rggplot2geom-bar
Stacked bar plot with multiple logical variables in x axis
I'm dealing with time series data. I have an horizon of 16 time points and 3 models. I performed a Forecast Error Variance Decomposition for each model and I want to plot the FEVD for a given variable for every model side by side. I don't know if I'm being clear, but suppose in Time 1 I have 0% for model 1, 5% for model 2 and 3% for model 3. I want to plot separate bars for each model in each time period. Is this possible with ggplot2?
Below a sample of my database:
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 Following the suggestions of @A.Suliman, I change my data a little by doing:
Data %>% mutate(Models = Model) %>% unite(Shocks, Shock, Model)
and then plot:
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")
The plot is
But it seems that some of the labels are not being plotted.
EDIT2: I've managed to get the desired plot in Excel. How do I plot this with ggplot?
Solution
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"
)
Data
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())
Data 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")
For more ideas about labeling two variables in X Axis, check here. I didn't define switch = x in facet_grid as x-axis label will be below facet variable as shows here, which I believe isn't cool.
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