[英]Reshaping data.frame from wide to long format
我在将data.frame
从宽表转换为长表时遇到了一些麻烦。 目前它看起来像这样:
Code Country 1950 1951 1952 1953 1954
AFG Afghanistan 20,249 21,352 22,532 23,557 24,555
ALB Albania 8,097 8,986 10,058 11,123 12,246
现在我想把这个data.frame
变成一个长的data.frame
。 是这样的:
Code Country Year Value
AFG Afghanistan 1950 20,249
AFG Afghanistan 1951 21,352
AFG Afghanistan 1952 22,532
AFG Afghanistan 1953 23,557
AFG Afghanistan 1954 24,555
ALB Albania 1950 8,097
ALB Albania 1951 8,986
ALB Albania 1952 10,058
ALB Albania 1953 11,123
ALB Albania 1954 12,246
正如某些人在类似问题中所建议的那样,我已经查看并尝试使用melt()
和reshape()
函数。 但是,到目前为止,我只得到混乱的结果。
如果可能的话,我想用reshape()
function 来做,因为它看起来更容易处理。
三种替代解决方案:
1)使用data.table :
您可以使用与reshape2
包中相同的melt
功能(这是一个扩展和改进的实现)。 来自data.table
的melt
还具有比来自reshape2
的melt
-function 更多的参数。 例如,您还可以指定变量列的名称:
library(data.table)
long <- melt(setDT(wide), id.vars = c("Code","Country"), variable.name = "year")
这使:
> long Code Country year value 1: AFG Afghanistan 1950 20,249 2: ALB Albania 1950 8,097 3: AFG Afghanistan 1951 21,352 4: ALB Albania 1951 8,986 5: AFG Afghanistan 1952 22,532 6: ALB Albania 1952 10,058 7: AFG Afghanistan 1953 23,557 8: ALB Albania 1953 11,123 9: AFG Afghanistan 1954 24,555 10: ALB Albania 1954 12,246
一些替代符号:
melt(setDT(wide), id.vars = 1:2, variable.name = "year")
melt(setDT(wide), measure.vars = 3:7, variable.name = "year")
melt(setDT(wide), measure.vars = as.character(1950:1954), variable.name = "year")
2)使用tidyr :
library(tidyr)
long <- wide %>% gather(year, value, -c(Code, Country))
一些替代符号:
wide %>% gather(year, value, -Code, -Country)
wide %>% gather(year, value, -1:-2)
wide %>% gather(year, value, -(1:2))
wide %>% gather(year, value, -1, -2)
wide %>% gather(year, value, 3:7)
wide %>% gather(year, value, `1950`:`1954`)
3) 使用reshape2 :
library(reshape2)
long <- melt(wide, id.vars = c("Code", "Country"))
给出相同结果的一些替代符号:
# you can also define the id-variables by column number
melt(wide, id.vars = 1:2)
# as an alternative you can also specify the measure-variables
# all other variables will then be used as id-variables
melt(wide, measure.vars = 3:7)
melt(wide, measure.vars = as.character(1950:1954))
笔记:
数据的另一个问题是这些值将被 R 作为字符值读取(作为数字中的,
的结果)。 您可以使用gsub
和as.numeric
修复它:
long$value <- as.numeric(gsub(",", "", long$value))
或直接使用data.table
或dplyr
:
# data.table
long <- melt(setDT(wide),
id.vars = c("Code","Country"),
variable.name = "year")[, value := as.numeric(gsub(",", "", value))]
# tidyr and dplyr
long <- wide %>% gather(year, value, -c(Code,Country)) %>%
mutate(value = as.numeric(gsub(",", "", value)))
数据:
wide <- read.table(text="Code Country 1950 1951 1952 1953 1954
AFG Afghanistan 20,249 21,352 22,532 23,557 24,555
ALB Albania 8,097 8,986 10,058 11,123 12,246", header=TRUE, check.names=FALSE)
reshape()
需要一段时间来适应,就像melt
/ cast
一样。 这是一个重塑的解决方案,假设您的数据框称为d
:
reshape(d,
direction = "long",
varying = list(names(d)[3:7]),
v.names = "Value",
idvar = c("Code", "Country"),
timevar = "Year",
times = 1950:1954)
使用tidyr_1.0.0
,另一个选项是pivot_longer
library(tidyr)
pivot_longer(df1, -c(Code, Country), values_to = "Value", names_to = "Year")
# A tibble: 10 x 4
# Code Country Year Value
# <fct> <fct> <chr> <fct>
# 1 AFG Afghanistan 1950 20,249
# 2 AFG Afghanistan 1951 21,352
# 3 AFG Afghanistan 1952 22,532
# 4 AFG Afghanistan 1953 23,557
# 5 AFG Afghanistan 1954 24,555
# 6 ALB Albania 1950 8,097
# 7 ALB Albania 1951 8,986
# 8 ALB Albania 1952 10,058
# 9 ALB Albania 1953 11,123
#10 ALB Albania 1954 12,246
df1 <- structure(list(Code = structure(1:2, .Label = c("AFG", "ALB"), class = "factor"),
Country = structure(1:2, .Label = c("Afghanistan", "Albania"
), class = "factor"), `1950` = structure(1:2, .Label = c("20,249",
"8,097"), class = "factor"), `1951` = structure(1:2, .Label = c("21,352",
"8,986"), class = "factor"), `1952` = structure(2:1, .Label = c("10,058",
"22,532"), class = "factor"), `1953` = structure(2:1, .Label = c("11,123",
"23,557"), class = "factor"), `1954` = structure(2:1, .Label = c("12,246",
"24,555"), class = "factor")), class = "data.frame", row.names = c(NA,
-2L))
使用重塑包:
#data
x <- read.table(textConnection(
"Code Country 1950 1951 1952 1953 1954
AFG Afghanistan 20,249 21,352 22,532 23,557 24,555
ALB Albania 8,097 8,986 10,058 11,123 12,246"), header=TRUE)
library(reshape)
x2 <- melt(x, id = c("Code", "Country"), variable_name = "Year")
x2[,"Year"] <- as.numeric(gsub("X", "" , x2[,"Year"]))
由于这个答案被标记为r-faq ,我觉得从基础 R 分享另一个替代方案会很有用: stack
。
但是请注意,该stack
不适用于factor
s——它仅在is.vector
为TRUE
时才有效,并且从is.vector
的文档中,我们发现:
如果 x 是指定模式的向量,除了 names 之外没有其他属性,
is.vector
返回TRUE
。 否则返回FALSE
。
我正在使用来自@Jaap's answer的样本数据,其中年份列中的值是factor
s。
这是stack
方法:
cbind(wide[1:2], stack(lapply(wide[-c(1, 2)], as.character)))
## Code Country values ind
## 1 AFG Afghanistan 20,249 1950
## 2 ALB Albania 8,097 1950
## 3 AFG Afghanistan 21,352 1951
## 4 ALB Albania 8,986 1951
## 5 AFG Afghanistan 22,532 1952
## 6 ALB Albania 10,058 1952
## 7 AFG Afghanistan 23,557 1953
## 8 ALB Albania 11,123 1953
## 9 AFG Afghanistan 24,555 1954
## 10 ALB Albania 12,246 1954
这是另一个示例,展示了使用tidyr
gather
的方法。 您可以选择要gather
的列,方法是单独删除它们(就像我在这里所做的那样),或者明确包含您想要的年份。
请注意,为了处理逗号(如果未设置check.names = FALSE
则添加 X),我还使用dplyr
的 mutate 和parse_number
中的readr
将文本值转换回数字。 这些都是tidyverse
的一部分,因此可以与library(tidyverse)
一起加载
wide %>%
gather(Year, Value, -Code, -Country) %>%
mutate(Year = parse_number(Year)
, Value = parse_number(Value))
回报:
Code Country Year Value
1 AFG Afghanistan 1950 20249
2 ALB Albania 1950 8097
3 AFG Afghanistan 1951 21352
4 ALB Albania 1951 8986
5 AFG Afghanistan 1952 22532
6 ALB Albania 1952 10058
7 AFG Afghanistan 1953 23557
8 ALB Albania 1953 11123
9 AFG Afghanistan 1954 24555
10 ALB Albania 1954 12246
这是一个sqldf解决方案:
sqldf("Select Code, Country, '1950' As Year, `1950` As Value From wide
Union All
Select Code, Country, '1951' As Year, `1951` As Value From wide
Union All
Select Code, Country, '1952' As Year, `1952` As Value From wide
Union All
Select Code, Country, '1953' As Year, `1953` As Value From wide
Union All
Select Code, Country, '1954' As Year, `1954` As Value From wide;")
要在不输入所有内容的情况下进行查询,您可以使用以下命令:
感谢 G. Grothendieck 实施它。
ValCol <- tail(names(wide), -2)
s <- sprintf("Select Code, Country, '%s' As Year, `%s` As Value from wide", ValCol, ValCol)
mquery <- paste(s, collapse = "\n Union All\n")
cat(mquery) #just to show the query
#> Select Code, Country, '1950' As Year, `1950` As Value from wide
#> Union All
#> Select Code, Country, '1951' As Year, `1951` As Value from wide
#> Union All
#> Select Code, Country, '1952' As Year, `1952` As Value from wide
#> Union All
#> Select Code, Country, '1953' As Year, `1953` As Value from wide
#> Union All
#> Select Code, Country, '1954' As Year, `1954` As Value from wide
sqldf(mquery)
#> Code Country Year Value
#> 1 AFG Afghanistan 1950 20,249
#> 2 ALB Albania 1950 8,097
#> 3 AFG Afghanistan 1951 21,352
#> 4 ALB Albania 1951 8,986
#> 5 AFG Afghanistan 1952 22,532
#> 6 ALB Albania 1952 10,058
#> 7 AFG Afghanistan 1953 23,557
#> 8 ALB Albania 1953 11,123
#> 9 AFG Afghanistan 1954 24,555
#> 10 ALB Albania 1954 12,246
不幸的是,我认为PIVOT
和UNPIVOT
不适用于R
SQLite
。 如果您想以更复杂的方式编写查询,还可以查看以下帖子:
也可以使用cdata
包,它使用(转换)控制表的概念:
# data
wide <- read.table(text="Code Country 1950 1951 1952 1953 1954
AFG Afghanistan 20,249 21,352 22,532 23,557 24,555
ALB Albania 8,097 8,986 10,058 11,123 12,246", header=TRUE, check.names=FALSE)
library(cdata)
# build control table
drec <- data.frame(
Year=as.character(1950:1954),
Value=as.character(1950:1954),
stringsAsFactors=FALSE
)
drec <- cdata::rowrecs_to_blocks_spec(drec, recordKeys=c("Code", "Country"))
# apply control table
cdata::layout_by(drec, wide)
我目前正在探索该软件包并发现它很容易获得。 它是为更复杂的转换而设计的,包括反向转换。 有一个教程可用。
您还可以在R 食谱中看到许多示例
olddata_wide <- read.table(header=TRUE, text='
subject sex control cond1 cond2
1 M 7.9 12.3 10.7
2 F 6.3 10.6 11.1
3 F 9.5 13.1 13.8
4 M 11.5 13.4 12.9
')
# Make sure the subject column is a factor
olddata_wide$subject <- factor(olddata_wide$subject)
olddata_long <- read.table(header=TRUE, text='
subject sex condition measurement
1 M control 7.9
1 M cond1 12.3
1 M cond2 10.7
2 F control 6.3
2 F cond1 10.6
2 F cond2 11.1
3 F control 9.5
3 F cond1 13.1
3 F cond2 13.8
4 M control 11.5
4 M cond1 13.4
4 M cond2 12.9
')
# Make sure the subject column is a factor
olddata_long$subject <- factor(olddata_long$subject)
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