Question

我有一个包含多年销售额的数据集。在此处示例：

yr_2008 <- data.frame(agent = c("agent1", "agent4", "agent1", "agent1", "agent1", "agent4"), sales = c(100, 200, 300, 130, 200, 400), year = 2008)
yr_2009 <- data.frame(agent = c("agent1", "agent3", "agent4", "agent1", "agent3", "agent4", "agent1", "agent3", "agent4"), sales = c(200, 500, 200, 200, 100, 100, 200, 300, 200), year = 2009)
yr_2010 <- data.frame(agent = c("agent1", "agent4", "agent2", "agent2", "agent2", "agent4"), sales = c(130, 300, 100, 200, 100, 200), year = 2010)
sales <- rbind(yr_2008, yr_2009, yr_2010)

每年为每个人生成摘要的适当方法是什么？例如，我想每年查看一个人进行销售的次数以及销售量。如果一个人当年不在那里，那么就有NA。例如，2008年，我希望将其作为输出

 sales_output <- data.frame(agent = c("agent1", "agent2", "agent3", "agent4"),
                       yr08_transaction = c(3, NA, NA, 2),
                       yr08_sales = c(730, NA, NA, 600))

我还希望将所有这些信息仅包含在一个表中，如下所示

扩展：

sales_output <- data.frame(agent = c("agent1", "agent2", "agent3", "agent4"),
                       yr08_transaction = c(3, NA, NA, 2),
                       yr08_sales = c(730, NA, NA, 600),
                       yr09_transaction = c(3, 0, 3, 3), 
                       yr09_sales = c(600, 0, 900, 500),
                       yr10_transaction = c(1, 3, 0, 2),
                       yr10_sales = c(130, 400, 0, 500))

sales_output
agent yr08_transaction yr08_sales yr09_transaction yr09_sales yr10_transaction yr10_sales
1 agent1                3        730                3        600                1        130
2 agent2               NA         NA                0          0                3        400
3 agent3               NA         NA                3        900                0          0
4 agent4                2        600                3        500                2        500

谢谢！

Answer 1

这是一个dplyr工作流程。如果您获取此数据并按年份对其进行分组代理商，您可以计算每个代理商每年的销售总额和参赛人数。要将其设置为宽格式，请使用gather首先使其更长，将销售和交易同时放入单个列，unite包含度量的年份，因此您有“2009_sales”等条目，然后spread让它回到广泛的位置。 spread也会使用NA填充缺失值。

library(tidyverse)

yr_2008 <- data.frame(agent = c("agent1", "agent4", "agent1", "agent1", "agent1", "agent4"), sales = c(100, 200, 300, 130, 200, 400), year = 2008)
yr_2009 <- data.frame(agent = c("agent1", "agent3", "agent4", "agent1", "agent3", "agent4", "agent1", "agent3", "agent4"), sales = c(200, 500, 200, 200, 100, 100, 200, 300, 200), year = 2009)
yr_2010 <- data.frame(agent = c("agent1", "agent4", "agent2", "agent2", "agent2", "agent4"), sales = c(130, 300, 100, 200, 100, 200), year = 2010)
sales <- rbind(yr_2008, yr_2009, yr_2010)

sales_summary <- sales %>%
    group_by(year, agent) %>%
    summarise(sales = sum(sales), transactions = n()) %>%
    gather(key = type, value = value, sales, transactions) %>%
    unite("yr", year, type) %>%
    spread(key = yr, value = value, sep = "")

sales_summary
#> # A tibble: 4 x 7
#>   agent  yr2008_sales yr2008_transactions yr2009_sales yr2009_transactions
#>   <fct>         <dbl>               <dbl>        <dbl>               <dbl>
#> 1 agent1          730                   4          600                   3
#> 2 agent4          600                   2          500                   3
#> 3 agent3           NA                  NA          900                   3
#> 4 agent2           NA                  NA           NA                  NA
#> # ... with 2 more variables: yr2010_sales <dbl>, yr2010_transactions <dbl>

由reprex package（v0.2.0）创建于2018-05-13。

Answer 2

以下是data.table的选项。总结以获得观察数量和sum'销售'按“代理人”和“年份”以及dcast分组为“广泛”格式

library(data.table)
dcast(setDT(sales)[, .(transaction = .N, Sumsales = sum(sales)), by = .(agent, year)],
     agent ~ substr(year, 3, 4), value.var = c('transaction', 'Sumsales'))

Answer 3

将dplyr与right_join

一起使用

sales$agent <- as.character(sales$agent)

sales %>% filter(year==2008) %>% group_by(agent) %>% 
  summarise(yr08_transaction=n(),yr08_sales=sum(sales)) %>% 
  right_join(sales[!duplicated(sales$agent),c('agent','year')],by="agent") %>%
  arrange(agent) %>% select(-year)

# A tibble: 4 x 3
agent yr08_transaction yr08_sales
<chr>            <int>      <dbl>
1 agent1                4        730
2 agent2               NA         NA
3 agent3               NA         NA
4 agent4                2        600

R重塑数据和汇总信息

3 个答案: