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)
结果应如下所示
result <- data.frame(
"date" = c("2018-01-01", "2018-01-01", "2018-01-01", "2018-01-01", "2018-02-01", "2018-02-01", "2018-02-01", "2018-02-01"),
"nr" = c(0, 0, 1, 1, 0, 0, 1, 1),
"income" = c(500, 40, 600, 50, 800, 10, 900, 30),
"source" = c("marketing", "services", "marketing", "services", "marketing", "services", "marketing", "services"),
stringsAsFactors = F)
这是我唯一的方法
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")
)
上面的代码不仅丑陋,而且有很多重复的部分,我不能再以这种方式使用它了,因为我的数据框有大约50列和很多数据。如果没有那么多重复的代码,您将如何获得结果数据帧?
答案 0 :(得分:1)
我们可以使用gather
将“宽”改成“长”,然后使用separate
列名仅返回前缀部分
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
答案 1 :(得分:0)
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