R-计算累积比例销售产品数量

Question

我有一个具有PPG销售的数据框。

数据是

    df= structure(list(Ppg = structure(c(1L, 1L, 1L, 1L, 2L, 2L, 2L), .Label = c("p1", 
"p2"), class = "factor"), product = structure(c(1L, 2L, 3L, 4L, 
1L, 2L, 3L), .Label = c("A", "B", "C", "D"), class = "factor"), 
    sales = c(50, 40, 30, 80, 100, 70, 30)), .Names = c("Ppg", 
"product", "sales"), row.names = c(NA, -7L), class = "data.frame")


  > df
  Ppg product sales
1  p1       A    50
2  p1       B    40
3  p1       C    30
4  p1       D    80
5  p2       A   100
6  p2       B    70
7  p2       C    30

我使用dplyr

检索了累积总和

df %>% group_by(Ppg) %>% summarise(sale = sum(sales) %>% mutate(c1 = cumsum(sales))
          Ppg product sales    c1
  <fctr>  <fctr> <dbl> <dbl>
1     p1       A    50    50
2     p1       B    40    90
3     p1       C    30   120
4     p1       D    80   200
5     p2       A   100   100
6     p2       B    70   170
7     p2       C    30   200

有什么办法

i(计算销售比例(基于库姆(

ii(有多少种不同的产品对销售的某些百分比造成了贡献。

PPG P1的Exmple，2种不同的产品(A＆amp; b Combind提供75％的销售(

所以最后的东西是理想的

ppg Number_Products_towards_75%
p1   2
p2   1

Answer 1

假设您使用该产品当前正在为您回答的顺序，因为重新排序行会给您带来不同的结果(：

对于1，您可以通过额外的突变获得结果。只需将累积总和除以该组的所有销售量：

df %>% 
  group_by(Ppg) %>% 
  mutate(c1 = cumsum(sales)) %>%
  mutate(percent = c1 / sum(sales))

让你：

# A tibble: 7 x 5
# Groups: Ppg [2]
  Ppg    product sales    c1 percent
  <fctr> <fctr>  <dbl> <dbl>   <dbl>
1 p1     A        50.0  50.0   0.250
2 p1     B        40.0  90.0   0.450
3 p1     C        30.0 120     0.600
4 p1     D        80.0 200     1.00 
5 p2     A       100   100     0.500
6 p2     B        70.0 170     0.850
7 p2     C        30.0 200     1.00 

对于2，您可以使用突变型添加一列，以使该产品低于阈值并总结以计数低于阈值的产品(然后在计数中添加一个产品，因为又有一个可以使您越过它(。

threshold <- 0.5
df %>% 
  group_by(Ppg) %>% 
  mutate(c1 = cumsum(sales)) %>%
  mutate(percent = c1 / sum(sales)) %>%
  mutate(isbelowthreshold = percent < threshold) %>% # add a column for if it's below the threshold
  summarize(count = sum(isbelowthreshold) + 1) # we need to add one since one extra product will put you over the threshold

让你：

# A tibble: 2 x 2
  Ppg    count
  <fctr> <dbl>
1 p1      3.00
2 p2      1.00

，但这再次取决于产品的顺序。考虑首先从最高值订购它们？像

df %>% 
    group_by(Ppg) %>%
    arrange(Ppg, desc(sales))