Question

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，但是假设在时间1中我的模型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。这可能与ggplot2一起使用吗？

在我的数据库示例下面：

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

修改根据@ A.Suliman的建议，我做了一点改变我的数据：

Data %>% mutate(Models = Model) %>% unite(Shocks, Shock, Model)

然后绘图：

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

但似乎有些标签没有被绘制。

EDIT2 ：我已经设法在Excel中获得所需的情节。我如何用ggplot绘制这个？

Answer 1

<强> 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)

<强> 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。我没有在switch = x中定义facet_grid，因为x轴标签将位于facet变量之下，如here所示，我认为它并不酷。

在x轴上具有多个逻辑变量的堆积条形图

1 个答案: