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r - ggplot2 中的挑剔传奇

问题描述

我无法用我的scale_size_area()组件格式化我的图例。我希望 0% 的丰度是微小的点（由 创建scale_size_area()）而不是具有任何大小（由 my 创建scale_size_continuous()），因为它似乎具有误导性。谢谢！

p3 <- ggplot(data_melt, aes(x=index, y=variable, fill = time)) +
  geom_point(aes(size = value),shape = 21) +
  scale_size_area() +
  scale_size_continuous(name= "Percent\nAbundance", breaks = c(0,2,4,6),
                        labels = c("0%","20%","40%","60%")) +
  scale_fill_discrete(name = "Sampling\nEffort", labels = c("Summer","Winter")) +
  xlab("") +
  ylab("Taxonomy") +
  theme_linedraw() +
  scale_fill_manual(values = c("mediumseagreen","cornflowerblue")) +
  scale_color_manual(values = c("mediumseagreen","cornflowerblue")) +
  theme(legend.position = "bottom", legend.box = "horizontal", legend.direction = "horizontal",
        panel.background = element_blank()) +
  theme(axis.text.x = element_text(angle=45, hjust = 1)) +
  theme(plot.title = element_text(size = 8)) +
  theme(aspect.ratio = 1/2) 

print(p3)

再现性：

data_melt <- structure(list(system = structure(c(2L, 2L, 2L, 2L, 2L, 1L, 1L, 
1L, 1L, 1L, 1L, 2L, 6L, 6L, 3L, 4L, 4L, 4L, 4L, 5L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 6L, 6L, 3L, 4L, 4L, 4L, 4L, 
5L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 6L, 6L, 3L, 
4L, 4L, 4L, 4L, 5L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 6L, 6L, 3L, 4L, 4L, 4L, 4L, 5L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
1L, 1L, 1L, 1L, 2L, 6L, 6L, 3L, 4L, 4L, 4L, 4L, 5L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 6L, 6L, 3L, 4L, 4L, 4L, 4L, 
5L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 6L, 6L, 3L, 
4L, 4L, 4L, 4L, 5L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 6L, 6L, 3L, 4L, 4L, 4L, 4L, 5L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
1L, 1L, 1L, 1L, 2L, 6L, 6L, 3L, 4L, 4L, 4L, 4L, 5L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 6L, 6L, 3L, 4L, 4L, 4L, 4L, 
5L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 6L, 6L, 3L, 
4L, 4L, 4L, 4L, 5L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 6L, 6L, 3L, 4L, 4L, 4L, 4L, 5L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
1L, 1L, 1L, 1L, 2L, 6L, 6L, 3L, 4L, 4L, 4L, 4L, 5L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 6L, 6L, 3L, 4L, 4L, 4L, 4L, 
5L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 6L, 6L, 3L, 
4L, 4L, 4L, 4L, 5L), .Label = c("Saup2019", "Hotaling2019", "BOSS1", 
"Deployment", "BOSS2", "Retrieval"), class = "factor"), time = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("summer", 
"winter"), class = "factor"), index = structure(c(1L, 2L, 3L, 
4L, 5L, 7L, 8L, 9L, 10L, 11L, 12L, 6L, 19L, 20L, 13L, 14L, 15L, 
16L, 17L, 18L, 1L, 2L, 3L, 4L, 5L, 7L, 8L, 9L, 10L, 11L, 12L, 
6L, 19L, 20L, 13L, 14L, 15L, 16L, 17L, 18L, 1L, 2L, 3L, 4L, 5L, 
7L, 8L, 9L, 10L, 11L, 12L, 6L, 19L, 20L, 13L, 14L, 15L, 16L, 
17L, 18L, 1L, 2L, 3L, 4L, 5L, 7L, 8L, 9L, 10L, 11L, 12L, 6L, 
19L, 20L, 13L, 14L, 15L, 16L, 17L, 18L, 1L, 2L, 3L, 4L, 5L, 7L, 
8L, 9L, 10L, 11L, 12L, 6L, 19L, 20L, 13L, 14L, 15L, 16L, 17L, 
18L, 1L, 2L, 3L, 4L, 5L, 7L, 8L, 9L, 10L, 11L, 12L, 6L, 19L, 
20L, 13L, 14L, 15L, 16L, 17L, 18L, 1L, 2L, 3L, 4L, 5L, 7L, 8L, 
9L, 10L, 11L, 12L, 6L, 19L, 20L, 13L, 14L, 15L, 16L, 17L, 18L, 
1L, 2L, 3L, 4L, 5L, 7L, 8L, 9L, 10L, 11L, 12L, 6L, 19L, 20L, 
13L, 14L, 15L, 16L, 17L, 18L, 1L, 2L, 3L, 4L, 5L, 7L, 8L, 9L, 
10L, 11L, 12L, 6L, 19L, 20L, 13L, 14L, 15L, 16L, 17L, 18L, 1L, 
2L, 3L, 4L, 5L, 7L, 8L, 9L, 10L, 11L, 12L, 6L, 19L, 20L, 13L, 
14L, 15L, 16L, 17L, 18L, 1L, 2L, 3L, 4L, 5L, 7L, 8L, 9L, 10L, 
11L, 12L, 6L, 19L, 20L, 13L, 14L, 15L, 16L, 17L, 18L, 1L, 2L, 
3L, 4L, 5L, 7L, 8L, 9L, 10L, 11L, 12L, 6L, 19L, 20L, 13L, 14L, 
15L, 16L, 17L, 18L, 1L, 2L, 3L, 4L, 5L, 7L, 8L, 9L, 10L, 11L, 
12L, 6L, 19L, 20L, 13L, 14L, 15L, 16L, 17L, 18L, 1L, 2L, 3L, 
4L, 5L, 7L, 8L, 9L, 10L, 11L, 12L, 6L, 19L, 20L, 13L, 14L, 15L, 
16L, 17L, 18L, 1L, 2L, 3L, 4L, 5L, 7L, 8L, 9L, 10L, 11L, 12L, 
6L, 19L, 20L, 13L, 14L, 15L, 16L, 17L, 18L), .Label = c("CC", 
"LC1", "LC2", "NFTC1", "NFTC2", "SFTC", "Saup1", "Saup2", "Saup3", 
"Saup4", "Saup5", "Saup6", "BOSS1", "Deployment1", "Deployment2", 
"Deployment3", "Deployment4", "BOSS2", "Retrieval1", "Retrieval2"
), class = "factor"), variable = c("Gammaproteobacteria", "Gammaproteobacteria", 
"Gammaproteobacteria", "Gammaproteobacteria", "Gammaproteobacteria", 
"Gammaproteobacteria", "Gammaproteobacteria", "Gammaproteobacteria", 
"Gammaproteobacteria", "Gammaproteobacteria", "Gammaproteobacteria", 
"Gammaproteobacteria", "Gammaproteobacteria", "Gammaproteobacteria", 
"Gammaproteobacteria", "Gammaproteobacteria", "Gammaproteobacteria", 
"Gammaproteobacteria", "Gammaproteobacteria", "Gammaproteobacteria", 
"Bacteroidia", "Bacteroidia", "Bacteroidia", "Bacteroidia", "Bacteroidia", 
"Bacteroidia", "Bacteroidia", "Bacteroidia", "Bacteroidia", "Bacteroidia", 
"Bacteroidia", "Bacteroidia", "Bacteroidia", "Bacteroidia", "Bacteroidia", 
"Bacteroidia", "Bacteroidia", "Bacteroidia", "Bacteroidia", "Bacteroidia", 
"Oxyphotobacteria", "Oxyphotobacteria", "Oxyphotobacteria", "Oxyphotobacteria", 
"Oxyphotobacteria", "Oxyphotobacteria", "Oxyphotobacteria", "Oxyphotobacteria", 
"Oxyphotobacteria", "Oxyphotobacteria", "Oxyphotobacteria", "Oxyphotobacteria", 
"Oxyphotobacteria", "Oxyphotobacteria", "Oxyphotobacteria", "Oxyphotobacteria", 
"Oxyphotobacteria", "Oxyphotobacteria", "Oxyphotobacteria", "Oxyphotobacteria", 
"Alphaproteobacteria", "Alphaproteobacteria", "Alphaproteobacteria", 
"Alphaproteobacteria", "Alphaproteobacteria", "Alphaproteobacteria", 
"Alphaproteobacteria", "Alphaproteobacteria", "Alphaproteobacteria", 
"Alphaproteobacteria", "Alphaproteobacteria", "Alphaproteobacteria", 
"Alphaproteobacteria", "Alphaproteobacteria", "Alphaproteobacteria", 
"Alphaproteobacteria", "Alphaproteobacteria", "Alphaproteobacteria", 
"Alphaproteobacteria", "Alphaproteobacteria", "Deltaproteobacteria", 
"Deltaproteobacteria", "Deltaproteobacteria", "Deltaproteobacteria", 
"Deltaproteobacteria", "Deltaproteobacteria", "Deltaproteobacteria", 
"Deltaproteobacteria", "Deltaproteobacteria", "Deltaproteobacteria", 
"Deltaproteobacteria", "Deltaproteobacteria", "Deltaproteobacteria", 
"Deltaproteobacteria", "Deltaproteobacteria", "Deltaproteobacteria", 
"Deltaproteobacteria", "Deltaproteobacteria", "Deltaproteobacteria", 
"Deltaproteobacteria", "Verrucomicrobiae", "Verrucomicrobiae", 
"Verrucomicrobiae", "Verrucomicrobiae", "Verrucomicrobiae", "Verrucomicrobiae", 
"Verrucomicrobiae", "Verrucomicrobiae", "Verrucomicrobiae", "Verrucomicrobiae", 
"Verrucomicrobiae", "Verrucomicrobiae", "Verrucomicrobiae", "Verrucomicrobiae", 
"Verrucomicrobiae", "Verrucomicrobiae", "Verrucomicrobiae", "Verrucomicrobiae", 
"Verrucomicrobiae", "Verrucomicrobiae", "Actinobacteria", "Actinobacteria", 
"Actinobacteria", "Actinobacteria", "Actinobacteria", "Actinobacteria", 
"Actinobacteria", "Actinobacteria", "Actinobacteria", "Actinobacteria", 
"Actinobacteria", "Actinobacteria", "Actinobacteria", "Actinobacteria", 
"Actinobacteria", "Actinobacteria", "Actinobacteria", "Actinobacteria", 
"Actinobacteria", "Actinobacteria", "Methanomicrobia", "Methanomicrobia", 
"Methanomicrobia", "Methanomicrobia", "Methanomicrobia", "Methanomicrobia", 
"Methanomicrobia", "Methanomicrobia", "Methanomicrobia", "Methanomicrobia", 
"Methanomicrobia", "Methanomicrobia", "Methanomicrobia", "Methanomicrobia", 
"Methanomicrobia", "Methanomicrobia", "Methanomicrobia", "Methanomicrobia", 
"Methanomicrobia", "Methanomicrobia", "Parcubacteria", "Parcubacteria", 
"Parcubacteria", "Parcubacteria", "Parcubacteria", "Parcubacteria", 
"Parcubacteria", "Parcubacteria", "Parcubacteria", "Parcubacteria", 
"Parcubacteria", "Parcubacteria", "Parcubacteria", "Parcubacteria", 
"Parcubacteria", "Parcubacteria", "Parcubacteria", "Parcubacteria", 
"Parcubacteria", "Parcubacteria", "Clostridia", "Clostridia", 
"Clostridia", "Clostridia", "Clostridia", "Clostridia", "Clostridia", 
"Clostridia", "Clostridia", "Clostridia", "Clostridia", "Clostridia", 
"Clostridia", "Clostridia", "Clostridia", "Clostridia", "Clostridia", 
"Clostridia", "Clostridia", "Clostridia", "Bacilli", "Bacilli", 
"Bacilli", "Bacilli", "Bacilli", "Bacilli", "Bacilli", "Bacilli", 
"Bacilli", "Bacilli", "Bacilli", "Bacilli", "Bacilli", "Bacilli", 
"Bacilli", "Bacilli", "Bacilli", "Bacilli", "Bacilli", "Bacilli", 
"Planctomycetacia", "Planctomycetacia", "Planctomycetacia", "Planctomycetacia", 
"Planctomycetacia", "Planctomycetacia", "Planctomycetacia", "Planctomycetacia", 
"Planctomycetacia", "Planctomycetacia", "Planctomycetacia", "Planctomycetacia", 
"Planctomycetacia", "Planctomycetacia", "Planctomycetacia", "Planctomycetacia", 
"Planctomycetacia", "Planctomycetacia", "Planctomycetacia", "Planctomycetacia", 
"Woesearchaeia", "Woesearchaeia", "Woesearchaeia", "Woesearchaeia", 
"Woesearchaeia", "Woesearchaeia", "Woesearchaeia", "Woesearchaeia", 
"Woesearchaeia", "Woesearchaeia", "Woesearchaeia", "Woesearchaeia", 
"Woesearchaeia", "Woesearchaeia", "Woesearchaeia", "Woesearchaeia", 
"Woesearchaeia", "Woesearchaeia", "Woesearchaeia", "Woesearchaeia", 
"Fibrobacteria", "Fibrobacteria", "Fibrobacteria", "Fibrobacteria", 
"Fibrobacteria", "Fibrobacteria", "Fibrobacteria", "Fibrobacteria", 
"Fibrobacteria", "Fibrobacteria", "Fibrobacteria", "Fibrobacteria", 
"Fibrobacteria", "Fibrobacteria", "Fibrobacteria", "Fibrobacteria", 
"Fibrobacteria", "Fibrobacteria", "Fibrobacteria", "Fibrobacteria", 
"Gemmatimonadetes", "Gemmatimonadetes", "Gemmatimonadetes", "Gemmatimonadetes", 
"Gemmatimonadetes", "Gemmatimonadetes", "Gemmatimonadetes", "Gemmatimonadetes", 
"Gemmatimonadetes", "Gemmatimonadetes", "Gemmatimonadetes", "Gemmatimonadetes", 
"Gemmatimonadetes", "Gemmatimonadetes", "Gemmatimonadetes", "Gemmatimonadetes", 
"Gemmatimonadetes", "Gemmatimonadetes", "Gemmatimonadetes", "Gemmatimonadetes"
), value = c(0.476106048997322, 0.830003735903297, 0.478638864791961, 
0.545188599796108, 0.891044596554104, 1.28806347236381, 0.412659013341607, 
0.442926162087547, 1.89068367030337, 0.188124893146834, 0.433174821278186, 
0.50953921748656, 0.276266882800281, 0.0242517112336713, 0.226687013620217, 
0.00151968947678358, 0.188948058280091, 0.22174802282067, 0.158427627954688, 
0.515997897762891, 0.24309339904279, 0.456688622470739, 0.323532199213382, 
0.790645879732739, 0.561294602663128, 1.48130597545373, 0.769321897360565, 
0.739823721745723, 2.76169265033408, 0.126166895702033, 0.561413069231863, 
0.608325830450647, 0.370089560725963, 0.0485712931810643, 0.00770032696772971, 
0, 0.138842818556603, 0.00035539970620291, 0, 0.0111358574610245, 
0.969546030497662, 0.0591310933426545, 1.23563595053961, 1.20607040386828, 
1.36642344271129, 0.337600675783924, 1.04032857080439, 1.25005461615764, 
0.704184325890972, 0.0272352572785133, 0.462999373734726, 1.23650980906191, 
0.0276721865396659, 0.000582572348203492, 0.00436929261152619, 
0.000436929261152619, 0.0656850322599438, 0.00509750804678056, 
0.000436929261152619, 0, 0.543384391618563, 0.427480569065043, 
0.635652924867051, 0.140675423844371, 0.632016726512431, 1.02904413435753, 
0.619062769874097, 0.603836189264124, 2.12535793827553, 0.289532293986637, 
0.694286623335303, 0.639970910413163, 1.00972683059861, 0.255215672014908, 
0.0211354029362302, 0.000681787191491296, 0.307940548156902, 
0.00795418390073178, 0.00181809917731012, 0.0152265806099723, 
0.517491852840708, 0.0852016176528329, 1.17083513290667, 0.00510424437551533, 
0.195139188817778, 1.67026581334171, 0.540264635439161, 0.508068632455142, 
2.92787310063214, 0.228120460167262, 1.08406297852291, 0.434646040284267, 
0.351407593545094, 0.00117790254819585, 0.0416192233695866, 0, 
0.22969099689819, 0.000785268365463897, 0, 0.00824531783737092, 
0.669516527184057, 0.38713950267927, 1.20296224938286, 0.00541874887109399, 
0.0662291528689265, 1.22162682882774, 0.897103979770004, 0.801372749714011, 
2.29694743813595, 0.338370762839424, 0.762237341200554, 1.01270395568668, 
0.212535372388464, 0, 0.00602083207899332, 0.00240833283159733, 
0.11740622554037, 0, 0, 0, 0.585285805451728, 1.48965265748929, 
0.0774911113137022, 1.35655027805634, 0.139484000364664, 1.25809098368128, 
0.583462485185523, 0.660041936366123, 1.27358920594402, 0.412070380162275, 
0.298112863524478, 0.156805542893609, 0.803172577263196, 0.416628680827787, 
0.0127632418634333, 0.00546996079861428, 0.416628680827787, 0.033731424924788, 
0.00820494119792141, 0.0127632418634333, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0.0138257343001106, 0.000614477080004916, 0, 0.0119823030600959, 
0.128118471181025, 2.07939043873664, 0.000921715620007374, 0.00491581664003933, 
1.19454344352956, 0.856888288066855, 5.70879931178567, 0.976924139836949, 
0.0497443692137626, 0.801437059555064, 0.00276357606743126, 0.0138178803371563, 
1.63327345585187, 0.79314633135277, 0.80281884758878, 2.69310487771176, 
0.374464557136935, 0.695039380958961, 0.476716871631892, 0.266685090507116, 
0, 0, 0, 0.397954953710101, 0.00414536410114688, 0, 0.0179632444383032, 
0.136315973435862, 0.0227193289059769, 0.0926249563089829, 0.012233484795526, 
0, 1.48025166025865, 0.470115344285215, 0.290108353722475, 2.49737853897239, 
0.270884306186648, 0.223698007689619, 0.700803914715135, 3.09507165326809, 
0.0104858441104509, 0.00524292205522545, 0.0034952813701503, 
0.64837469416288, 0.0279622509612024, 0.00699056274030059, 0.00524292205522545, 
0.118838028169014, 0.0132042253521127, 0.0902288732394366, 0.0154049295774648, 
0.00880281690140845, 0.492957746478873, 1.39084507042254, 0.0770246478873239, 
0.506161971830986, 0.352112676056338, 0.129841549295775, 0.305897887323944, 
6.07174295774648, 0.0374119718309859, 0.0220070422535211, 0, 
0.209066901408451, 0.0990316901408451, 0.0110035211267606, 0.0484154929577465, 
1.12915129151292, 0.357933579335793, 1.07011070110701, 0, 0.243542435424354, 
0.664206642066421, 0.287822878228782, 0.642066420664207, 1.92250922509225, 
1.20664206642066, 0.586715867158672, 0.760147601476015, 0.512915129151291, 
0, 0.247232472324723, 0, 0.335793357933579, 0.033210332103321, 
0, 0, 0.0332565873624968, 0, 0.273727295983628, 0, 0, 2.49680225121514, 
0.470708621130724, 0.381171655154771, 4.60475825019186, 1.01560501407009, 
0.237912509593246, 0.153491941673062, 0.214888718342287, 0, 0.0153491941673062, 
0, 0.0869787669480686, 0, 0, 0.0153491941673062, 0.400183992640294, 
0, 2.34130634774609, 0, 0, 1.06255749770009, 0.94296228150874, 
0.570377184912604, 2.5114995400184, 0.216191352345906, 0.745170193192272, 
0.16099356025759, 0.915363385464581, 0, 0, 0, 0.119595216191352, 
0, 0, 0.0137994480220791, 0.938628158844765, 0.391095066185319, 
1.00481347773767, 0, 1.43200962695548, 0.673886883273165, 0.577617328519856, 
0.553549939831528, 1.44404332129964, 1.01083032490975, 1.22141997593261, 
0.649819494584837, 0.0240673886883273, 0.0180505415162455, 0.0421179302045728, 
0, 0.0180505415162455, 0, 0, 0)), row.names = c(NA, -300L), class = "data.frame")

标签： rggplot2

解决方案

您可以指定range您希望出现的尺寸scale_size_continuous。请注意，每个绘图只应调用一个scale_fill_...和函数，因为后续调用会覆盖以前的调用，从而使它们变得多余。scale_size...

ggplot(data_melt, aes(index, variable, fill = time)) +
  geom_point(aes(size = value), shape = 21) +
  scale_size_continuous(name   = "Percent\nAbundance", 
                        breaks = c(0, 2, 4, 6),
                        labels = c("0%", "20%", "40%", "60%"),
                        range  = c(0, 6)) +
  scale_fill_manual(values = c("mediumseagreen", "cornflowerblue"),
                    name   = "Sampling\nError",
                    guide  = guide_legend(override.aes = list(size = 5))) +
  labs(x = "", y = "Taxonomy") +
  theme_linedraw() +
  theme(legend.position  = "bottom", 
        legend.box       = "horizontal",
        legend.direction = "horizontal",
        panel.background = element_blank(),
        axis.text.x      = element_text(angle=45, hjust = 1),
        plot.title       = element_text(size = 8),
        aspect.ratio     = 1/2)

在此处输入图像描述

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