如何計算R中的累積列和一個特定列？

我有按年份和副產品劃分的銷售資料，可以這樣說：

Year <- c(2010,2010,2010,2010,2010,2011,2011,2011,2011,2011,2012,2012,2012,2012,2012)
Model <- c("a","b","c","d","e","a","b","c","d","e","a","b","c","d","e")
Sale <- c("30","45","23","33","24","11","56","19","45","56","33","32","89","33","12")
df <- data.frame(Year, Model, Sale)

首先，我需要計算“份額”列，它代表每年每種產品的份額。

在我這樣計算累積份額之后：

第三步需要識別出去年（本例中為2012年）累計銷售額達到70%的產品，并在整個資料框中只保留這些產品 添加一個排名列（基于去年）并匯總所有其余產品為“其他”類別。所以最終的資料框應該是這樣的：

uj5u.com熱心網友回復：

這是一項相當復雜的資料整理任務，但可以使用以下方法實作dplyr：

library(dplyr)

df %>%
  mutate(Sale = as.numeric(Sale)) %>%
  group_by(Year) %>%
  mutate(Share = 100 * Sale/ sum(Sale),
         Year_order = order(order(-Share))) %>%
  arrange(Year, Year_order, by_group = TRUE) %>%
  mutate(Cumm.Share = cumsum(Share)) %>%
  ungroup() %>%
  mutate(below_70 = Model %in% Model[Year == max(Year) & Cumm.Share < 70]) %>%
  mutate(Model = ifelse(below_70, Model, 'Other')) %>%
  group_by(Year, Model) %>%
  summarize(Sale = sum(Sale), Share = sum(Share), .groups = 'keep') %>%
  group_by(Year) %>%
  mutate(pseudoShare = ifelse(Model == 'Other', 0, Share)) %>%
  arrange(Year, -pseudoShare, by_group = TRUE) %>%
  ungroup() %>%
  mutate(Rank = match(Model, Model[Year == max(Year)])) %>%
  select(-pseudoShare)
#> # A tibble: 9 x 5
#>    Year Model  Sale Share  Rank
#>   <dbl> <chr> <dbl> <dbl> <int>
#> 1  2010 a        30 19.4      2
#> 2  2010 c        23 14.8      1
#> 3  2010 Other   102 65.8      3
#> 4  2011 c        19 10.2      1
#> 5  2011 a        11  5.88     2
#> 6  2011 Other   157 84.0      3
#> 7  2012 c        89 44.7      1
#> 8  2012 a        33 16.6      2
#> 9  2012 Other    77 38.7      3

請注意，在輸出中，此代碼保留了組a和c，而不是c和d，就像您預期的輸出一樣。這是因為a和d在最后一年（16.6）具有相同的值，因此可以選擇其中任何一個。

^{由reprex 包于 2022-04-21 創建(v2.0.1)}

uj5u.com熱心網友回復：

Year <- c(2010,2010,2010,2010,2010,2011,2011,2011,2011,2011,2012,2012,2012,2012,2012)
Model <- c("a","b","c","d","e","a","b","c","d","e","a","b","c","d","e")
Sale <- c("30","45","23","33","24","11","56","19","45","56","33","32","89","33","12")
df <- data.frame(Year, Model, Sale, stringsAsFactors=F)


years <- unique(df$Year)

shares <- c()
cumshares <- c()

for (year in years){
  extract <- df[df$Year == year, ]
  sale <- as.numeric(extract$Sale)
  share <- 100*sale/sum(sale)
  shares <- append(shares, share)
  cumshare <- rev(cumsum(rev(share)))
  cumshares <- append(cumshares, cumshare)
  
}

df$Share <- shares
df$Cumm.Share <- cumshares

df

給

> df
   Year Model Sale     Share Cumm.Share
1  2010     a   30 19.354839 100.000000
2  2010     b   45 29.032258  80.645161
3  2010     c   23 14.838710  51.612903
4  2010     d   33 21.290323  36.774194
5  2010     e   24 15.483871  15.483871
6  2011     a   11  5.882353 100.000000
7  2011     b   56 29.946524  94.117647
8  2011     c   19 10.160428  64.171123
9  2011     d   45 24.064171  54.010695
10 2011     e   56 29.946524  29.946524
11 2012     a   33 16.582915 100.000000
12 2012     b   32 16.080402  83.417085
13 2012     c   89 44.723618  67.336683
14 2012     d   33 16.582915  22.613065
15 2012     e   12  6.030151   6.030151

我不明白您所說的第 3 步是什么意思，您如何決定保留哪些產品？

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