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社区首页 >问答首页 >X轴上多个逻辑变量的叠加条形图

问X轴上多个逻辑变量的叠加条形图
EN

Stack Overflow用户
提问于 2018-05-02 00:10:43
回答 1查看 1.1K关注 0票数 0

我在处理时间序列数据。我有一个16个时间点和3个模型的地平线。我对每个模型进行了预测误差方差分解，并且希望为每个模型并排绘制给定变量的FEVD。我不知道我是否清楚，但假设在第一时间，我有0%的模型1，5%的模型2和3%的模型3。我想为每个模型在每一个时间段分别画出不同的条形图。这与ggplot2是可能的吗？

下面是我的数据库示例：

代码语言：javascript
复制
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

编辑遵循@A.Suliman的建议，我做了一些修改数据的操作：

代码语言：javascript
复制
Data %>% mutate(Models = Model) %>% unite(Shocks, Shock, Model)

然后情节：

代码语言：javascript
复制
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")

情节是

​

​

但似乎有些标签并没有被标出。

EDIT2：我已经在Excel中获得了所需的绘图。我该怎么用ggplot来画这个呢？

​

​

r
ggplot2
geom-bar
EN

回答 1

Stack Overflow用户

回答已采纳

发布于 2018-05-02 05:38:33

1)

代码语言：javascript
复制
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

代码语言：javascript
复制
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)

代码语言：javascript
复制
 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

代码语言：javascript
复制
#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轴中标注两个变量的更多想法，检查这里。我并没有将switch = x在facet_grid中定义为x轴标签将低于facet变量，这显示了这里，我认为这并不酷。

票数 2
EN
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：

https://stackoverflow.com/questions/50125478

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