R-rbind逻辑

我有这个数据框：

source_data <- data.frame(
    "date" = c("2018-01-01", "2018-01-01", "2018-02-01", "2018-02-01"), 
    "nr" = c(0, 1, 0, 1),
    "marketing_fees" = c(500, 600, 800, 900),
    "services_paid" = c(40, 50, 10, 30),
    stringsAsFactors = F)

源数据%
mutate（source=“marketing”），
源\u数据%>%
过滤器（日期==“2018-01-01”）%>%
选择（日期、nr、收入=已支付的服务）%>%
mutate（source=“services”），
源\u数据%>%
过滤器（日期==“2018-02-01”）%>%
选择（日期、nr、收入=营销费用）%>%
mutate（source=“marketing”），
源\u数据%>%
过滤器（日期==“2018-02-01”）%>%
选择（日期、nr、收入=已支付的服务）%>%
变异（source=“services”）
)

---

上面的代码不仅仅是有很多重复的部分，我不能再这样使用它了，因为我的数据框架有大约50列和很多数据。如果没有这么多重复的代码，您如何实现结果数据帧？

我们可以使用

聚集

将“宽”改为“长”，然后

分离列名以仅返回前缀部分

library(tidyverse)
source_data %>% 
    gather(source, income, marketing_fees:services_paid) %>% 
    separate(source, into = c('source', 'extra')) %>%
    select(-extra) %>% 
    arrange(date, nr)
#        date nr    source income
#1 2018-01-01  0 marketing    500
#2 2018-01-01  0  services     40
#3 2018-01-01  1 marketing    600
#4 2018-01-01  1  services     50
#5 2018-02-01  0 marketing    800
#6 2018-02-01  0  services     10
#7 2018-02-01  1 marketing    900
#8 2018-02-01  1  services     30

库（data.table）
图书馆（magrittr）
结果2就我所见，这是重塑和一些基本的文本处理。我将发布一个答案作为证据。请注意，这与前面提到的重新打开的逻辑相似，我看不出有任何区别。规则应该与每个人相似，而不是针对其他人
result <- rbind(
  source_data %>% 
    filter(date == "2018-01-01") %>% 
    select(date, nr, income = marketing_fees) %>% 
    mutate(source = "marketing"),

  source_data %>% 
    filter(date == "2018-01-01") %>% 
    select(date, nr, income = services_paid) %>% 
    mutate(source = "services"),

  source_data %>% 
    filter(date == "2018-02-01") %>% 
    select(date, nr, income = marketing_fees) %>% 
    mutate(source = "marketing"),

  source_data %>% 
    filter(date == "2018-02-01") %>% 
    select(date, nr, income = services_paid) %>% 
    mutate(source = "services")
)

library(tidyverse)
source_data %>% 
    gather(source, income, marketing_fees:services_paid) %>% 
    separate(source, into = c('source', 'extra')) %>%
    select(-extra) %>% 
    arrange(date, nr)
#        date nr    source income
#1 2018-01-01  0 marketing    500
#2 2018-01-01  0  services     40
#3 2018-01-01  1 marketing    600
#4 2018-01-01  1  services     50
#5 2018-02-01  0 marketing    800
#6 2018-02-01  0  services     10
#7 2018-02-01  1 marketing    900
#8 2018-02-01  1  services     30

library(data.table)
library(magrittr)
result2 <- melt(
  setDT(source_data), 
  id.vars = c("date", "nr"), 
  value.name = "income", 
  variable.name = "source"
)[, source := sub("_.*", "", source)][order(date, nr)]°

         date nr    source income
1: 2018-01-01  0 marketing    500
2: 2018-01-01  0  services     40
3: 2018-01-01  1 marketing    600
4: 2018-01-01  1  services     50
5: 2018-02-01  0 marketing    800
6: 2018-02-01  0  services     10
7: 2018-02-01  1 marketing    900
8: 2018-02-01  1  services     30