在 x 轴上具有多个逻辑变量的堆积条形图
Stacked bar plot with multiple logical variables in x axis
我正在处理时间序列数据。我有 16 个时间点和 3 个模型的视野。我为每个模型执行了预测误差方差分解，我想并排绘制每个模型的给定变量的 FEVD。我不知道我是否清楚，但假设在时间 1 中，模型 1 为 0%，模型 2 为 5%，模型 3 为 3%。我想在每个时间段为每个模型绘制单独的条形图.这对 ggplot2 可行吗？
下面是我的数据库示例：
Horizon Variable Response Shock Country Model
1 GDP 0.000000000 PCOM Brazil Model 1
2 GDP 0.404381850 PCOM Brazil Model 1
3 GDP 0.401069156 PCOM Brazil Model 1
4 GDP 0.368749090 PCOM Brazil Model 1
5 GDP 0.351268777 PCOM Brazil Model 1
6 GDP 0.345947281 PCOM Brazil Model 1
7 GDP 0.347482783 PCOM Brazil Model 1
8 GDP 0.352164160 PCOM Brazil Model 1
9 GDP 0.357781202 PCOM Brazil Model 1
10 GDP 0.363198705 PCOM Brazil Model 1
11 GDP 0.367974083 PCOM Brazil Model 1
12 GDP 0.372078699 PCOM Brazil Model 1
13 GDP 0.375666736 PCOM Brazil Model 1
14 GDP 0.378901315 PCOM Brazil Model 1
15 GDP 0.381878427 PCOM Brazil Model 1
16 GDP 0.384630719 PCOM Brazil Model 1
1 GDP 0.000000000 PCOM Brazil Model 2
2 GDP 0.301533139 PCOM Brazil Model 2
3 GDP 0.308349733 PCOM Brazil Model 2
4 GDP 0.263588570 PCOM Brazil Model 2
5 GDP 0.239982463 PCOM Brazil Model 2
6 GDP 0.235266964 PCOM Brazil Model 2
7 GDP 0.240041605 PCOM Brazil Model 2
8 GDP 0.248219530 PCOM Brazil Model 2
9 GDP 0.256646193 PCOM Brazil Model 2
10 GDP 0.263902054 PCOM Brazil Model 2
11 GDP 0.269612632 PCOM Brazil Model 2
12 GDP 0.273995159 PCOM Brazil Model 2
13 GDP 0.277464105 PCOM Brazil Model 2
14 GDP 0.280368261 PCOM Brazil Model 2
15 GDP 0.282903588 PCOM Brazil Model 2
16 GDP 0.285144263 PCOM Brazil Model 2
1 GDP 0.000000000 PCOM Brazil Model 3
2 GDP 0.034171019 PCOM Brazil Model 3
3 GDP 0.024779691 PCOM Brazil Model 3
4 GDP 0.016802809 PCOM Brazil Model 3
5 GDP 0.011206834 PCOM Brazil Model 3
6 GDP 0.009575322 PCOM Brazil Model 3
7 GDP 0.008935842 PCOM Brazil Model 3
8 GDP 0.008605141 PCOM Brazil Model 3
9 GDP 0.008182777 PCOM Brazil Model 3
10 GDP 0.007498230 PCOM Brazil Model 3
11 GDP 0.006684634 PCOM Brazil Model 3
12 GDP 0.005917865 PCOM Brazil Model 3
13 GDP 0.005320365 PCOM Brazil Model 3
14 GDP 0.004940644 PCOM Brazil Model 3
15 GDP 0.004782973 PCOM Brazil Model 3
16 GDP 0.004831577 PCOM Brazil Model 3
编辑
按照@A.Suliman的建议，我通过以下方式稍微更改了我的数据：
Data %>% mutate(Models = Model) %>% unite(Shocks, Shock, Model)
然后绘制：
gdp_br <- filter(Data, Variable == "GDP")
xticks <- seq(min(0), max(16), by = 1)
ggplot(gdp_br, aes(as.factor(Horizon), Response, fill = Shocks, group = Models)) +
geom_bar(stat = "identity", width = 0.7, position = position_dodge(width = 0.8)) +
theme(plot.title = element_text(size = 10, face = "bold", lineheight = 1, hjust = 0),
axis.text.x = element_text(size = rel(1.1), angle = 10),
legend.position = "bottom",
legend.title = element_blank()) +
scale_y_continuous(labels = percent_format()) +
labs(x = "Horizon")
剧情是
不过好像有些标签没有画出来。
EDIT2：我已经设法在 Excel 中获得了想要的情节。我如何使用 ggplot 绘制它？
1)
library(ggplot2)
library(scales)
ggplot(Data, aes(as.factor(Horizon), Response,fill= Model)) +
geom_bar( stat="identity", width = 0.7, position = position_dodge(width = 0.8)) +
theme(plot.title = element_text(size = 10, face = "bold", lineheight=1,hjust = 0), axis.text.x = element_text( size = rel(1.1), angle = 10),legend.position = "bottom",legend.title = element_blank()) + scale_y_continuous(labels = percent_format()) +
labs(
x = "Horizon"
#y = "Percentages",
#title = gg_title,
#subtitle = gg_title_subtitle
#caption = "Data from fueleconomy.gov"
)
数据
Input = ("
Horizon Variable Response Shock Country Model
1 GDP 0.000000000 PCOM Brazil 'Model 1'
2 GDP 0.404381850 PCOM Brazil 'Model 1'
3 GDP 0.401069156 PCOM Brazil 'Model 1'
4 GDP 0.368749090 PCOM Brazil 'Model 1'
5 GDP 0.351268777 PCOM Brazil 'Model 1'
6 GDP 0.345947281 PCOM Brazil 'Model 1'
7 GDP 0.347482783 PCOM Brazil 'Model 1'
8 GDP 0.352164160 PCOM Brazil 'Model 1'
9 GDP 0.357781202 PCOM Brazil 'Model 1'
10 GDP 0.363198705 PCOM Brazil 'Model 1'
11 GDP 0.367974083 PCOM Brazil 'Model 1'
12 GDP 0.372078699 PCOM Brazil 'Model 1'
13 GDP 0.375666736 PCOM Brazil 'Model 1'
14 GDP 0.378901315 PCOM Brazil 'Model 1'
15 GDP 0.381878427 PCOM Brazil 'Model 1'
16 GDP 0.384630719 PCOM Brazil 'Model 1'
1 GDP 0.000000000 PCOM Brazil 'Model 2'
2 GDP 0.301533139 PCOM Brazil 'Model 2'
3 GDP 0.308349733 PCOM Brazil 'Model 2'
4 GDP 0.263588570 PCOM Brazil 'Model 2'
5 GDP 0.239982463 PCOM Brazil 'Model 2'
6 GDP 0.235266964 PCOM Brazil 'Model 2'
7 GDP 0.240041605 PCOM Brazil 'Model 2'
8 GDP 0.248219530 PCOM Brazil 'Model 2'
9 GDP 0.256646193 PCOM Brazil 'Model 2'
10 GDP 0.263902054 PCOM Brazil 'Model 2'
11 GDP 0.269612632 PCOM Brazil 'Model 2'
12 GDP 0.273995159 PCOM Brazil 'Model 2'
13 GDP 0.277464105 PCOM Brazil 'Model 2'
14 GDP 0.280368261 PCOM Brazil 'Model 2'
15 GDP 0.282903588 PCOM Brazil 'Model 2'
16 GDP 0.285144263 PCOM Brazil 'Model 2'
1 GDP 0.000000000 PCOM Brazil 'Model 3'
2 GDP 0.034171019 PCOM Brazil 'Model 3'
3 GDP 0.024779691 PCOM Brazil 'Model 3'
4 GDP 0.016802809 PCOM Brazil 'Model 3'
5 GDP 0.011206834 PCOM Brazil 'Model 3'
6 GDP 0.009575322 PCOM Brazil 'Model 3'
7 GDP 0.008935842 PCOM Brazil 'Model 3'
8 GDP 0.008605141 PCOM Brazil 'Model 3'
9 GDP 0.008182777 PCOM Brazil 'Model 3'
10 GDP 0.007498230 PCOM Brazil 'Model 3'
11 GDP 0.006684634 PCOM Brazil 'Model 3'
12 GDP 0.005917865 PCOM Brazil 'Model 3'
13 GDP 0.005320365 PCOM Brazil 'Model 3'
14 GDP 0.004940644 PCOM Brazil 'Model 3'
15 GDP 0.004782973 PCOM Brazil 'Model 3'
16 GDP 0.004831577 PCOM Brazil 'Model 3'
")
Data = read.table(textConnection(Input),header=TRUE)
2)
ggplot(Data,aes(Model, Response, fill=Shock)) +
geom_bar( stat = "identity", position = "stack") +
facet_grid(~ Horizon, scales = "free_x", space = "free_x") +
theme_bw() +
theme(panel.spacing = unit(0,"lines"),
strip.background = element_blank(),plot.title = element_text(size = 10, face = "bold", lineheight=1,hjust = 0), axis.text.x = element_text( size = rel(1.1), angle = 90),legend.position = "bottom") + scale_y_continuous(labels = percent_format())
数据2
#Using dput(Data)
Data <- structure(list(Horizon = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 12L, 13L, 14L, 15L, 16L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
14L, 15L, 16L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 12L, 13L, 14L, 15L, 16L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L), Variable = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "GDP", class = "factor"),
Response = c(0, 0.40438185, 0.401069156, 0.36874909, 0.351268777,
0.345947281, 0.347482783, 0.35216416, 0.357781202, 0.363198705,
0.367974083, 0.372078699, 0.375666736, 0.378901315, 0.381878427,
0.384630719, 0, 0.301533139, 0.308349733, 0.26358857, 0.239982463,
0.235266964, 0.240041605, 0.24821953, 0.256646193, 0.263902054,
0.269612632, 0.273995159, 0.277464105, 0.280368261, 0.282903588,
0.285144263, 0, 0.034171019, 0.024779691, 0.016802809, 0.011206834,
0.009575322, 0.008935842, 0.008605141, 0.008182777, 0.00749823,
0.006684634, 0.005917865, 0.005320365, 0.004940644, 0.004782973,
0.004831577, 0.1, 0.50438185, 0.501069156, 0.46874909, 0.451268777,
0.445947281, 0.447482783, 0.45216416, 0.457781202, 0.463198705,
0.467974083, 0.472078699, 0.475666736, 0.478901315, 0.481878427,
0.484630719, 0.1, 0.401533139, 0.408349733, 0.36358857, 0.339982463,
0.335266964, 0.340041605, 0.34821953, 0.356646193, 0.363902054,
0.369612632, 0.373995159, 0.377464105, 0.380368261, 0.382903588,
0.385144263, 0.1, 0.134171019, 0.124779691, 0.116802809,
0.111206834, 0.109575322, 0.108935842, 0.108605141, 0.108182777,
0.10749823, 0.106684634, 0.105917865, 0.105320365, 0.104940644,
0.104782973, 0.104831577, 0.2, 0.60438185, 0.601069156, 0.56874909,
0.551268777, 0.545947281, 0.547482783, 0.55216416, 0.557781202,
0.563198705, 0.567974083, 0.572078699, 0.575666736, 0.578901315,
0.581878427, 0.584630719, 0.2, 0.501533139, 0.508349733,
0.46358857, 0.439982463, 0.435266964, 0.440041605, 0.44821953,
0.456646193, 0.463902054, 0.469612632, 0.473995159, 0.477464105,
0.480368261, 0.482903588, 0.485144263, 0.2, 0.234171019,
0.224779691, 0.216802809, 0.211206834, 0.209575322, 0.208935842,
0.208605141, 0.208182777, 0.20749823, 0.206684634, 0.205917865,
0.205320365, 0.204940644, 0.204782973, 0.204831577), Shock = structure(c(3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("AAA", "BBB",
"PCOM"), class = "factor"), Country = structure(c(1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "Brazil", class = "factor"),
Model = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("Model 1",
"Model 2", "Model 3"), class = "factor")), .Names = c("Horizon",
"Variable", "Response", "Shock", "Country", "Model"),
row.names = c(NA,-144L), class = "data.frame")
有关在 X 轴中标记两个变量的更多想法，check here. I didn't define switch = x in facet_grid as x-axis label will be below facet variable as shows here，我认为这并不酷。
我正在处理时间序列数据。我有 16 个时间点和 3 个模型的视野。我为每个模型执行了预测误差方差分解，我想并排绘制每个模型的给定变量的 FEVD。我不知道我是否清楚，但假设在时间 1 中，模型 1 为 0%，模型 2 为 5%，模型 3 为 3%。我想在每个时间段为每个模型绘制单独的条形图.这对 ggplot2 可行吗？
下面是我的数据库示例：
Horizon Variable Response Shock Country Model
1 GDP 0.000000000 PCOM Brazil Model 1
2 GDP 0.404381850 PCOM Brazil Model 1
3 GDP 0.401069156 PCOM Brazil Model 1
4 GDP 0.368749090 PCOM Brazil Model 1
5 GDP 0.351268777 PCOM Brazil Model 1
6 GDP 0.345947281 PCOM Brazil Model 1
7 GDP 0.347482783 PCOM Brazil Model 1
8 GDP 0.352164160 PCOM Brazil Model 1
9 GDP 0.357781202 PCOM Brazil Model 1
10 GDP 0.363198705 PCOM Brazil Model 1
11 GDP 0.367974083 PCOM Brazil Model 1
12 GDP 0.372078699 PCOM Brazil Model 1
13 GDP 0.375666736 PCOM Brazil Model 1
14 GDP 0.378901315 PCOM Brazil Model 1
15 GDP 0.381878427 PCOM Brazil Model 1
16 GDP 0.384630719 PCOM Brazil Model 1
1 GDP 0.000000000 PCOM Brazil Model 2
2 GDP 0.301533139 PCOM Brazil Model 2
3 GDP 0.308349733 PCOM Brazil Model 2
4 GDP 0.263588570 PCOM Brazil Model 2
5 GDP 0.239982463 PCOM Brazil Model 2
6 GDP 0.235266964 PCOM Brazil Model 2
7 GDP 0.240041605 PCOM Brazil Model 2
8 GDP 0.248219530 PCOM Brazil Model 2
9 GDP 0.256646193 PCOM Brazil Model 2
10 GDP 0.263902054 PCOM Brazil Model 2
11 GDP 0.269612632 PCOM Brazil Model 2
12 GDP 0.273995159 PCOM Brazil Model 2
13 GDP 0.277464105 PCOM Brazil Model 2
14 GDP 0.280368261 PCOM Brazil Model 2
15 GDP 0.282903588 PCOM Brazil Model 2
16 GDP 0.285144263 PCOM Brazil Model 2
1 GDP 0.000000000 PCOM Brazil Model 3
2 GDP 0.034171019 PCOM Brazil Model 3
3 GDP 0.024779691 PCOM Brazil Model 3
4 GDP 0.016802809 PCOM Brazil Model 3
5 GDP 0.011206834 PCOM Brazil Model 3
6 GDP 0.009575322 PCOM Brazil Model 3
7 GDP 0.008935842 PCOM Brazil Model 3
8 GDP 0.008605141 PCOM Brazil Model 3
9 GDP 0.008182777 PCOM Brazil Model 3
10 GDP 0.007498230 PCOM Brazil Model 3
11 GDP 0.006684634 PCOM Brazil Model 3
12 GDP 0.005917865 PCOM Brazil Model 3
13 GDP 0.005320365 PCOM Brazil Model 3
14 GDP 0.004940644 PCOM Brazil Model 3
15 GDP 0.004782973 PCOM Brazil Model 3
16 GDP 0.004831577 PCOM Brazil Model 3
编辑 按照@A.Suliman的建议，我通过以下方式稍微更改了我的数据：
Data %>% mutate(Models = Model) %>% unite(Shocks, Shock, Model)
然后绘制：
gdp_br <- filter(Data, Variable == "GDP")
xticks <- seq(min(0), max(16), by = 1)
ggplot(gdp_br, aes(as.factor(Horizon), Response, fill = Shocks, group = Models)) +
geom_bar(stat = "identity", width = 0.7, position = position_dodge(width = 0.8)) +
theme(plot.title = element_text(size = 10, face = "bold", lineheight = 1, hjust = 0),
axis.text.x = element_text(size = rel(1.1), angle = 10),
legend.position = "bottom",
legend.title = element_blank()) +
scale_y_continuous(labels = percent_format()) +
labs(x = "Horizon")
剧情是
不过好像有些标签没有画出来。
EDIT2：我已经设法在 Excel 中获得了想要的情节。我如何使用 ggplot 绘制它？
1)
library(ggplot2)
library(scales)
ggplot(Data, aes(as.factor(Horizon), Response,fill= Model)) +
geom_bar( stat="identity", width = 0.7, position = position_dodge(width = 0.8)) +
theme(plot.title = element_text(size = 10, face = "bold", lineheight=1,hjust = 0), axis.text.x = element_text( size = rel(1.1), angle = 10),legend.position = "bottom",legend.title = element_blank()) + scale_y_continuous(labels = percent_format()) +
labs(
x = "Horizon"
#y = "Percentages",
#title = gg_title,
#subtitle = gg_title_subtitle
#caption = "Data from fueleconomy.gov"
)
数据
Input = ("
Horizon Variable Response Shock Country Model
1 GDP 0.000000000 PCOM Brazil 'Model 1'
2 GDP 0.404381850 PCOM Brazil 'Model 1'
3 GDP 0.401069156 PCOM Brazil 'Model 1'
4 GDP 0.368749090 PCOM Brazil 'Model 1'
5 GDP 0.351268777 PCOM Brazil 'Model 1'
6 GDP 0.345947281 PCOM Brazil 'Model 1'
7 GDP 0.347482783 PCOM Brazil 'Model 1'
8 GDP 0.352164160 PCOM Brazil 'Model 1'
9 GDP 0.357781202 PCOM Brazil 'Model 1'
10 GDP 0.363198705 PCOM Brazil 'Model 1'
11 GDP 0.367974083 PCOM Brazil 'Model 1'
12 GDP 0.372078699 PCOM Brazil 'Model 1'
13 GDP 0.375666736 PCOM Brazil 'Model 1'
14 GDP 0.378901315 PCOM Brazil 'Model 1'
15 GDP 0.381878427 PCOM Brazil 'Model 1'
16 GDP 0.384630719 PCOM Brazil 'Model 1'
1 GDP 0.000000000 PCOM Brazil 'Model 2'
2 GDP 0.301533139 PCOM Brazil 'Model 2'
3 GDP 0.308349733 PCOM Brazil 'Model 2'
4 GDP 0.263588570 PCOM Brazil 'Model 2'
5 GDP 0.239982463 PCOM Brazil 'Model 2'
6 GDP 0.235266964 PCOM Brazil 'Model 2'
7 GDP 0.240041605 PCOM Brazil 'Model 2'
8 GDP 0.248219530 PCOM Brazil 'Model 2'
9 GDP 0.256646193 PCOM Brazil 'Model 2'
10 GDP 0.263902054 PCOM Brazil 'Model 2'
11 GDP 0.269612632 PCOM Brazil 'Model 2'
12 GDP 0.273995159 PCOM Brazil 'Model 2'
13 GDP 0.277464105 PCOM Brazil 'Model 2'
14 GDP 0.280368261 PCOM Brazil 'Model 2'
15 GDP 0.282903588 PCOM Brazil 'Model 2'
16 GDP 0.285144263 PCOM Brazil 'Model 2'
1 GDP 0.000000000 PCOM Brazil 'Model 3'
2 GDP 0.034171019 PCOM Brazil 'Model 3'
3 GDP 0.024779691 PCOM Brazil 'Model 3'
4 GDP 0.016802809 PCOM Brazil 'Model 3'
5 GDP 0.011206834 PCOM Brazil 'Model 3'
6 GDP 0.009575322 PCOM Brazil 'Model 3'
7 GDP 0.008935842 PCOM Brazil 'Model 3'
8 GDP 0.008605141 PCOM Brazil 'Model 3'
9 GDP 0.008182777 PCOM Brazil 'Model 3'
10 GDP 0.007498230 PCOM Brazil 'Model 3'
11 GDP 0.006684634 PCOM Brazil 'Model 3'
12 GDP 0.005917865 PCOM Brazil 'Model 3'
13 GDP 0.005320365 PCOM Brazil 'Model 3'
14 GDP 0.004940644 PCOM Brazil 'Model 3'
15 GDP 0.004782973 PCOM Brazil 'Model 3'
16 GDP 0.004831577 PCOM Brazil 'Model 3'
")
Data = read.table(textConnection(Input),header=TRUE)
2)
ggplot(Data,aes(Model, Response, fill=Shock)) +
geom_bar( stat = "identity", position = "stack") +
facet_grid(~ Horizon, scales = "free_x", space = "free_x") +
theme_bw() +
theme(panel.spacing = unit(0,"lines"),
strip.background = element_blank(),plot.title = element_text(size = 10, face = "bold", lineheight=1,hjust = 0), axis.text.x = element_text( size = rel(1.1), angle = 90),legend.position = "bottom") + scale_y_continuous(labels = percent_format())
数据2
#Using dput(Data)
Data <- structure(list(Horizon = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 12L, 13L, 14L, 15L, 16L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
14L, 15L, 16L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 12L, 13L, 14L, 15L, 16L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L), Variable = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "GDP", class = "factor"),
Response = c(0, 0.40438185, 0.401069156, 0.36874909, 0.351268777,
0.345947281, 0.347482783, 0.35216416, 0.357781202, 0.363198705,
0.367974083, 0.372078699, 0.375666736, 0.378901315, 0.381878427,
0.384630719, 0, 0.301533139, 0.308349733, 0.26358857, 0.239982463,
0.235266964, 0.240041605, 0.24821953, 0.256646193, 0.263902054,
0.269612632, 0.273995159, 0.277464105, 0.280368261, 0.282903588,
0.285144263, 0, 0.034171019, 0.024779691, 0.016802809, 0.011206834,
0.009575322, 0.008935842, 0.008605141, 0.008182777, 0.00749823,
0.006684634, 0.005917865, 0.005320365, 0.004940644, 0.004782973,
0.004831577, 0.1, 0.50438185, 0.501069156, 0.46874909, 0.451268777,
0.445947281, 0.447482783, 0.45216416, 0.457781202, 0.463198705,
0.467974083, 0.472078699, 0.475666736, 0.478901315, 0.481878427,
0.484630719, 0.1, 0.401533139, 0.408349733, 0.36358857, 0.339982463,
0.335266964, 0.340041605, 0.34821953, 0.356646193, 0.363902054,
0.369612632, 0.373995159, 0.377464105, 0.380368261, 0.382903588,
0.385144263, 0.1, 0.134171019, 0.124779691, 0.116802809,
0.111206834, 0.109575322, 0.108935842, 0.108605141, 0.108182777,
0.10749823, 0.106684634, 0.105917865, 0.105320365, 0.104940644,
0.104782973, 0.104831577, 0.2, 0.60438185, 0.601069156, 0.56874909,
0.551268777, 0.545947281, 0.547482783, 0.55216416, 0.557781202,
0.563198705, 0.567974083, 0.572078699, 0.575666736, 0.578901315,
0.581878427, 0.584630719, 0.2, 0.501533139, 0.508349733,
0.46358857, 0.439982463, 0.435266964, 0.440041605, 0.44821953,
0.456646193, 0.463902054, 0.469612632, 0.473995159, 0.477464105,
0.480368261, 0.482903588, 0.485144263, 0.2, 0.234171019,
0.224779691, 0.216802809, 0.211206834, 0.209575322, 0.208935842,
0.208605141, 0.208182777, 0.20749823, 0.206684634, 0.205917865,
0.205320365, 0.204940644, 0.204782973, 0.204831577), Shock = structure(c(3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("AAA", "BBB",
"PCOM"), class = "factor"), Country = structure(c(1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "Brazil", class = "factor"),
Model = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("Model 1",
"Model 2", "Model 3"), class = "factor")), .Names = c("Horizon",
"Variable", "Response", "Shock", "Country", "Model"),
row.names = c(NA,-144L), class = "data.frame")
有关在 X 轴中标记两个变量的更多想法，check here. I didn't define switch = x in facet_grid as x-axis label will be below facet variable as shows here，我认为这并不酷。