R 融化数据,保持某些列成对
我有以下资料:R 融化数据,保持某些列成对,r,reshape,melt,R,Reshape,Melt,我有以下资料: DT <- structure(list(ECOST = c("Choice_01", "Choice_02", "Choice_03", "Choice_04", "Choice_05", "Choice_06", "Choice_07", "Choice_08", "Choice_09",
DT <- structure(list(ECOST = c("Choice_01", "Choice_02", "Choice_03",
"Choice_04", "Choice_05", "Choice_06", "Choice_07", "Choice_08",
"Choice_09", "Choice_10", "Choice_11", "Choice_12"), control = c(18,
30, 47, 66, 86, 35, 31, 46, 55, 39, 55, 41), treatment = c(31,
35, 46, 68, 86, 36, 32, 42, 52, 39, 58, 43), control_p = c(0.163636363636364,
0.272727272727273, 0.427272727272727, 0.6, 0.781818181818182,
0.318181818181818, 0.281818181818182, 0.418181818181818, 0.5,
0.354545454545455, 0.5, 0.372727272727273), treatment_p = c(0.319587628865979,
0.360824742268041, 0.474226804123711, 0.701030927835051, 0.88659793814433,
0.371134020618557, 0.329896907216495, 0.43298969072165, 0.536082474226804,
0.402061855670103, 0.597938144329897, 0.443298969072165)), row.names = c(NA,
-12L), class = c("tbl_df", "tbl", "data.frame"))
# A tibble: 12 x 5
ECOST control treatment control_p treatment_p
<chr> <dbl> <dbl> <dbl> <dbl>
1 Choice_01 18 31 0.164 0.320
2 Choice_02 30 35 0.273 0.361
3 Choice_03 47 46 0.427 0.474
4 Choice_04 66 68 0.6 0.701
5 Choice_05 86 86 0.782 0.887
6 Choice_06 35 36 0.318 0.371
7 Choice_07 31 32 0.282 0.330
8 Choice_08 46 42 0.418 0.433
9 Choice_09 55 52 0.5 0.536
10 Choice_10 39 39 0.355 0.402
11 Choice_11 55 58 0.5 0.598
12 Choice_12 41 43 0.373 0.443
使用
pivot\u longer
、一些数据争用以及之后的pivot\u wide
您可以实现所需的结果,如下所示:
library(tidyr)
图书馆(dplyr)
DT%>%
枢轴长度(-ECOST)%>%
分离(名称,分为=c(“组”,“什么”))%>%
突变(what=ifelse(is.na(what),“count”,“percentage”))%>%
枢轴(名称来自“什么”,值来自“值”)
#>#tibble:24 x 4
#>生态成本组计数百分比
#>
#>1选择_01控制18 0.164
#>2选择_01治疗31 0.320
#>3选择_02控制30 0.273
#>4选择02治疗35 0.361
#>5选项03控制47 0.427
#>6选择_03治疗46 0.474
#>7选择04控制66 0.6
#>8选择04治疗68 0.701
#>9选择_05对照86 0.782
#>10选择_05治疗86 0.887
#>#…还有14行
由(v1.0.0)于2021年2月21日创建,使用
pivot\u longer
,一些数据争用,然后pivot\u更宽
您可以实现如下所示的预期结果:
library(tidyr)
图书馆(dplyr)
DT%>%
枢轴长度(-ECOST)%>%
分离(名称,分为=c(“组”,“什么”))%>%
突变(what=ifelse(is.na(what),“count”,“percentage”))%>%
枢轴(名称来自“什么”,值来自“值”)
#>#tibble:24 x 4
#>生态成本组计数百分比
#>
#>1选择_01控制18 0.164
#>2选择_01治疗31 0.320
#>3选择_02控制30 0.273
#>4选择02治疗35 0.361
#>5选项03控制47 0.427
#>6选择_03治疗46 0.474
#>7选择04控制66 0.6
#>8选择04治疗68 0.701
#>9选择_05对照86 0.782
#>10选择_05治疗86 0.887
#>#…还有14行
由(v1.0.0)于2021-02-21创建,您可以重命名列,以便在
count
和percentage
列之间有明确的区别,然后再使用pivot\u
library(dplyr)
library(tidyr)
DT %>%
rename_with(~paste(sub('_.*', '', .),
rep(c('count', 'percentage'), each = 2), sep = '_'), -1) %>%
pivot_longer(cols = -ECOST,
names_to = c('group', '.value'),
names_sep = '_')
# A tibble: 24 x 4
# ECOST group count percentage
# <chr> <chr> <dbl> <dbl>
# 1 Choice_01 control 18 0.164
# 2 Choice_01 treatment 31 0.320
# 3 Choice_02 control 30 0.273
# 4 Choice_02 treatment 35 0.361
# 5 Choice_03 control 47 0.427
# 6 Choice_03 treatment 46 0.474
# 7 Choice_04 control 66 0.6
# 8 Choice_04 treatment 68 0.701
# 9 Choice_05 control 86 0.782
#10 Choice_05 treatment 86 0.887
# … with 14 more rows
库(dplyr)
图书馆(tidyr)
DT%>%
将_重命名为(~paste(sub)(“.*”,“,”),
代表(c('count','percentage'),各=2),sep='''.',-1)%>%
枢轴长度(cols=-ECOST,
name_to=c('group','.value'),
名称_sep='')
#A tibble:24 x 4
#生态成本组计数百分比
#
#1选择_01控制18 0.164
#2选择_01治疗31 0.320
#3选择_02控制30 0.273
#4选择02治疗35 0.361
#5选项03控制47 0.427
#6选择_03治疗46 0.474
#7选择04控制66 0.6
#8选择04治疗68 0.701
#9选择_05对照86 0.782
#10选择_05治疗86 0.887
#…还有14行
您可以重命名列,以便清楚区分计数
和百分比
列,然后再使用pivot\u
library(dplyr)
library(tidyr)
DT %>%
rename_with(~paste(sub('_.*', '', .),
rep(c('count', 'percentage'), each = 2), sep = '_'), -1) %>%
pivot_longer(cols = -ECOST,
names_to = c('group', '.value'),
names_sep = '_')
# A tibble: 24 x 4
# ECOST group count percentage
# <chr> <chr> <dbl> <dbl>
# 1 Choice_01 control 18 0.164
# 2 Choice_01 treatment 31 0.320
# 3 Choice_02 control 30 0.273
# 4 Choice_02 treatment 35 0.361
# 5 Choice_03 control 47 0.427
# 6 Choice_03 treatment 46 0.474
# 7 Choice_04 control 66 0.6
# 8 Choice_04 treatment 68 0.701
# 9 Choice_05 control 86 0.782
#10 Choice_05 treatment 86 0.887
# … with 14 more rows
库(dplyr)
图书馆(tidyr)
DT%>%
将_重命名为(~paste(sub)(“.*”,“,”),
代表(c('count','percentage'),各=2),sep='''.',-1)%>%
枢轴长度(cols=-ECOST,
name_to=c('group','.value'),
名称_sep='')
#A tibble:24 x 4
#生态成本组计数百分比
#
#1选择_01控制18 0.164
#2选择_01治疗31 0.320
#3选择_02控制30 0.273
#4选择02治疗35 0.361
#5选项03控制47 0.427
#6选择_03治疗46 0.474
#7选择04控制66 0.6
#8选择04治疗68 0.701
#9选择_05对照86 0.782
#10选择_05治疗86 0.887
#…还有14行
这里是一个数据表方法,其中包含了melt.data.table()的限制/功能
库(data.table)
setDT(DT)
#获取后缀
后缀这里是一个data.table
方法,它具有melt.data.table()
库(data.table)
setDT(DT)
#获取后缀
后缀被接受为最容易理解(至少对我而言),因此(对我而言)最容易适应其他情况。被接受为最容易理解(至少对我而言),因此(对我而言)最容易适应其他情况。
library( data.table )
setDT(DT)
#get suffixes
suffix <- unique( sub( "(^.*)(_[a-z])", "\\1", names( DT[ , -1] ) ) )
#melt
DT2 <- melt( DT, id.vars = "ECOST", measure.vars = patterns( count = "[a-oq-z]$", percentage = "_p$"))
#replace factor-levels with the colnames
setattr(DT2$variable, "levels", suffix )
ECOST variable count percentage
1: Choice_01 control 18 0.1636364
2: Choice_02 control 30 0.2727273
3: Choice_03 control 47 0.4272727
4: Choice_04 control 66 0.6000000
5: Choice_05 control 86 0.7818182
6: Choice_06 control 35 0.3181818
7: Choice_07 control 31 0.2818182
8: Choice_08 control 46 0.4181818
9: Choice_09 control 55 0.5000000
10: Choice_10 control 39 0.3545455
11: Choice_11 control 55 0.5000000
12: Choice_12 control 41 0.3727273
13: Choice_01 treatment 31 0.3195876
14: Choice_02 treatment 35 0.3608247
15: Choice_03 treatment 46 0.4742268
16: Choice_04 treatment 68 0.7010309
17: Choice_05 treatment 86 0.8865979
18: Choice_06 treatment 36 0.3711340
19: Choice_07 treatment 32 0.3298969
20: Choice_08 treatment 42 0.4329897
21: Choice_09 treatment 52 0.5360825
22: Choice_10 treatment 39 0.4020619
23: Choice_11 treatment 58 0.5979381
24: Choice_12 treatment 43 0.4432990
ECOST variable count percentage