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據幀聚集的集團 - 按範圍分離一列值

2016-12-27 37 views
-1

我有一個數據幀如下：據幀聚集的集團 - 按範圍分離一列值

parent<- c('a', 'b', 'c', 'd', 
     'e', 'f', 'g', 'h', 
     'i', 'j', 'k', 'l', 
     'm', 'n', 'o', 'p', 
     'q', 'r', 's', 't', 
     'u', 'v', 'w', 'x', 
     'y', 'z') 
child<- c('A', 'B', 'C', 'D', 
     'E', 'F', 'G', 'H', 
     'I', 'J', 'K', 'L', 
     'M', 'N', 'O', 'P', 
     'Q', 'R', 'S', 'T', 
     'U', 'V', 'W', 'X', 
     'Y', 'Z') 
Type<- c('desktop', 'desktop', 'desktop', 'desktop', 
     'desktop', 'desktop', 'desktop', 'desktop', 
     'desktop', 'desktop', 'desktop', 'desktop', 
     'desktop', 'desktop', 'desktop', 'desktop', 
     'desktop', 'desktop', 'desktop', 'desktop', 
     'desktop', 'desktop', 'desktop', 'desktop', 
     'desktop', 'desktop') 
Size<- c('MEDIUM', 'MEDIUM', 'LARGE', 'LARGE', 
     'SMALL', 'MEDIUM', 'LARGE', 'SMALL', 
     'MEDIUM', 'SMALL', 'LARGE', 'LARGE', 
     'SMALL', 'SMALL', 'LARGE', 'LARGE', 
     'MEDIUM', 'SMALL', 'SMALL', 'MEDIUM', 
     'LARGE', 'MEDIUM', 'SMALL', 'MEDIUM', 
     'LARGE', 'MEDIUM') 
Revenue<- c(22138.16, 18617.94, 12394.36, 10535.76, 
     8901.41, 7320.17, 3821.40, 2811.50, 
     2483.10, 2145.76, 2138.41, 2037.67, 
     1950.52, 1837.93, 1737.68, 1554.61, 
     1374.40, 1334.02, 1214.60, 1191.41, 
     1189.56, 1174.55, 1162.80, 1131.29, 
     1127.05, 1108.53) 
NumberofSales<- c(1954720, 5129937, 1086104, 970326, 
        1608012, 746613, 333424, 236643, 
        352294, 587541, 209218, 342455, 
        192670, 340580, 275260, 248049, 
        251790, 128845, 303515, 112218, 
        149878, 226633, 194973, 103425, 
        101819, 114570) 
Price<- c(11.325489, 3.629273, 11.411762, 10.857959, 
      5.535661, 9.804504, 11.461083, 11.880766, 
      7.048374, 3.652103, 10.220966, 5.950183, 
      10.123631, 5.396471, 6.312868, 6.267350, 
      5.458517, 10.353681, 4.001779, 10.616924, 
      7.936855, 5.182608, 5.963908, 10.938264, 
      11.069152, 9.675570) 
Opps<- c(5144351, 6038044, 2354341, 4578272, 
     7197544, 474510, 1045528, 181471, 
     1071631, 801038, 928563, 477870, 
     590497, 849537, 410179, 432703, 
     1983993, 330478, 939806, 191824, 
     283107, 575004, 256846, 249530, 
     142318, 2036363) 
df<-data.frame(parent, child, Type, Size, 
       Revenue, NumberofSales, Price, Opps)

這是什麼樣子：

df 

    parent child Type Size Revenue NumberofSales  Price Opps 
1  a  A desktop MEDIUM 22138.16  1954720 11.325489 5144351 
2  b  B desktop MEDIUM 18617.94  5129937 3.629273 6038044 
3  c  C desktop LARGE 12394.36  1086104 11.411762 2354341 
4  d  D desktop LARGE 10535.76  970326 10.857959 4578272 
5  e  E desktop SMALL 8901.41  1608012 5.535661 7197544 
6  f  F desktop MEDIUM 7320.17  746613 9.804504 474510 
7  g  G desktop LARGE 3821.40  333424 11.461083 1045528 
8  h  H desktop SMALL 2811.50  236643 11.880766 181471 
9  i  I desktop MEDIUM 2483.10  352294 7.048374 1071631 
10  j  J desktop SMALL 2145.76  587541 3.652103 801038 
11  k  K desktop LARGE 2138.41  209218 10.220966 928563 
12  l  L desktop LARGE 2037.67  342455 5.950183 477870 
13  m  M desktop SMALL 1950.52  192670 10.123631 590497 
14  n  N desktop SMALL 1837.93  340580 5.396471 849537 
15  o  O desktop LARGE 1737.68  275260 6.312868 410179 
16  p  P desktop LARGE 1554.61  248049 6.267350 432703 
17  q  Q desktop MEDIUM 1374.40  251790 5.458517 1983993 
18  r  R desktop SMALL 1334.02  128845 10.353681 330478 
19  s  S desktop SMALL 1214.60  303515 4.001779 939806 
20  t  T desktop MEDIUM 1191.41  112218 10.616924 191824 
21  u  U desktop LARGE 1189.56  149878 7.936855 283107 
22  v  V desktop MEDIUM 1174.55  226633 5.182608 575004 
23  w  W desktop SMALL 1162.80  194973 5.963908 256846 
24  x  X desktop MEDIUM 1131.29  103425 10.938264 249530 
25  y  Y desktop LARGE 1127.05  101819 11.069152 142318 
26  z  Z desktop MEDIUM 1108.53  114570 9.675570 2036363

我想創建一個數據幀是節目Price BY Size和Type的分佈以及這些Price範圍的所有適當度量。我希望最終的數據框看起來像這樣。 （我沒有對度量值進行聚合，因爲它現在做的時間太長，這就是爲什麼它們現在都是相同的，但最終答案應該有所有不同的值）

 Type Size  Price Range SUM_Opps SUM_NumberofSales SUM_Revenue 
1 desktop LARGE  $3-$3.99 9,143,587   2,531,983 $8,453.93 
1 desktop LARGE  $4-$4.99 9,143,587   2,531,983 $8,453.93 
1 desktop LARGE  $5-$5.99 9,143,587   2,531,983 $8,453.93 
1 desktop LARGE  $6-$6.99 9,143,587   2,531,983 $8,453.93 
1 desktop LARGE  $7-$7.99 9,143,587   2,531,983 $8,453.93 
1 desktop LARGE  $8-$8.99 9,143,587   2,531,983 $8,453.93 
1 desktop LARGE  $9-$9.99 9,143,587   2,531,983 $8,453.93 
1 desktop LARGE  $10-$10.99 9,143,587   2,531,983 $8,453.93 
1 desktop LARGE  $11-$11.99 9,143,587   2,531,983 $8,453.93 
1 desktop LARGE  $12-$12.99 9,143,587   2,531,983 $8,453.93 
1 desktop LARGE  $13-Greater 9,143,587   2,531,983 $8,453.93 
1 desktop MEDIUM  $3-$3.99 9,143,587   2,531,983 $8,453.93 
1 desktop MEDIUM  $4-$4.99 9,143,587   2,531,983 $8,453.93 
1 desktop MEDIUM  $5-$5.99 9,143,587   2,531,983 $8,453.93 
1 desktop MEDIUM  $6-$6.99 9,143,587   2,531,983 $8,453.93 
1 desktop MEDIUM  $7-$7.99 9,143,587   2,531,983 $8,453.93 
1 desktop MEDIUM  $8-$8.99 9,143,587   2,531,983 $8,453.93 
1 desktop MEDIUM  $9-$9.99 9,143,587   2,531,983 $8,453.93 
1 desktop MEDIUM  $10-$10.99 9,143,587   2,531,983 $8,453.93 
1 desktop MEDIUM  $11-$11.99 9,143,587   2,531,983 $8,453.93 
1 desktop MEDIUM  $12-$12.99 9,143,587   2,531,983 $8,453.93 
1 desktop MEDIUM  $13-Greater 9,143,587   2,531,983 $8,453.93 
1 desktop SMALL  $3-$3.99 9,143,587   2,531,983 $8,453.93 
1 desktop SMALL  $4-$4.99 9,143,587   2,531,983 $8,453.93 
1 desktop SMALL  $5-$5.99 9,143,587   2,531,983 $8,453.93 
1 desktop SMALL  $6-$6.99 9,143,587   2,531,983 $8,453.93 
1 desktop SMALL  $7-$7.99 9,143,587   2,531,983 $8,453.93 
1 desktop SMALL  $8-$8.99 9,143,587   2,531,983 $8,453.93 
1 desktop SMALL  $9-$9.99 9,143,587   2,531,983 $8,453.93 
1 desktop SMALL  $10-$10.99 9,143,587   2,531,983 $8,453.93 
1 desktop SMALL  $11-$11.99 9,143,587   2,531,983 $8,453.93 
1 desktop SMALL  $12-$12.99 9,143,587   2,531,983 $8,453.93 
1 desktop SMALL  $13-Greater 9,143,587   2,531,983 $8,453.93

如何創建上述表格？上表顯示了OPPS,Number of Sales和Revenue BY Type,Size和Price Range的總和。

我明白如何使用dplyr來進行簡單的聚合，但困難的部分是做價格分配。

任何幫助將是偉大的，謝謝！

來源

2016-12-27 Nick Knauer

+1

你能告訴你已經嘗試了什麼票？ – krish

回答

2

你可以使用Hmisc::cut2()生成你的價格區間爲因素的水平：

library(Hmisc) 
library(dplyr) 

df$cut_Price <- cut2(df$Price, cuts = 4:13) 

df %>% group_by(cut_Price, Size, Type) %>% 
    summarise_at(c("Opps", "NumberofSales", "Revenue"),"sum") %>% 
    arrange(Size, cut_Price) %>% ungroup() %>% 
    mutate(cut_Price = gsub("(.*, \\d\\.)00", "\\199", cut_Price)) 

# A tibble: 16 × 6 
     cut_Price Size Type Opps NumberofSales Revenue 
      <chr> <fctr> <fctr> <dbl>   <dbl> <dbl> 
1 [ 5.00, 6.99) LARGE desktop 477870  342455 2037.67 
2 [ 6.00, 7.99) LARGE desktop 842882  523309 3292.29 
3 [ 7.00, 8.99) LARGE desktop 283107  149878 1189.56 
4 [10.00,11.00) LARGE desktop 5506835  1179544 12674.17 
5 [11.00,12.00) LARGE desktop 3542187  1521347 17342.81 
6 [ 3.63, 4.99) MEDIUM desktop 6038044  5129937 18617.94 
7 [ 5.00, 6.99) MEDIUM desktop 2558997  478423 2548.95 
8 [ 7.00, 8.99) MEDIUM desktop 1071631  352294 2483.10 
9 [ 9.00,10.00) MEDIUM desktop 2510873  861183 8428.70 
10 [10.00,11.00) MEDIUM desktop 441354  215643 2322.70 
11 [11.00,12.00) MEDIUM desktop 5144351  1954720 22138.16 
12 [ 3.63, 4.99) SMALL desktop 801038  587541 2145.76 
13 [ 4.00, 5.99) SMALL desktop 939806  303515 1214.60 
14 [ 5.00, 6.99) SMALL desktop 8303927  2143565 11902.14 
15 [10.00,11.00) SMALL desktop 920975  321515 3284.54 
16 [11.00,12.00) SMALL desktop 181471  236643 2811.50
如果要調整削減每0.5而不是1

，你可以這樣做，因爲它傳遞給矢量cut = ...被定義 「切點」：

df$cut_Price <- cut2(df$Price, cuts = seq(4,13,.5))

來源

2016-12-27 20:04:55 Nate

+1

如果您使用的是dplyr，那麼您最好使用'mutate'。 – alistaire

+0

@Nathan Day，是否有可能從相距1美元的距離創造降價並將截值降至13美元或更高？除此之外，這真是太棒了！ –

+1

'？cut2'表示左邊的端點是包含的，'['符號類似於<='。 ''''在右手端點指示排除，與'<'類似的想法。所以它們已經代表了不重疊的間隔符，但是我將在'gsub（...）'中編輯以調整端點 – Nate

0

這將增加的價格區間

library(dplyr) 
df %>% 
    mutate(price_bin=ifelse(Price>13, 13, floor(Price))) %>% 
    group_by(Type, Size, price_bin) %>% 
    summarise(sum_opps=sum(Opps), sum_sales=sum(NumberofSales), sum_revenue=sum(Revenue))

更新

不知道爲什麼有下來的時候它返回相同的結果接受的答案，而不需要額外的庫

 Type Size price_bin sum_opps sum_sales sum_revenue 
    <fctr> <fctr>  <dbl> <dbl>  <dbl>  <dbl> 
1 desktop LARGE   5 477870 342455  2037.67 
2 desktop LARGE   6 842882 523309  3292.29 
3 desktop LARGE   7 283107 149878  1189.56 
4 desktop LARGE  10 5506835 1179544  12674.17 
5 desktop LARGE  11 3542187 1521347  17342.81 
6 desktop MEDIUM   3 6038044 5129937  18617.94 
7 desktop MEDIUM   5 2558997 478423  2548.95 
8 desktop MEDIUM   7 1071631 352294  2483.10 
9 desktop MEDIUM   9 2510873 861183  8428.70 
10 desktop MEDIUM  10 441354 215643  2322.70 
11 desktop MEDIUM  11 5144351 1954720  22138.16 
12 desktop SMALL   3 801038 587541  2145.76 
13 desktop SMALL   4 939806 303515  1214.60 
14 desktop SMALL   5 8303927 2143565  11902.14 
15 desktop SMALL  10 920975 321515  3284.54 
16 desktop SMALL  11 181471 236643  2811.50

來源

2016-12-27 20:06:09 manotheshark

+1

你檢查'價格'列已被劃分如何？ –

+0

@ joel.Wilson我錯過了'價格'切入點，但是這可以通過使用'floor'來完成，因爲它們是以美元爲單位的 – manotheshark

+0

爲什麼這個表格仍然會以正確的輸出表決呢？ – manotheshark

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