鴻溝一個單元爲一個單獨的熱圖/多顏色代碼

Question

樣本數據 -

Country segments variable value 
     US A Kerosene 16.09 
     US B Kerosene 2.81 
     US C Kerosene -7.96 
     US D Kerosene 25.76 
     US E Kerosene 75.47 
     US A Petrol 26.82 
     US B Petrol 8.72 
     US C Petrol 2.07 
     US D Petrol 16 
     US E Petrol -11 
     US A Edible Oil 4.02 
     US B Edible Oil 10.6 
     US C Edible Oil 49.05 
     US D Edible Oil 16.07 
     US E Edible Oil 51.87 
     US A LPG -1.07 
     US B LPG 49.19 
     US C LPG 37.9 
     US D LPG 33.05 
     US E LPG 102 

這是該數據爲美國設定，這樣我還有其他14個國家在文件中的數據集。現在我用分層方法制作15個獨立的熱圖。 x軸是變量，y軸是分段和熱圖，值得關注的是每個國家的價值觀。

我在想的是和所有國家一起製作一張幻燈片。因此，最終的結果是 - y軸與區段相同，x軸與國家和每個單元格是一個熱圖，具有四個不同的變量和單個單元格中的對應值。

任何幫助將是偉大的任何想法如何我可以編碼和使一個單元格到一個heapmap有四個不同的值和顏色code.Something下面 - 這將是一個8國家和4段的例子4個變量。

Answer 1

既然你沒有提供一個完整的可重複的例子（或你已經嘗試過的任何代碼），我做了一些假的數據顯示，你會如何做。

library(dplyr) 
library(ggplot2) 

fake.df <- 
expand.grid(Country = c("US", "Canada", "Mexico"), 
      segments = LETTERS[1:5], 
      variable = c("Kerosene", "Petrol", 
         "Edible Oil", "LPG")) %>% 
    mutate(value = runif(length(Country), -20, 50)) 

fake.df.adj <- 
fake.df %>% 
    mutate(xadj = ifelse(variable %in% c("Kerosene", "Edible Oil"), -0.33/2, 0.33/2), 
     yadj = ifelse(variable %in% c("Kerosene", "Petrol"),  0.33/2, -0.33/2), 
     xpos = as.numeric(factor(Country)) + xadj, 
     ypos = as.numeric(factor(segments)) + yadj) 

mutate代碼在您想要繪製的每個中心點周圍創建一個調整框。對於「煤油」和「食用油」，這些瓷磚向左移動了每個Country（因此每個瓷磚的中心爲-0.33/2）之間的空間的1/3。其他人正確轉移。與y調整一樣，但上下左右，而不是左右。

然後，將您的因素（Country和segments）轉換爲數字位置並將它們相加。

---

fake.df.adj 

Country segments variable  value xadj yadj xpos ypos 
1  US  A Kerosene -11.8318607 -0.165 0.165 0.835 1.165 
2 Canada  A Kerosene 39.1181835 -0.165 0.165 1.835 1.165 
3 Mexico  A Kerosene 25.9354644 -0.165 0.165 2.835 1.165 
4  US  B Kerosene -19.3503525 -0.165 0.165 0.835 2.165 
5 Canada  B Kerosene -12.4459506 -0.165 0.165 1.835 2.165 
6 Mexico  B Kerosene 9.9017016 -0.165 0.165 2.835 2.165 
7  US  C Kerosene 37.9767562 -0.165 0.165 0.835 3.165 
8 Canada  C Kerosene 3.4687111 -0.165 0.165 1.835 3.165 
9 Mexico  C Kerosene 0.6280239 -0.165 0.165 2.835 3.165 
10  US  D Kerosene 43.4739464 -0.165 0.165 0.835 4.165 
11 Canada  D Kerosene 8.4671510 -0.165 0.165 1.835 4.165 
12 Mexico  D Kerosene 19.7357609 -0.165 0.165 2.835 4.165 
13  US  E Kerosene -2.6120700 -0.165 0.165 0.835 5.165 
14 Canada  E Kerosene -10.6013792 -0.165 0.165 1.835 5.165 
15 Mexico  E Kerosene -6.2059698 -0.165 0.165 2.835 5.165 
16  US  A  Petrol 17.1079974 0.165 0.165 1.165 1.165 
17 Canada  A  Petrol 46.8944785 0.165 0.165 2.165 1.165 
18 Mexico  A  Petrol 9.3977815 0.165 0.165 3.165 1.165 
19  US  B  Petrol 38.7547489 0.165 0.165 1.165 2.165 
20 Canada  B  Petrol -14.4210703 0.165 0.165 2.165 2.165 
21 Mexico  B  Petrol 32.5160861 0.165 0.165 3.165 2.165 
22  US  C  Petrol -1.3750645 0.165 0.165 1.165 3.165 
23 Canada  C  Petrol 17.0540527 0.165 0.165 2.165 3.165 
24 Mexico  C  Petrol -16.8449931 0.165 0.165 3.165 3.165 
25  US  D  Petrol 33.8465349 0.165 0.165 1.165 4.165 
26 Canada  D  Petrol 43.7369153 0.165 0.165 2.165 4.165 
27 Mexico  D  Petrol 32.2145640 0.165 0.165 3.165 4.165 
28  US  E  Petrol -13.1811532 0.165 0.165 1.165 5.165 
29 Canada  E  Petrol 46.1913082 0.165 0.165 2.165 5.165 
30 Mexico  E  Petrol -7.5030316 0.165 0.165 3.165 5.165 
31  US  A Edible Oil 36.1643957 -0.165 -0.165 0.835 0.835 
32 Canada  A Edible Oil 26.9887728 -0.165 -0.165 1.835 0.835 
33 Mexico  A Edible Oil 9.2825143 -0.165 -0.165 2.835 0.835 
34  US  B Edible Oil 32.7455557 -0.165 -0.165 0.835 1.835 
35 Canada  B Edible Oil 9.0447777 -0.165 -0.165 1.835 1.835 
36 Mexico  B Edible Oil 21.5822081 -0.165 -0.165 2.835 1.835 
37  US  C Edible Oil 2.3906442 -0.165 -0.165 0.835 2.835 
38 Canada  C Edible Oil 25.2536855 -0.165 -0.165 1.835 2.835 
39 Mexico  C Edible Oil 5.0618632 -0.165 -0.165 2.835 2.835 
40  US  D Edible Oil 41.0292115 -0.165 -0.165 0.835 3.835 
41 Canada  D Edible Oil 8.4515698 -0.165 -0.165 1.835 3.835 
42 Mexico  D Edible Oil 11.9080938 -0.165 -0.165 2.835 3.835 
43  US  E Edible Oil 42.2518838 -0.165 -0.165 0.835 4.835 
44 Canada  E Edible Oil 25.6458033 -0.165 -0.165 1.835 4.835 
45 Mexico  E Edible Oil -19.3037443 -0.165 -0.165 2.835 4.835 
46  US  A  LPG 25.8891215 0.165 -0.165 1.165 0.835 
47 Canada  A  LPG -3.1028641 0.165 -0.165 2.165 0.835 
48 Mexico  A  LPG 47.5884154 0.165 -0.165 3.165 0.835 
49  US  B  LPG 11.3000701 0.165 -0.165 1.165 1.835 
50 Canada  B  LPG 26.8041755 0.165 -0.165 2.165 1.835 
51 Mexico  B  LPG -13.0209453 0.165 -0.165 3.165 1.835 
52  US  C  LPG -5.5292231 0.165 -0.165 1.165 2.835 
53 Canada  C  LPG 46.3101034 0.165 -0.165 2.165 2.835 
54 Mexico  C  LPG 19.7622448 0.165 -0.165 3.165 2.835 
55  US  D  LPG 8.5258791 0.165 -0.165 1.165 3.835 
56 Canada  D  LPG 20.5856857 0.165 -0.165 2.165 3.835 
57 Mexico  D  LPG -10.7589733 0.165 -0.165 3.165 3.835 
58  US  E  LPG 14.8577291 0.165 -0.165 1.165 4.835 
59 Canada  E  LPG -6.9590007 0.165 -0.165 2.165 4.835 
60 Mexico  E  LPG -3.1651872 0.165 -0.165 3.165 4.835 

fake.df.adj %>% 
    ggplot(aes(xpos, ypos, fill = value)) + 
    geom_tile(height = 0.33, width = 0.33) + 
    geom_text(aes(label = paste0(round(value, 1), "%"), 
       color = value < 30), show.legend = F) + 
    geom_text(data = fake.df.adj %>% filter(Country == "Mexico", segments == "E"), 
      aes(label = variable, 
       y = ypos + 1.75*yadj), 
      fontface = "bold") + 
    scale_fill_gradient2(labels = function(breaks) paste0(breaks, "%")) + 
    scale_color_manual(values = c("FALSE" = "white", "TRUE" = "black")) + 
    scale_x_continuous(breaks = 1:3, name = NULL, 
        labels = levels(fake.df$Country)) + 
    scale_y_continuous(breaks = 1:5, name = "Segments", 
        labels = levels(fake.df$segments)) + 
    theme_classic() 

現在你可以使用繪圖geom_tile，設置瓷磚的高度和寬度爲0.33。

我在每個圖塊的頂部添加了標籤，四捨五入到小數點後一位，並帶有％符號。爲了使它們很好地適應高價值的黑暗背景填充，我將標籤的顏色美學作爲邏輯表達式，然後將這些值進一步向下映射爲黑色和白色。

然後，無法判斷哪種瓷磚中有哪四種variable屬於哪個瓷磚，因此我通過將數據過濾到墨西哥和E以及稍微移動y位置來標記右上角的那些瓷磚比使用與之前相同的調整值的瓦片的中心更遠。

然後我添加了一個發散的填充調色板並用％符號標記了色彩條。

然後，您必須指定x和y刻度的標籤，因爲它們現在只是數字位置。