library(tidyverse)
library(lubridate)

df %>%
   group_by(Type, Date)%>%
   summarise(Number_Items = n(),
             Year = year(mdy(Date[1])),
             Total_Cost = sum(parse_number(Cost)),
             .groups = 'drop')

# A tibble: 8 × 5
  Type        Date       Number_Items  Year Total_Cost
  <chr>       <chr>             <int> <dbl>      <dbl>
1 Conditioner 03/15/2020            1  2020         17
2 Conditioner 05/16/2022            1  2022         19
3 Shampoo     01/31/2022            2  2022         25
4 Shampoo     02/22/2019            1  2019         15
5 Soap        01/06/2022            1  2022          2
6 Soap        10/10/2022            1  2022          5
7 Soap        12/31/2019            1  2019          3
8 Soap.       01/31/2021            1  2021          5

你必须按类型和年份对你的数据进行分组，然后计算物品的数量和总成本，这里有一个例子供你适应。

library(dplyr)
library(lubridate)

your_data_frame %>% 
  group_by(type, year = year(dmy(Date))) %>% 
  summarise(
    number_of_items = n(),
    total_cost = sum(cost,na.rm = TRUE)
  )

这一个与@onyambu的解决方案非常相似，但在分组上有所不同：
第一个