965156794425, 0.282229965156794, 
0.0759581881533101, 0.061917195076464, 0.0682926829268293, 0.259604625139873, 
0.0222222222222222, 0.259604625139873, 0.212543554006969, 0.282229965156794, 
0.061917195076464, 0.212543554006969, 0.0484893696381947, 0.0662020905923345, 
0.198433420365535, 0.061917195076464, 0.34402332361516, 0.0682926829268293, 
1, 0.183673469387755, 0.282229965156794, 0.375, 0.259604625139873, 
0.282229965156794, 0.21606648199446, 0.0699708454810496, 0.307479224376731, 
0.0124223602484472, 0.259604625139873, 0.259604625139873, 0.375, 
0.0152354570637119, 0.0248447204968944, 0.0761772853185596, 0.259604625139873, 
0.120477433793361, 0.0595567867036011, 0.259604625139873, 0.259604625139873, 
0.196195449459157, 0.120477433793361, 0.282229965156794, 0.198433420365535, 
0.198433420365535, 0.0982578397212544, 0.307479224376731, 0.307479224376731, 
0.259604625139873, 0.120477433793361, 0.0522648083623693, 0.198433420365535, 
0.259604625139873, 0.0662020905923345, 0.307479224376731, 0.0655052264808362, 
0.196195449459157, 0.0481163744871317, 0.282229965156794, 0.120477433793361, 
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>)

这是ggplot

的结果图

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

和莱迪思的情节

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

如您所见，圆圈不相同。我不知道哪一个是错的。

PD2：我总结了数据只保留唯一对。

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"))

莱迪思与这个独特的数据集不会产生相同的结果，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)

Answer 1

您可以使用cex参数设置lattice中点数的大小。

代码可能如下所示，带有一些发明的数据：

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 )

这产生以下图：

如果您还想使用分组（如在ggplot示例中），请添加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)

请告诉我这是否是你想要的。

更新

我们可以看到一个点是几个数据点过度绘制的结果：例如麻木“1”的汽车“LR”出现了61次。

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

让我们删除这些LR-1组合（在以后保存之后），并确保只有其中一个存在。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

绘图

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

和lattice xyplot()

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

比较两个点阵图清楚地表明，LR-1组合的多样性在点的大小中起作用。如果我们想 - **并且我们需要知道我们是否想要这个** - 得到与ggplot相同的结果，我们需要有唯一的行。

如何在格子中改变点大小？

1 个答案:

更新