是否可以根据二进制变量为 ggplot2 中折线图下的特定区域着色？
Is it possibe to color specific regions under the line graph in ggplot2 based on a binary variable?
我正在制作折线图，它应该显示某些问题随时间推移的可能性趋势。 我的目标是突出显示线下的两个区域 - (2008-2010) 和 (2015-2017) - 不突出线上方的区域。我尝试使用 geom_area() 但它没有成功，因为定义突出显示组的变量是二进制的，即 2008、2009、2010、2015、2016、2017 编码为 1 和 0 否则。
正如您在图表上看到的，我现在只能创建彩色条，但我想避免使用它们，因为它们看起来不是很直观：
下面是生成这张图的代码：
# Plot mean theta for Economy of the EU
ggplot(means, aes(x=as.numeric(Year), y=`Economy of the EU`)) +
#geom_label(aes(label=Response, fill=Response), fontface = "bold", colour = "grey15") +
geom_line(color = "grey15", size=1.1, alpha=0.6) +
theme_tufte() +
ylab("Mean Posterior Probability per Year") +
xlab("Submission Year")+
labs(title = "Model prediction for the topic 'Economy of the European Union'") +
theme_tufte() +
scale_x_continuous(breaks = seq(2000, 2017, by = 1)) +
theme(axis.text.x = element_text(
size=9, angle=45, hjust = 1, color = "grey15"),
axis.title = element_text(size = 10, color = "grey15")) +
theme(axis.text.y = element_text(size=9, color = "grey15")) +
theme(axis.line = element_line(colour = 'grey15', size = 0.5)) +
geom_rect(aes(xmin = 2008, xmax = 2010, ymin = 0, ymax = Inf),
fill = "grey15", alpha = 0.005)+
geom_rect(aes(xmin = 2015, xmax = 2017, ymin = 0, ymax = Inf),
fill = "grey15", alpha = 0.005)+
geom_text(aes(label ="Global economic crisis"), y=0.017, x = 2008.6, angle = 90, hjust = 0, size = 4)+
geom_text(aes(label = "European refugee crisis"), y=0.017, x = 2016, angle = 90, hjust = 0, size = 4)+
theme(axis.title.y= element_text(margin = margin(t = 0, r = 10, b = 0, l = 0)),
axis.title.y.right = element_text(margin = margin(t = 0, r = 0, b = 0, l = 10)
))
数据结构如下：
Year Economy of the EU X Y Z Response
1 2000 0.027733525 0.13407536 0.060126541 0.030515047 0
2 2001 0.009785368 0.09069352 0.045551195 0.036031561 0
3 2002 0.014343537 0.09829514 0.043757775 0.038343551 0
4 2003 0.028534253 0.09245992 0.100776232 0.041664380 0
5 2004 0.029453570 0.09755516 0.115880728 0.021007489 0
6 2005 0.019728747 0.08945665 0.070050617 0.080241471 0
7 2006 0.009816122 0.11857852 0.034646992 0.075113106 0
8 2007 0.015449088 0.09218845 0.048940357 0.015437598 0
9 2008 0.005629083 0.13172009 0.047490240 0.180294545 1
10 2009 0.016832789 0.11154000 0.030691807 0.090695709 1
11 2010 0.108598687 0.06898694 0.029462945 0.041829649 1
12 2011 0.082072062 0.09820219 0.150685004 0.065741991 0
13 2012 0.097722542 0.05812513 0.093679897 0.044311032 0
14 2013 0.087020362 0.07605144 0.114518255 0.062448333 0
15 2014 0.048143041 0.06783845 0.038510355 0.018508783 0
16 2015 0.028227368 0.06425895 0.056478657 0.011985935 1
17 2016 0.124701763 0.13341157 0.048756972 0.069039444 1
18 2017 0.118745565 0.09740252 0.002971395 0.006389235 1
所需的输出将是：
在此先感谢您的帮助！
尝试使用此方法 geom_bar() 来模仿设置 alpha 因子的阴影行为。您可以使用 geom_bar() 中 aes() 内的二进制变量，以便根据数据帧中的最大值分配条形图的上限。这里的代码：
#Code
ggplot(means, aes(x=as.numeric(Year), y=`Economy of the EU`)) +
#geom_label(aes(label=Response, fill=Response), fontface = "bold", colour = "grey15") +
geom_line(color = "grey15", size=1.1, alpha=0.6) +
theme_tufte() +
geom_bar(stat='identity',aes(y=ifelse(Response==1,max(means$`Economy of the EU`),NA)),
width = 1,fill='grey15',alpha=0.2)+
ylab("Mean Posterior Probability per Year") +
xlab("Submission Year")+
labs(title = "Model prediction for the topic 'Economy of the European Union'") +
theme_tufte() +
scale_x_continuous(breaks = seq(2000, 2017, by = 1)) +
theme(axis.text.x = element_text(
size=9, angle=45, hjust = 1, color = "grey15"),
axis.title = element_text(size = 10, color = "grey15")) +
theme(axis.text.y = element_text(size=9, color = "grey15")) +
theme(axis.line = element_line(colour = 'grey15', size = 0.5)) +
geom_text(aes(label ="Global economic crisis"),
y=0.017, x = 2008.6, angle = 90, hjust = 0, size = 4)+
geom_text(aes(label = "European refugee crisis"),
y=0.017, x = 2016, angle = 90, hjust = 0, size = 4)+
theme(axis.title.y= element_text(margin = margin(t = 0, r = 10, b = 0, l = 0)),
axis.title.y.right = element_text(margin = margin(t = 0, r = 0, b = 0, l = 10)))
输出：
使用了一些数据：
#Data
means <- structure(list(Year = 2000:2017, `Economy of the EU` = c(0.027733525,
0.009785368, 0.014343537, 0.028534253, 0.02945357, 0.019728747,
0.009816122, 0.015449088, 0.005629083, 0.016832789, 0.108598687,
0.082072062, 0.097722542, 0.087020362, 0.048143041, 0.028227368,
0.124701763, 0.118745565), X = c(0.13407536, 0.09069352, 0.09829514,
0.09245992, 0.09755516, 0.08945665, 0.11857852, 0.09218845, 0.13172009,
0.11154, 0.06898694, 0.09820219, 0.05812513, 0.07605144, 0.06783845,
0.06425895, 0.13341157, 0.09740252), Y = c(0.060126541, 0.045551195,
0.043757775, 0.100776232, 0.115880728, 0.070050617, 0.034646992,
0.048940357, 0.04749024, 0.030691807, 0.029462945, 0.150685004,
0.093679897, 0.114518255, 0.038510355, 0.056478657, 0.048756972,
0.002971395), Z = c(0.030515047, 0.036031561, 0.038343551, 0.04166438,
0.021007489, 0.080241471, 0.075113106, 0.015437598, 0.180294545,
0.090695709, 0.041829649, 0.065741991, 0.044311032, 0.062448333,
0.018508783, 0.011985935, 0.069039444, 0.006389235), Response = c(0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L,
1L)), row.names = c(NA, -18L), class = "data.frame")
如果我没理解错的话，您只需要在线下阴影的突出显示区域。在这种情况下，您正在寻找 geom_area 但您需要绘制两个单独的 geom_area 区域，这些区域通过对数据进行子集化来定义：
library(ggplot2)
library(ggthemes)
library(dplyr)
ggplot(means, aes(x=as.numeric(Year), y=`Economy of the EU`)) +
geom_line(color = "grey15", size = 1.1, alpha = 0.6) +
geom_area(data = means %>% filter(Year > 2007 & Year < 2012), alpha = 0.1) +
geom_area(data = means %>% filter(Year > 2014), alpha = 0.1) +
geom_text(aes(label ="Global economic crisis"), y = 0.017, x = 2008.6,
angle = 90, hjust = 0, size = 4, check_overlap = TRUE) +
geom_text(aes(label = "European refugee crisis"), y = 0.017, x = 2016,
angle = 90, hjust = 0, size = 4, check_overlap = TRUE) +
scale_x_continuous(breaks = seq(2000, 2017, by = 1)) +
labs(x = "Submission Year",
y = "Mean Posterior Probability per Year",
title = "Model prediction for the topic 'Economy of the European Union'") +
theme_tufte() +
theme(axis.text.x = element_text(size=9, angle = 45, hjust = 1,
color = "grey15"),
axis.title = element_text(size = 10, color = "grey15"),
axis.text.y = element_text(size = 9, color = "grey15"),
axis.line = element_line(colour = 'grey15', size = 0.5),
axis.title.y = element_text(margin =
margin(t = 0, r = 10, b = 0, l = 0)),
axis.title.y.right = element_text(margin =
margin(t = 0, r = 0, b = 0, l = 10)))
我正在制作折线图，它应该显示某些问题随时间推移的可能性趋势。 我的目标是突出显示线下的两个区域 - (2008-2010) 和 (2015-2017) - 不突出线上方的区域。我尝试使用 geom_area() 但它没有成功，因为定义突出显示组的变量是二进制的，即 2008、2009、2010、2015、2016、2017 编码为 1 和 0 否则。
正如您在图表上看到的，我现在只能创建彩色条，但我想避免使用它们，因为它们看起来不是很直观：
下面是生成这张图的代码：
# Plot mean theta for Economy of the EU
ggplot(means, aes(x=as.numeric(Year), y=`Economy of the EU`)) +
#geom_label(aes(label=Response, fill=Response), fontface = "bold", colour = "grey15") +
geom_line(color = "grey15", size=1.1, alpha=0.6) +
theme_tufte() +
ylab("Mean Posterior Probability per Year") +
xlab("Submission Year")+
labs(title = "Model prediction for the topic 'Economy of the European Union'") +
theme_tufte() +
scale_x_continuous(breaks = seq(2000, 2017, by = 1)) +
theme(axis.text.x = element_text(
size=9, angle=45, hjust = 1, color = "grey15"),
axis.title = element_text(size = 10, color = "grey15")) +
theme(axis.text.y = element_text(size=9, color = "grey15")) +
theme(axis.line = element_line(colour = 'grey15', size = 0.5)) +
geom_rect(aes(xmin = 2008, xmax = 2010, ymin = 0, ymax = Inf),
fill = "grey15", alpha = 0.005)+
geom_rect(aes(xmin = 2015, xmax = 2017, ymin = 0, ymax = Inf),
fill = "grey15", alpha = 0.005)+
geom_text(aes(label ="Global economic crisis"), y=0.017, x = 2008.6, angle = 90, hjust = 0, size = 4)+
geom_text(aes(label = "European refugee crisis"), y=0.017, x = 2016, angle = 90, hjust = 0, size = 4)+
theme(axis.title.y= element_text(margin = margin(t = 0, r = 10, b = 0, l = 0)),
axis.title.y.right = element_text(margin = margin(t = 0, r = 0, b = 0, l = 10)
))
数据结构如下：
Year Economy of the EU X Y Z Response
1 2000 0.027733525 0.13407536 0.060126541 0.030515047 0
2 2001 0.009785368 0.09069352 0.045551195 0.036031561 0
3 2002 0.014343537 0.09829514 0.043757775 0.038343551 0
4 2003 0.028534253 0.09245992 0.100776232 0.041664380 0
5 2004 0.029453570 0.09755516 0.115880728 0.021007489 0
6 2005 0.019728747 0.08945665 0.070050617 0.080241471 0
7 2006 0.009816122 0.11857852 0.034646992 0.075113106 0
8 2007 0.015449088 0.09218845 0.048940357 0.015437598 0
9 2008 0.005629083 0.13172009 0.047490240 0.180294545 1
10 2009 0.016832789 0.11154000 0.030691807 0.090695709 1
11 2010 0.108598687 0.06898694 0.029462945 0.041829649 1
12 2011 0.082072062 0.09820219 0.150685004 0.065741991 0
13 2012 0.097722542 0.05812513 0.093679897 0.044311032 0
14 2013 0.087020362 0.07605144 0.114518255 0.062448333 0
15 2014 0.048143041 0.06783845 0.038510355 0.018508783 0
16 2015 0.028227368 0.06425895 0.056478657 0.011985935 1
17 2016 0.124701763 0.13341157 0.048756972 0.069039444 1
18 2017 0.118745565 0.09740252 0.002971395 0.006389235 1
所需的输出将是：
在此先感谢您的帮助！
尝试使用此方法 geom_bar() 来模仿设置 alpha 因子的阴影行为。您可以使用 geom_bar() 中 aes() 内的二进制变量，以便根据数据帧中的最大值分配条形图的上限。这里的代码：
#Code
ggplot(means, aes(x=as.numeric(Year), y=`Economy of the EU`)) +
#geom_label(aes(label=Response, fill=Response), fontface = "bold", colour = "grey15") +
geom_line(color = "grey15", size=1.1, alpha=0.6) +
theme_tufte() +
geom_bar(stat='identity',aes(y=ifelse(Response==1,max(means$`Economy of the EU`),NA)),
width = 1,fill='grey15',alpha=0.2)+
ylab("Mean Posterior Probability per Year") +
xlab("Submission Year")+
labs(title = "Model prediction for the topic 'Economy of the European Union'") +
theme_tufte() +
scale_x_continuous(breaks = seq(2000, 2017, by = 1)) +
theme(axis.text.x = element_text(
size=9, angle=45, hjust = 1, color = "grey15"),
axis.title = element_text(size = 10, color = "grey15")) +
theme(axis.text.y = element_text(size=9, color = "grey15")) +
theme(axis.line = element_line(colour = 'grey15', size = 0.5)) +
geom_text(aes(label ="Global economic crisis"),
y=0.017, x = 2008.6, angle = 90, hjust = 0, size = 4)+
geom_text(aes(label = "European refugee crisis"),
y=0.017, x = 2016, angle = 90, hjust = 0, size = 4)+
theme(axis.title.y= element_text(margin = margin(t = 0, r = 10, b = 0, l = 0)),
axis.title.y.right = element_text(margin = margin(t = 0, r = 0, b = 0, l = 10)))
输出：
使用了一些数据：
#Data
means <- structure(list(Year = 2000:2017, `Economy of the EU` = c(0.027733525,
0.009785368, 0.014343537, 0.028534253, 0.02945357, 0.019728747,
0.009816122, 0.015449088, 0.005629083, 0.016832789, 0.108598687,
0.082072062, 0.097722542, 0.087020362, 0.048143041, 0.028227368,
0.124701763, 0.118745565), X = c(0.13407536, 0.09069352, 0.09829514,
0.09245992, 0.09755516, 0.08945665, 0.11857852, 0.09218845, 0.13172009,
0.11154, 0.06898694, 0.09820219, 0.05812513, 0.07605144, 0.06783845,
0.06425895, 0.13341157, 0.09740252), Y = c(0.060126541, 0.045551195,
0.043757775, 0.100776232, 0.115880728, 0.070050617, 0.034646992,
0.048940357, 0.04749024, 0.030691807, 0.029462945, 0.150685004,
0.093679897, 0.114518255, 0.038510355, 0.056478657, 0.048756972,
0.002971395), Z = c(0.030515047, 0.036031561, 0.038343551, 0.04166438,
0.021007489, 0.080241471, 0.075113106, 0.015437598, 0.180294545,
0.090695709, 0.041829649, 0.065741991, 0.044311032, 0.062448333,
0.018508783, 0.011985935, 0.069039444, 0.006389235), Response = c(0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L,
1L)), row.names = c(NA, -18L), class = "data.frame")
如果我没理解错的话，您只需要在线下阴影的突出显示区域。在这种情况下，您正在寻找 geom_area 但您需要绘制两个单独的 geom_area 区域，这些区域通过对数据进行子集化来定义：
library(ggplot2)
library(ggthemes)
library(dplyr)
ggplot(means, aes(x=as.numeric(Year), y=`Economy of the EU`)) +
geom_line(color = "grey15", size = 1.1, alpha = 0.6) +
geom_area(data = means %>% filter(Year > 2007 & Year < 2012), alpha = 0.1) +
geom_area(data = means %>% filter(Year > 2014), alpha = 0.1) +
geom_text(aes(label ="Global economic crisis"), y = 0.017, x = 2008.6,
angle = 90, hjust = 0, size = 4, check_overlap = TRUE) +
geom_text(aes(label = "European refugee crisis"), y = 0.017, x = 2016,
angle = 90, hjust = 0, size = 4, check_overlap = TRUE) +
scale_x_continuous(breaks = seq(2000, 2017, by = 1)) +
labs(x = "Submission Year",
y = "Mean Posterior Probability per Year",
title = "Model prediction for the topic 'Economy of the European Union'") +
theme_tufte() +
theme(axis.text.x = element_text(size=9, angle = 45, hjust = 1,
color = "grey15"),
axis.title = element_text(size = 10, color = "grey15"),
axis.text.y = element_text(size = 9, color = "grey15"),
axis.line = element_line(colour = 'grey15', size = 0.5),
axis.title.y = element_text(margin =
margin(t = 0, r = 10, b = 0, l = 0)),
axis.title.y.right = element_text(margin =
margin(t = 0, r = 0, b = 0, l = 10)))