如何在 R 中操作（聚合）数据？

我有一个数据集，如下所示：

df <- tribble(
  ~id,  ~price, ~number_of_book,        
  "1",    10,         3,        
  "1",     5,         1,         
  "2",     7,         4,
  "2",     6,         2, 
  "2",     3,         4,
  "3",     4,         1,
  "4",     5,         1,
  "4",     6,         1,
  "5",     1,         2,
  "5",     9,         3,
)

正如您在数据集中看到的，如果 id 为“1”，则有 3 本书每本书售价 10 美元，而 1 本书每本书售价 5 美元。基本上，我想查看每个价格区间的图书数量份额 (%)。这是我想要的数据集：

df <- tribble(
  ~id,    ~less_than_three,   ~three-five,  ~five-six, ~more_than_six,     
  "1",          "0%",              "25%",     "0%",         "75%",
  "2",          "0%",              "40%",     "20%",        "40%",
  "3",          "0%",              "100%",    "0%",         "0%",  
  "4",          "0%",              "50%",     "50%",        "0%",
  "5",          "40%",             "0%",      "0%",         "60%",
)

现在，我首先对价格进行聚类。为此，我运行以下代码：

out <- cut(df$price, breaks = c(0, 3, 5, 6, 10),
           labels = c("<3","3-5","5-6", ">6")) 

out = table(out) / sum(table(out)) 

但不幸的是，由于缺乏编码知识，我无法更进一步。你能帮我获取所需的数据吗？

我们可以用cut获取间隔，然后使用tidyr将数据转换为宽格式，最后使用janitor添加百分比。

library(dplyr)
library(tidyr)
library(janitor)

df %>% 
  mutate(interval = cut(price, c(0,3,5,6,Inf))) %>% 
  select(-price) %>% 
  pivot_wider(names_from = interval, values_from = number_of_book) %>% 
  adorn_percentages()

#>  id (6,Inf] (3,5] (5,6] (0,3]
#>   1    0.75  0.25    NA    NA
#>   2    0.40    NA   0.2   0.4
#>   3      NA  1.00    NA    NA
#>   4      NA  0.50   0.5    NA
#>   5    0.60    NA    NA   0.4