R语言 在条形图中添加标签和删除条形

6qfn3psc  于 2022-12-05  发布在  其他
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我有一个数据框架results_df，其中包含模型及其各自的性能指标。
下面的条形图中显示了9个模型。我想删除AR(1)模型（第一个条形），并在每个条形上添加标签，以计算y轴（MAPE）值。有办法吗？

结果_df：dput（结果_df）

structure(list(Model = c("Naive (baseline)", "Naive (baseline)", 
"Holt-Winter's Smoothing", "Holt-Winter's Smoothing", "Exponential Smoothing (AAN)", 
"Exponential Smoothing (AAN)", "Exponential Smoothing (AAN)", 
"Exponential Smoothing (AAN)", "Exponential Smoothing (MMN)", 
"Exponential Smoothing (MMN)", "Exponential Smoothing (MMdN)", 
"Exponential Smoothing (MMdN)", "Simple Regression", "Simple Regression", 
"AR(1)", "AR(1)", "ARIMA", "ARIMA", "Neural Network Autoregression", 
"Neural Network Autoregression", "Neural Network Autoregression", 
"Neural Network Autoregression", "Neural Network Autoregression", 
"Neural Network Autoregression", "Neural Network Autoregression", 
"Neural Network Autoregression", "Neural Network Autoregression", 
"Neural Network Autoregression"), Set = c("Train", "Test", "Train", 
"Test", "Train", "Test", "Train", "Test", "Train", "Test", "Train", 
"Test", "Train", "Test", "Train", "Test", "Train", "Test", "Train", 
"Test", "Train", "Test", "Train", "Test", "Train", "Test", "Train", 
"Test"), ME = c(783.614908859554, 261.373668023744, 14.3109110454195, 
195.100466156422, 26.3876287540668, 463.951556368907, 18.0130216685751, 
359.483123328568, -22.1643363307684, -339.196046699131, 12.6912377584583, 
518.09851679099, 0.0000000000000174739705362564, -529.41545982092, 
-1.22546154090165, 3925.73996146196, 8.85383136321011, 124.533565617977, 
0.284309224688646, -6.72677362104004, 0.284309224688646, -6.72677362104004, 
0.284309224688646, -6.72677362104004, 0.284309224688646, -6.72677362104004, 
0.284309224688646, -6.72677362104004), RMSE = c(1217.13789794065, 
751.642354127382, 500.820891022085, 477.824050637917, 519.473902002702, 
642.857862958026, 519.161630651347, 557.229990542789, 523.067637426936, 
589.028868073331, 528.35952997731, 686.44615027821, 524.569099190936, 
735.240889009488, 454.850494992402, 3949.12093581915, 512.863753849202, 
533.856079481684, 135.490259727469, 485.401392424817, 135.490259727469, 
485.401392424817, 135.490259727469, 485.401392424817, 135.490259727469, 
485.401392424817, 135.490259727469, 485.401392424817), MAE = c(996.051963196766, 
506.736876037775, 260.159300687626, 353.225967700696, 294.653723455236, 
540.046166565811, 295.649955005283, 444.757424142394, 309.824947101753, 
423.457221068304, 306.092832486747, 589.143961366613, 392.62157043408, 
583.197541495569, 287.909371174162, 3925.73996146196, 248.525867346098, 
371.164249425045, 84.7526369385231, 293.994074536976, 84.7526369385231, 
293.994074536976, 84.7526369385231, 293.994074536976, 84.7526369385231, 
293.994074536976, 84.7526369385231, 293.994074536976), MPE = c(17.1491069887359, 
4.32046155430935, -6.74519182268278, 2.87120046577417, -6.82283325669752, 
9.90612395387752, -7.20214793423834, 7.23484722049863, -8.92406901313896, 
-10.618241704038, -7.35920566787723, 11.316397549041, -8.15817902292851, 
-16.2120961149314, 278.150530756537, 100.940759473326, -4.78085408038733, 
0.903132637273637, -1.0450388130686, -3.16256781033905, -1.0450388130686, 
-3.16256781033905, -1.0450388130686, -3.16256781033905, -1.0450388130686, 
-3.16256781033905, -1.0450388130686, -3.16256781033905), MAPE = c(37.6646414975729, 
14.2674058209844, 13.4253935410948, 10.2678719351011, 17.2343529926111, 
14.5662838896106, 17.3299734983734, 12.2848516454307, 18.1093841365566, 
12.5514842599768, 17.8614665736195, 15.7476258421098, 21.2960874782265, 
17.3861366179269, 328.041997124712, 100.940759473326, 12.5829038948982, 
10.9566576520148, 3.39520095739983, 9.9164317537263, 3.39520095739983, 
9.9164317537263, 3.39520095739983, 9.9164317537263, 3.39520095739983, 
9.9164317537263, 3.39520095739983, 9.9164317537263), MASE = c(1, 
0.508745421686069, NaN, NaN, 0.295821638169924, 0.542186739768643, 
0.2968218184686, 0.446520302730967, 0.311052995776836, 0.425135672349106, 
0.307306088232949, 0.591479142790696, 0.39417779889112, 0.585509153180959, 
0.374562870610186, 5.10728922520835, 0.2495109457427, 0.372635427808221, 
0.0850885697434046, 0.295159374610759, 0.0850885697434046, 0.295159374610759, 
0.0850885697434046, 0.295159374610759, 0.0850885697434046, 0.295159374610759, 
0.0850885697434046, 0.295159374610759), ACF1 = c(0.477101737419842, 
-0.236641331303488, -0.0688752655501213, 0.0486772478154593, 
0.121398483630841, 0.085203686984427, 0.124889761137534, 0.00218743715991606, 
0.191776077581811, 0.186812955247135, 0.209332741368281, 0.105740160073441, 
0.470691853904666, 0.0555995310408417, -0.00100961596465889, 
0.0152620209423001, -0.0743813163863567, 0.0591555324997216, 
0.0603986214142024, 0.00539281074890409, 0.0603986214142024, 
0.00539281074890409, 0.0603986214142024, 0.00539281074890409, 
0.0603986214142024, 0.00539281074890409, 0.0603986214142024, 
0.00539281074890409), Theil.s.U = c(NA, 0.679390982324587, NA, 
0.483244519716164, NA, 0.658305710036264, NA, 0.572169692732178, 
NA, 0.554481369845331, NA, 0.704434769895274, NA, 0.724366674060867, 
NA, 4.02609591299822, NA, 0.512019993546249, NA, 0.403328477993201, 
NA, 0.403328477993201, NA, 0.403328477993201, NA, 0.403328477993201, 
NA, 0.403328477993201)), row.names = c("Training set", "Test set", 
"Training set1", "Test set1", "Training set2", "Test set2", "Training set3", 
"Test set3", "Training set4", "Test set4", "Training set5", "Test set5", 
"Training set6", "Test set6", "Training set7", "Test set7", "Training set8", 
"Test set8", "Training set9", "Test set9", "Training set10", 
"Test set10", "Training set11", "Test set11", "Training set12", 
"Test set12", "Training set13", "Test set13"), class = "data.frame")

代码：

results_df_reshape = reshape2::melt(results_df, c("MAPE", "Model"))

ggplot(results_df_reshape) +
  geom_bar(aes(x = Model, y = MAPE, fill = Model),
           stat = "identity",
           position = "dodge") +
  labs(title = "MAPE of All Models",
       x = "Model",
       y = "MAPE",
       bty = "l")

r

2条答案

按热度按时间
bvjveswy

bvjveswy1#

library(dplyr)
library(ggplot2)

results_df_reshape <-
  reshape2::melt(results_df, c("MAPE", "Model")) %>% 
  #Removal of AR(1)
  filter(Model != "AR(1)")

results_df_reshape %>%
  ggplot() +
  geom_bar(aes(x = Model, y = MAPE, fill = Model),
           stat = "identity",
           position = "dodge") +
  #Label for MAPE
  geom_text(
    data = results_df_reshape %>% 
      group_by(Model) %>% 
      summarise(MAPE = max(MAPE),label_mape = round(sum(MAPE,na.rm= TRUE),2)),
    aes(x = Model, y = MAPE,label = label_mape),
    nudge_y = 1)+
  labs(title = "MAPE of All Models",
       x = "Model",
       y = "MAPE",
       bty = "l")

赞(0）分享  回复(0）举报  2022-12-05
htzpubme

htzpubme2#

我已经用了求和：

library(tidyverse)

df %>% 
  pivot_longer(-c(Model, MAPE, Set)) %>% 
  filter(Model != "AR(1)") %>% 
  group_by(Model) %>% 
  summarise(Mape_sum= sum(MAPE, na.rm = TRUE)) %>% 
  ggplot(aes(x = Model, y = Mape_sum, fill = Model))+
  geom_col()+
  geom_text(aes(label=round(Mape_sum, 1)), size=3.5, vjust=-1)+
  labs(title = "MAPE of All Models",  x = "Model", y = "MAPE", bty = "l")

赞(0）分享  回复(0）举报  2022-12-05
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