Столбчатая диаграмма с несколькими логическими переменными по оси X - PullRequest
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Столбчатая диаграмма с несколькими логическими переменными по оси X

0 голосов
/ 02 мая 2018

Я имею дело с данными временных рядов. У меня горизонт 16 временных точек и 3 модели. Я выполнил разложение дисперсии ошибки прогноза для каждой модели и хочу построить FEVD для данной переменной для каждой модели рядом. Я не знаю, ясно ли мне, но предположим, что во время 1 у меня есть 0% для модели 1, 5% для модели 2 и 3% для модели 3. Я хочу построить отдельные столбцы для каждой модели в каждый период времени , Это возможно с ggplot2?

Ниже образец моей базы данных: 

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

EDIT Следуя советам @ A.Suliman, я немного изменил свои данные, выполнив: 

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

и затем сюжет: 

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")

Сюжет

illustration

Но похоже, что некоторые из меток не наносятся на график.

EDIT2 : мне удалось получить нужный график в Excel. Как мне построить это с помощью ggplot?

illustration

1 Ответ

0 голосов
/ 02 мая 2018

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"
 )

enter image description here

Данные 

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())

Данные 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")

enter image description here

Для получения дополнительной информации о маркировке двух переменных в оси X, проверьте здесь . Я не определил switch = x в facet_grid, так как метка оси X будет ниже переменной фасета, как показано здесь , что, я считаю, не круто.

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