重新排列多个GGPLOT2图形和移动/缩放比例时遇到麻烦
问题描述 投票：0回答：1
我正在使用一个包含不同植物基因型，肥料施用量和5种不同测量值的数据集。我正在使用ggplot2生成多个条形图，然后使用gridExtra包将多个图组合到单个页面上。我遇到的麻烦是移动和调整比例尺，以便每个图形只有一个比例尺，我想将其移动到图形的右下角。下面的数据/代码应更好地解释我的意思。
包/数据集
#Open packages
library(dplyr)
library(ggplot2)
library(gridExtra)
#Dataset
plantdata <- data.frame(genotype = c(1,
1,
1,
1,
2,
2,
2,
2,
3,
3,
3,
3,
2,
2,
1,
3,
3,
3,
1,
3,
2,
2,
1,
1,
1,
2,
2,
1,
3,
3,
3,
3,
2,
1,
2,
1),
rate=c(1,
2,
3,
4,
1,
2,
3,
4,
1,
2,
3,
4,
2,
4,
1,
1,
3,
2,
3,
4,
1,
3,
4,
2,
2,
1,
3,
3,
4,
1,
3,
2,
4,
1,
2,
4),
measure1=c(958,
309,
750,
43,
20,
868,
905,
674,
64,
151,
677,
144,
803,
485,
707,
881,
684,
222,
399,
507,
4,
690,
831,
574,
104,
238,
378,
897,
63,
154,
582,
641,
750,
855,
194,
55),
measure2=c(359,
728,
180,
614,
241,
989,
117,
101,
95,
156,
227,
355,
597,
50,
636,
912,
149,
862,
897,
601,
176,
7,
182,
214,
453,
569,
124,
113,
969,
781,
713,
613,
800,
334,
435,
748),
measure3=c(639,
304,
891,
317,
869,
901,
723,
267,
837,
923,
171,
991,
107,
309,
733,
705,
819,
807,
346,
447,
891,
957,
359,
323,
846,
944,
400,
548,
327,
48,
677,
624,
564,
854,
658,
343),
measure4=c(805,
24,
624,
675,
261,
437,
601,
129,
733,
172,
746,
586,
142,
243,
103,
779,
612,
870,
84,
881,
850,
456,
255,
52,
228,
492,
556,
66,
670,
682,
736,
178,
568,
501,
229,
500),
measure5=c(667,
105,
565,
724,
238,
861,
299,
13,
171,
759,
755,
557,
739,
228,
870,
595,
793,
790,
572,
590,
365,
974,
550,
766,
441,
265,
245,
909,
150,
88,
473,
245,
340,
378,
998,
121))
均值标准误差的功能：
sem <- function(x) sd(x)/sqrt(length(x))
生成图形：
#Measurement 1 graph
meas1 <- select(plantdata, genotype, rate, measure1)
#Aggregating data frame
meas1_mean <- aggregate(meas1, by=list(meas1$genotype, meas1$rate), mean)
meas1_sem <- aggregate(meas1, by=list(meas1$genotype, meas1$rate), sem)
g1 <- ggplot(meas1_mean, aes(x=Group.1, y=measure1, fill=factor(Group.2)))+
geom_bar(stat="identity",width=0.6, position="dodge", col="black")+
scale_fill_discrete(name= 'rate', labels=c("1","2","3","4"))+
xlab("Genotype")+ylab("Measurement")+
geom_errorbar(aes(ymin= meas1_mean$measure1, ymax=meas1_mean$measure1+meas1_sem$measure1), width=0.2, position = position_dodge(0.6))+
ggtitle("Plant Measurement 1")+
scale_fill_brewer(palette='PRGn', name= 'rate', labels=c("1","2","3","4"))+
theme(plot.title = element_text(hjust=0.5))
####################################################################
#Measurement 2 graph
meas2 <- select(plantdata, genotype, rate, measure2)
#Aggregating dataframe
meas2_mean <- aggregate(meas2, by=list(meas2$genotype, meas2$rate), mean)
meas2_sem <- aggregate(meas2, by=list(meas2$genotype, meas2$rate), sem)
#Generating graph
g2 <- ggplot(meas2_mean, aes(x=Group.1, y=measure2, fill=factor(Group.2)))+
geom_bar(stat="identity",width=0.6, position="dodge", col="black")+
scale_fill_discrete(name= 'rate', labels=c("1","2","3","4"))+
xlab("Genotype")+ylab("Measurement")+
geom_errorbar(aes(ymin= meas2_mean$measure2, ymax=meas2_mean$measure2+meas2_sem$measure2), width=0.2, position = position_dodge(0.6))+
ggtitle("Plant Measurement 2")+
scale_fill_brewer(palette='PRGn', name= 'rate', labels=c("1","2","3","4"))+
theme(plot.title = element_text(hjust=0.5))
####################################################################
#Measurement 3 graph
meas3 <- select(plantdata, genotype, rate, measure3)
#Aggregating dataframe
meas3_mean <- aggregate(meas3, by=list(meas3$genotype, meas3$rate), mean)
meas3_sem <- aggregate(meas3, by=list(meas3$genotype, meas3$rate), sem)
#Graph
g3 <- ggplot(meas3_mean, aes(x=Group.1, y=measure3, fill=factor(Group.2)))+
geom_bar(stat="identity",width=0.6, position="dodge", col="black")+
scale_fill_discrete(name= 'rate', labels=c("1","2","3","4"))+
xlab("Genotype")+ylab("Measurement")+
geom_errorbar(aes(ymin= meas3_mean$measure3, ymax=meas3_mean$measure3+meas3_sem$measure3), width=0.2, position = position_dodge(0.6))+
ggtitle("Plant Measurement 3")+
scale_fill_brewer(palette='PRGn', name= 'rate', labels=c("1","2","3","4"))+
theme(plot.title = element_text(hjust=0.5))
##############################################################
#Measurement 4 graph
meas4 <- select(plantdata, genotype, rate, measure4)
#Aggregating dataframe
meas4_mean <- aggregate(meas4, by=list(meas4$genotype, meas4$rate), mean)
meas4_sem <- aggregate(meas4, by=list(meas4$genotype, meas4$rate), sem)
#Graph
g4 <- ggplot(meas4_mean, aes(x=Group.1, y=measure4, fill=factor(Group.2)))+
geom_bar(stat="identity",width=0.6, position="dodge", col="black")+
scale_fill_discrete(name= 'rate', labels=c("1","2","3","4"))+
xlab("Genotype")+ylab("Measurement")+
geom_errorbar(aes(ymin= meas4_mean$measure4, ymax=meas4_mean$measure4+meas4_sem$measure4), width=0.2, position = position_dodge(0.6))+
ggtitle("Plant Measurement 4")+
scale_fill_brewer(palette='PRGn', name= 'rate', labels=c("1","2","3","4"))+
theme(plot.title = element_text(hjust=0.5))
################################################################
#Measurement 5 graph
meas5 <- select(plantdata, genotype, rate, measure5)
#Aggregate dataframe
meas5_mean <- aggregate(meas5, by=list(meas5$genotype, meas5$rate), mean)
meas5_sem <- aggregate(meas5, by=list(meas5$genotype, meas5$rate), sem)
#Graph
g5 <- ggplot(meas5_mean, aes(x=Group.1, y=measure5, fill=factor(Group.2)))+
geom_bar(stat="identity",width=0.6, position="dodge", col="black")+
scale_fill_discrete(name= 'rate', labels=c("1","2","3","4"))+
xlab("Genotype")+ylab("Measurement")+
geom_errorbar(aes(ymin= meas5_mean$measure5, ymax=meas5_mean$measure5+meas5_sem$measure5), width=0.2, position = position_dodge(0.6))+
ggtitle("Plant Measurement 5")+
scale_fill_brewer(palette='PRGn', name= 'rate', labels=c("1","2","3","4"))+
theme(plot.title = element_text(hjust=0.5))
然后，我将所有图形排列在一页上，如下所示：
grid.arrange(g1, g2, g3, g4, g5,
nrow=2, ncol=3)
输出：
因为此图的要求是需要以较小的格式导出，所以我想重新设置比例，以便整个组合图像只有一个比例，并且我想增加尺寸它的。见下文：
我该怎么做？如果有比我更好的方法来生成它，那么我愿意学习如何做。
1个回答
1
投票
投票
请考虑ggpubr程序包，并在其中使用参数ggarrange来安装common.legend = TRUE函数。
a <- data.frame(x = rnorm(100), y=rnorm(100), group = gl(5,20))
b <- data.frame(x = rnorm(100), y=rnorm(100), group = gl(5,20))
c <- data.frame(x = rnorm(100), y=rnorm(100), group = gl(5,20))
library(ggplot2)
library(ggpubr)
p1 <- ggplot(a, aes(x = x, y = y, color = group)) + geom_point()
p2 <- ggplot(b, aes(x = x, y = y, color = group)) + geom_point()
p3 <- ggplot(c, aes(x = x, y = y, color = group)) + geom_point()
ggarrange(p1, p2, p3, common.legend = TRUE)
文档：https://rpkgs.datanovia.com/ggpubr/reference/ggarrange.html
最新问题
- 我无法查看我上传到mongodb的个人资料图片
- flask sqlalchemy 与抽象类的关系错误
- 目标函数作为参数的平均值
- 对强类型元组实现递归类型推理
- R 中 N 元素与 q 元素的组合
- 将单元格 G18 中的日期保存到文件名中
- Heroku 是否支持带有正斜杠的图像名称？
- 在 Cordova 中添加自定义 Gradle 依赖项
- 如何修复 ktor/intellij 中 Koin 的安装
- 执行递归函数时出现 stackoverlow 错误
- 如何编写 Flex 代码来匹配协议（http、https）、域名和可选端口号以及 URL 的路径
- 如何在 Swift 4.2 中使用 ARFaceTracking 获取面部网格中的点，如眼睛、眉毛、嘴唇、嘴巴、鼻子？
- 无法解析主要参考，因为它是针对比当前目标框架更高版本的 .NET 框架构建的
- 子模态组件中的参数未正确更新
- 带有链式规划变量的@PlanningPin
- 在 python 中根据分配给两名参赛者的数据创建比率
- Codeigniter 逻辑运算符
- Christopher Moore《计算的本质》中柯尼斯堡桥问题的路径数
- 如何使用 React 在 Github 上托管我的网站
- 获取mysql字段的总和并将总和传递到codeigniter中查看
© www.soinside.com 2019 - 2024. All rights reserved.