我的数据框如下：
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)
这就是它的样子：
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
我想创建一个数据框，显示Price BY Size和Type的分布，以及这些Price范围的所有适当指标。我希望最终的数据框看起来像这样。 （我没有对度量值进行聚合，因为它占用了我目前正在进行的方式太长时间，这就是为什么它们现在都是一样的，但最终的答案应该具有所有不同的值）
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 $8,453.93
如何创建上表？上表显示了OPPS，Number of Sales和Revenue BY Type，Size和Price Range的总和。
我理解如何使用dplyr进行简单聚合，但困难的部分是进行价格分配。
任何帮助都会很棒，谢谢！
答案 0 :(得分：2)
您可以使用Hmisc::cut2()生成价格区间作为因素的级别：
library(Hmisc)
library(dplyr)
df$cut_Price <- cut2(df$Price, cuts = 4:13)
df %>% group_by(cut_Price, Size, Type) %>%
summarise_at(c("Opps", "NumberofSales", "Revenue"),"sum") %>%
arrange(Size, cut_Price) %>% ungroup() %>%
mutate(cut_Price = gsub("(.*, \\d\\.)00", "\\199", cut_Price))
# A tibble: 16 × 6
cut_Price Size Type Opps NumberofSales Revenue
<chr> <fctr> <fctr> <dbl> <dbl> <dbl>
1 [ 5.00, 6.99) LARGE desktop 477870 342455 2037.67
2 [ 6.00, 7.99) LARGE desktop 842882 523309 3292.29
3 [ 7.00, 8.99) LARGE desktop 283107 149878 1189.56
4 [10.00,11.00) LARGE desktop 5506835 1179544 12674.17
5 [11.00,12.00) LARGE desktop 3542187 1521347 17342.81
6 [ 3.63, 4.99) MEDIUM desktop 6038044 5129937 18617.94
7 [ 5.00, 6.99) MEDIUM desktop 2558997 478423 2548.95
8 [ 7.00, 8.99) MEDIUM desktop 1071631 352294 2483.10
9 [ 9.00,10.00) MEDIUM desktop 2510873 861183 8428.70
10 [10.00,11.00) MEDIUM desktop 441354 215643 2322.70
11 [11.00,12.00) MEDIUM desktop 5144351 1954720 22138.16
12 [ 3.63, 4.99) SMALL desktop 801038 587541 2145.76
13 [ 4.00, 5.99) SMALL desktop 939806 303515 1214.60
14 [ 5.00, 6.99) SMALL desktop 8303927 2143565 11902.14
15 [10.00,11.00) SMALL desktop 920975 321515 3284.54
16 [11.00,12.00) SMALL desktop 181471 236643 2811.50
如果你想将切割调整到每0.5而不是1，你可以这样做，因为传递给cut = ...的向量定义了“切割点”：
df$cut_Price <- cut2(df$Price, cuts = seq(4,13,.5))
答案 1 :(得分：0)
这将添加价格箱
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))
<强>更新
在不需要额外的库
的情况下，如果返回的结果与接受的答案相同，则不确定为什么会有投票结果 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