英文:
ggplot scatterplot for 2 categorical variables, 1 categorical variable by color
问题
我喜欢使用 ggboxplot 轻松将数据分成不同系列的能力。x轴标签保持易于阅读，同时通过相邻的彩色系列显示第二个分类变量。
p <- ggboxplot(df_dummy, x="Trt_Amend", y="Carbon_percent", color="Trt_CC",palette=c("red", "blue"),main="Great Plot Title",xlab="1st Categorical Variable",ylab="Continuous Variable") +theme(plot.title = element_text(hjust = 0.5)) + # 居中图标题.grids(linetype="dashed") +border("black")ggpar(p, x.text.angle=45,legend.title="2nd Categorical Variable",font.main=14,ylim=c(0.6, 1.6))
使用箱线图并不总是合适，特别是当每个组的观察次数较少 (< 20) 时。有人可以帮助我使用 geom_point 在 ggplot 中实现这个吗？
# 如何使用 geom_point 分离彩色系列？ggplot(df_dummy, aes(Trt_Amend, Carbon_percent, color=Trt_CC)) +geom_point()
感谢阅读！
英文:
I like the ability to easily separate data into different series using ggboxplot. The x-axis labels can remain easy to read while a 2nd categorical variable is shown via adjacent colored series.
p <- ggboxplot(df_dummy, x="Trt_Amend", y="Carbon_percent", color="Trt_CC",palette=c("red", "blue"),main="Great Plot Title",xlab="1st Categorical Variable",ylab="Continuous Variable") +theme(plot.title = element_text(hjust = 0.5)) + # Center plot title.grids(linetype="dashed") +border("black")ggpar(p, x.text.angle=45,legend.title="2nd Categorical Variable",font.main=14,ylim=c(0.6, 1.6))
Using boxplots isn't always appropriate though, like when each group has a low number of observations (< 20). Can someone help me figure out how to do this in a ggplot using geom_point?
# How to separate colored series using geom_point?ggplot(df_dummy, aes(Trt_Amend, Carbon_percent, color=Trt_CC)) +geom_point()
Thanks for reading!
答案1
得分: 1
以下是您要翻译的代码部分：
set.seed(123)df_dummy <- data.frame(Trt_Amend = paste0("Group", 1:5),Trt_CC = rep(factor(0:1), each = 5),Carbon_percent = rnorm(80, mean = 1, sd = .1))library(ggplot2)ggplot(df_dummy, aes(Trt_Amend, Carbon_percent, color = Trt_CC)) +geom_boxplot(width = .6, outlier.shape = NA) +geom_point(position = position_jitterdodge(jitter.width = .3)) +scale_color_manual(values = c("red", "blue")) +labs(title = "Great Plot Title",x = "1st Categorical Variable",y = "Continuous Variable",color = "2nd Categorical Variable") +ylim(0.6, 1.6) +theme_bw(base_size = 14) +theme(plot.title = element_text(hjust = 0.5),panel.grid = element_line(linetype = "dashed"),axis.text.x = element_text(angle = 45, hjust = 1),legend.position = "top")
EDIT You could use two stat_summary layers to add errorbars and the mean:
ggplot(df_dummy, aes(Trt_Amend, Carbon_percent, color = Trt_CC)) +geom_point(position = position_jitterdodge(jitter.width = .3)) +stat_summary(fun.data = "mean_sdl", fun.args = list(mult = 1), position = position_dodge(width = .75),geom = "errorbar",width = .3) +stat_summary(fun = "mean", position = position_dodge(width = .75),geom = "point", size = 4) +scale_color_manual(values = c("red", "blue")) +labs(title = "Great Plot Title",x = "1st Categorical Variable",y = "Continuous Variable",color = "2nd Categorical Variable") +ylim(0.6, 1.6) +theme_bw(base_size = 14) +theme(plot.title = element_text(hjust = 0.5),panel.grid = element_line(linetype = "dashed"),axis.text.x = element_text(angle = 45, hjust = 1),legend.position = "top")
请注意，我已经移除了 HTML 实体编码以及图像链接，因为这些部分不需要翻译。
英文:
The first step would be to dodge your points using position = position_dodge(.75) or to add some jitter using position_jitterdodge() as I do below. The rest of the code is - similar to ggpubr:: ggboxplot - just styling.
Using some fake random example data:
set.seed(123)df_dummy <- data.frame(Trt_Amend = paste0("Group", 1:5),Trt_CC = rep(factor(0:1), each = 5),Carbon_percent = rnorm(80, mean = 1, sd = .1))library(ggplot2)ggplot(df_dummy, aes(Trt_Amend, Carbon_percent, color = Trt_CC)) +geom_boxplot(width = .6, outlier.shape = NA) +geom_point(position = position_jitterdodge(jitter.width = .3)) +scale_color_manual(values = c("red", "blue")) +labs(title = "Great Plot Title",x = "1st Categorical Variable",y = "Continuous Variable",color = "2nd Categorical Variable") +ylim(0.6, 1.6) +theme_bw(base_size = 14) +theme(plot.title = element_text(hjust = 0.5),panel.grid = element_line(linetype = "dashed"),axis.text.x = element_text(angle = 45, hjust = 1),legend.position = "top")
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EDIT You could use two stat_summary layers to add errorbars and the mean:
ggplot(df_dummy, aes(Trt_Amend, Carbon_percent, color = Trt_CC)) +geom_point(position = position_jitterdodge(jitter.width = .3)) +stat_summary(fun.data = "mean_sdl", fun.args = list(mult = 1), position = position_dodge(width = .75),geom = "errorbar",width = .3) +stat_summary(fun = "mean", position = position_dodge(width = .75),geom = "point", size = 4) +scale_color_manual(values = c("red", "blue")) +labs(title = "Great Plot Title",x = "1st Categorical Variable",y = "Continuous Variable",color = "2nd Categorical Variable") +ylim(0.6, 1.6) +theme_bw(base_size = 14) +theme(plot.title = element_text(hjust = 0.5),panel.grid = element_line(linetype = "dashed"),axis.text.x = element_text(angle = 45, hjust = 1),legend.position = "top")
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