仅分离变量名后转置
Transpose after separating only the variable name
我是 R 的新手，但我沉迷于精通！我正在做一个工作项目，我完全被难住了！非常感谢任何帮助！
我需要转换这个数据框...
Brand UK__Sales__YA UK__Sales__MAT CN__Sales__YA CN__Sales__MAT
1 Snickers 100 110 90 95
2 Twix 50 60 30 35
3 Skittles 75 80 105 130
...到这个
Brand Country Year Sales
1 Snickers UK YA 100
2 Snickers UK MAT 110
3 Snickers CN YA 90
4 Snickers CN MAT 95
5 Twix UK YA 50
6 Twix UK MAT 60
7 Twix CN YA 30
8 Twix CN MAT 35
9 Skittles UK YA 75
10 Skittles UK MAT 80
11 Skittles CN YA 105
12 Skittles CN MAT 130
如您所知，我需要拆分销售变量的第一部分和最后一部分，并将它们创建为单独的数据堆栈。我的数据集中还有其他国家/地区和其他指标，但我认为如果您能帮我解决这个问题，那么我就可以完成它。谢谢！！ :-)
查看 tidyr package -- in fact, all of the packages in the tidyverse 对此类数据处理工作有帮助：
library(tidyr)
library(dplyr)
df %>%
gather(key, Sales, -Brand) %>%
separate(key, c("Country", "delete", "Year"), sep = "__") %>%
select(-delete) %>%
arrange(Brand)
# Brand Country Year Sales
# 1 Skittles UK YA 75
# 2 Skittles UK MAT 80
# 3 Skittles CN YA 105
# 4 Skittles CN MAT 130
# 5 Snickers UK YA 100
# 6 Snickers UK MAT 110
# 7 Snickers CN YA 90
# 8 Snickers CN MAT 95
# 9 Twix UK YA 50
# 10 Twix UK MAT 60
# 11 Twix CN YA 30
# 12 Twix CN MAT 35
要了解发生了什么，运行 每个管道 %>% 单独声明：（例如，查看 df %>% gather(key, Sales, -Brand) 之后的输出以了解其作用）。接下来 运行 通过 separate 管道进行转换。
这是 tidyverse 的一个选项。我们将 gather 转化为 'long' 格式然后 extract 将 'Var' 列转化为 'Country' 和 'Year'
library(tidyr)
library(dplyr)
gather(df1, Var, Sales, -Brand) %>%
extract(Var, into = c("Country", "Year"), "(\w+)__\w+__(\w+)")
# Brand Country Year Sales
#1 Snickers UK YA 100
#2 Twix UK YA 50
#3 Skittles UK YA 75
#4 Snickers UK MAT 110
#5 Twix UK MAT 60
#6 Skittles UK MAT 80
#7 Snickers CN YA 90
#8 Twix CN YA 30
#9 Skittles CN YA 105
#10 Snickers CN MAT 95
#11 Twix CN MAT 35
#12 Skittles CN MAT 130
与 data.table 对应的选项是
library(data.table)
melt(setDT(df1), id.var = "Brand", value.names = "Sales")[,
c("Country", "Year") := tstrsplit(variable, "__")[-2]][, variable := NULL][]
1) dplyr/tidyr 使用最后注释中可重复显示的数据，将数据框从宽到长的形式收集起来，然后分离出新专栏。使用 Value 列作为其中的值，将新的 Variable 列散布到 Price 和 Sales 中，然后进行排序。如果顺序无关紧要，最后一行代码可以省略。
library(dplyr)
library(tidyr)
DF %>%
gather(new, Value, -Brand) %>%
separate(new, c("Country", "Variable", "Year"), sep = "__") %>%
spread(Variable, Value) %>%
arrange(Brand, desc(Country), desc(Year))
给予：
Brand Country Year Sales
1 Skittles UK YA 75
2 Skittles UK MAT 80
3 Skittles CN YA 105
4 Skittles CN MAT 130
5 Snickers UK YA 100
6 Snickers UK MAT 110
7 Snickers CN YA 90
8 Snickers CN MAT 95
9 Twix UK YA 50
10 Twix UK MAT 60
11 Twix CN YA 30
12 Twix CN MAT 35
请注意，以上内容也适用 DF2 也在下面的注释中定义。
1a) 这个稍微短一点的替代方案也可以，但只适用于 DF，不适用于 DF2。同样，如果顺序无关紧要，可以省略 arrange 行。
DF %>%
gather(new, Sales, -Brand) %>%
separate(new, c("Country", "Year"), sep = "__Sales__") %>%
arrange(Brand, desc(Country), desc(Year))
2) 此替代方案不涉及使用 reshape 将宽格式重塑为长格式的包。如果行名和顺序无关紧要，则可以省略从 rownames(long) <- NULL 语句开始的所有内容。此代码也适用于 DF2.
varying <- split(names(DF)[-1], sub(".*__(.*)__.*", "\1", names(DF)[-1]))
long <- reshape(DF, dir = "long", idvar = "Brand", varying = varying,
v.names = names(varying))
out <- transform(long, Country = sub("__.*", "", time), Year = sub(".*__", "", time),
time = NULL)
rownames(out) <- NULL
o <- with(out, order(Brand, -xtfrm(Country), -xtfrm(Year)))
out <- out[o, ]
out
给予：
Brand Sales Country Year
3 Skittles 75 UK YA
6 Skittles 80 UK MAT
9 Skittles 105 CN YA
12 Skittles 130 CN MAT
1 Snickers 100 UK YA
4 Snickers 110 UK MAT
7 Snickers 90 CN YA
10 Snickers 95 CN MAT
2 Twix 50 UK YA
5 Twix 60 UK MAT
8 Twix 30 CN YA
11 Twix 35 CN MAT
备注
Lines <- " Brand UK__Sales__YA UK__Sales__MAT CN__Sales__YA CN__Sales__MAT
1 Snickers 100 110 90 95
2 Twix 50 60 30 35
3 Skittles 75 80 105 130"
DF <- read.table(text = Lines)
# same as DF but with additional columns for Price
DF2 <- cbind(DF, setNames(10 * DF[2:5], sub("Sales", "Price", names(DF)[2:5])))
这是一个使用包 reshape2 的解决方案。
new <- reshape2::melt(data, id.vars = "Brand")
new$Country <- sub("(^[^_]*)_.*$", "\1", new$variable)
new$Year <- sub("^.*_([[:alpha:]]*$)", "\1", new$variable)
new <- new[, c(1, 4, 5, 3)]
names(new)[4] <- "Sales"
head(new)
# Brand Country Year Sales
#1 Snickers UK YA 100
#2 Twix UK YA 50
#3 Skittles UK YA 75
#4 Snickers UK MAT 110
#5 Twix UK MAT 60
#6 Skittles UK MAT 80
数据
data <-
structure(list(Brand = c("Snickers", "Twix", "Skittles"), UK__Sales__YA = c(100L,
50L, 75L), UK__Sales__MAT = c(110L, 60L, 80L), CN__Sales__YA = c(90L,
30L, 105L), CN__Sales__MAT = c(95L, 35L, 130L)), .Names = c("Brand",
"UK__Sales__YA", "UK__Sales__MAT", "CN__Sales__YA", "CN__Sales__MAT"
), class = "data.frame", row.names = c("1", "2", "3"))
我是 R 的新手，但我沉迷于精通！我正在做一个工作项目，我完全被难住了！非常感谢任何帮助！
我需要转换这个数据框...
Brand UK__Sales__YA UK__Sales__MAT CN__Sales__YA CN__Sales__MAT
1 Snickers 100 110 90 95
2 Twix 50 60 30 35
3 Skittles 75 80 105 130
...到这个
Brand Country Year Sales
1 Snickers UK YA 100
2 Snickers UK MAT 110
3 Snickers CN YA 90
4 Snickers CN MAT 95
5 Twix UK YA 50
6 Twix UK MAT 60
7 Twix CN YA 30
8 Twix CN MAT 35
9 Skittles UK YA 75
10 Skittles UK MAT 80
11 Skittles CN YA 105
12 Skittles CN MAT 130
如您所知，我需要拆分销售变量的第一部分和最后一部分，并将它们创建为单独的数据堆栈。我的数据集中还有其他国家/地区和其他指标，但我认为如果您能帮我解决这个问题，那么我就可以完成它。谢谢！！ :-)
查看 tidyr package -- in fact, all of the packages in the tidyverse 对此类数据处理工作有帮助：
library(tidyr)
library(dplyr)
df %>%
gather(key, Sales, -Brand) %>%
separate(key, c("Country", "delete", "Year"), sep = "__") %>%
select(-delete) %>%
arrange(Brand)
# Brand Country Year Sales
# 1 Skittles UK YA 75
# 2 Skittles UK MAT 80
# 3 Skittles CN YA 105
# 4 Skittles CN MAT 130
# 5 Snickers UK YA 100
# 6 Snickers UK MAT 110
# 7 Snickers CN YA 90
# 8 Snickers CN MAT 95
# 9 Twix UK YA 50
# 10 Twix UK MAT 60
# 11 Twix CN YA 30
# 12 Twix CN MAT 35
要了解发生了什么，运行 每个管道 %>% 单独声明：（例如，查看 df %>% gather(key, Sales, -Brand) 之后的输出以了解其作用）。接下来 运行 通过 separate 管道进行转换。
这是 tidyverse 的一个选项。我们将 gather 转化为 'long' 格式然后 extract 将 'Var' 列转化为 'Country' 和 'Year'
library(tidyr)
library(dplyr)
gather(df1, Var, Sales, -Brand) %>%
extract(Var, into = c("Country", "Year"), "(\w+)__\w+__(\w+)")
# Brand Country Year Sales
#1 Snickers UK YA 100
#2 Twix UK YA 50
#3 Skittles UK YA 75
#4 Snickers UK MAT 110
#5 Twix UK MAT 60
#6 Skittles UK MAT 80
#7 Snickers CN YA 90
#8 Twix CN YA 30
#9 Skittles CN YA 105
#10 Snickers CN MAT 95
#11 Twix CN MAT 35
#12 Skittles CN MAT 130
与 data.table 对应的选项是
library(data.table)
melt(setDT(df1), id.var = "Brand", value.names = "Sales")[,
c("Country", "Year") := tstrsplit(variable, "__")[-2]][, variable := NULL][]
1) dplyr/tidyr 使用最后注释中可重复显示的数据，将数据框从宽到长的形式收集起来，然后分离出新专栏。使用 Value 列作为其中的值，将新的 Variable 列散布到 Price 和 Sales 中，然后进行排序。如果顺序无关紧要，最后一行代码可以省略。
library(dplyr)
library(tidyr)
DF %>%
gather(new, Value, -Brand) %>%
separate(new, c("Country", "Variable", "Year"), sep = "__") %>%
spread(Variable, Value) %>%
arrange(Brand, desc(Country), desc(Year))
给予：
Brand Country Year Sales
1 Skittles UK YA 75
2 Skittles UK MAT 80
3 Skittles CN YA 105
4 Skittles CN MAT 130
5 Snickers UK YA 100
6 Snickers UK MAT 110
7 Snickers CN YA 90
8 Snickers CN MAT 95
9 Twix UK YA 50
10 Twix UK MAT 60
11 Twix CN YA 30
12 Twix CN MAT 35
请注意，以上内容也适用 DF2 也在下面的注释中定义。
1a) 这个稍微短一点的替代方案也可以，但只适用于 DF，不适用于 DF2。同样，如果顺序无关紧要，可以省略 arrange 行。
DF %>%
gather(new, Sales, -Brand) %>%
separate(new, c("Country", "Year"), sep = "__Sales__") %>%
arrange(Brand, desc(Country), desc(Year))
2) 此替代方案不涉及使用 reshape 将宽格式重塑为长格式的包。如果行名和顺序无关紧要，则可以省略从 rownames(long) <- NULL 语句开始的所有内容。此代码也适用于 DF2.
varying <- split(names(DF)[-1], sub(".*__(.*)__.*", "\1", names(DF)[-1]))
long <- reshape(DF, dir = "long", idvar = "Brand", varying = varying,
v.names = names(varying))
out <- transform(long, Country = sub("__.*", "", time), Year = sub(".*__", "", time),
time = NULL)
rownames(out) <- NULL
o <- with(out, order(Brand, -xtfrm(Country), -xtfrm(Year)))
out <- out[o, ]
out
给予：
Brand Sales Country Year
3 Skittles 75 UK YA
6 Skittles 80 UK MAT
9 Skittles 105 CN YA
12 Skittles 130 CN MAT
1 Snickers 100 UK YA
4 Snickers 110 UK MAT
7 Snickers 90 CN YA
10 Snickers 95 CN MAT
2 Twix 50 UK YA
5 Twix 60 UK MAT
8 Twix 30 CN YA
11 Twix 35 CN MAT
备注
Lines <- " Brand UK__Sales__YA UK__Sales__MAT CN__Sales__YA CN__Sales__MAT
1 Snickers 100 110 90 95
2 Twix 50 60 30 35
3 Skittles 75 80 105 130"
DF <- read.table(text = Lines)
# same as DF but with additional columns for Price
DF2 <- cbind(DF, setNames(10 * DF[2:5], sub("Sales", "Price", names(DF)[2:5])))
这是一个使用包 reshape2 的解决方案。
new <- reshape2::melt(data, id.vars = "Brand")
new$Country <- sub("(^[^_]*)_.*$", "\1", new$variable)
new$Year <- sub("^.*_([[:alpha:]]*$)", "\1", new$variable)
new <- new[, c(1, 4, 5, 3)]
names(new)[4] <- "Sales"
head(new)
# Brand Country Year Sales
#1 Snickers UK YA 100
#2 Twix UK YA 50
#3 Skittles UK YA 75
#4 Snickers UK MAT 110
#5 Twix UK MAT 60
#6 Skittles UK MAT 80
数据
data <-
structure(list(Brand = c("Snickers", "Twix", "Skittles"), UK__Sales__YA = c(100L,
50L, 75L), UK__Sales__MAT = c(110L, 60L, 80L), CN__Sales__YA = c(90L,
30L, 105L), CN__Sales__MAT = c(95L, 35L, 130L)), .Names = c("Brand",
"UK__Sales__YA", "UK__Sales__MAT", "CN__Sales__YA", "CN__Sales__MAT"
), class = "data.frame", row.names = c("1", "2", "3"))