在R中，如何基于列表元素对数据帧的某些列进行降序排序，而其他列按升序排序？
约翰·史密斯
现在我有一个数据框和两个列表，每个列表包含一些数据框名称，我需要添加新列，其中包含该数据框内每个列的排名问题是我必须以Deslist1降序方式对列进行排名，而匹配的名称以Asclist1升序方式匹配，因此最终所需的输出类似于
我试过了any(，within但它又tr<-any(Asclist1 %in% DF1无法正常工作了，我的问题不是如何添加包含排名的新列，我的问题是如何基于列表元素进行排名，因为您可以看到Asclist1包含的元素在DF1列中不存在
DF1 <- data.frame("name" = c("john", "adam", "leo", "lena", "Di"),
"sex" = c("m", "m", "m", "f", "f"),
"age" = c(99, 46, 23, 54, 23),
"grade" = c(96, 46, 63, 54, 23),
"income" = c(59, 36, 93, 34, 23),
"score" = c(99, 46, 23, 54, 23))
print(DF1)
Asclist1<-list("score","income","spending")
Asclist1
Deslist2<-list("age","grade")
Deslist2
更新----代码1
library(readr)
library(tidyr)
library(purrr)
library(rlang)
library(glue)
library(dplyr)
library(miscTools)
library(matrixStats)
library(shiny)
library(reshape2)
library(dplyr)
hotdrinks<-list("tea","green tea")
juices<-list("orange","mango")
energydrinks<-list("powerhorse","redbull")
drinks<-list("hotdrinks"=hotdrinks,"juices"=juices,"energydrinks"=energydrinks)
biscuits<-list("loacker","tuc")
choc<-list("aftereight","lindt")
gum<-list("trident","clortes")
sweets<-list("gum"=gum,"biscuits"=biscuits,"choc"=choc)
all_products<-list("sweets"=sweets,"drinks"=drinks)
mt<-melt(all_products)
mt2<-mt%>%mutate("Price"=c(23,34,23,23,54,32,45,23,12,56,76,43),
"Quantity"=c(10,20,26,22,51,52,45,23,12,56,76,43),
"amount"=c(23,34,23,23,54,32,45,23,12,56,76,43))
t1<-mt2[,c(4,3,1,5,6,7)]
t1
colnames(t1)<-c("CAT","PN","SP","Quantity","Price","amount")
t2<-list(unique(t1$CAT))
t2
QL<-c("Quantity","Price")
QD<-c("Quantity","amount")
QS<-c("amount","Price")
all <- list("drinks"=drinks, "sweets"=sweets)
FCX<-data.frame("sbo"=c("w","q","a"),
"Quantity_fcx"=c(3,2,5),
"Price_fcx"=c(7,8,5),
"amount_fcx"=c(4,7,3)
)
#DF1<-Y
DF1 <- t1
DF1
#print(DF1)
DFCXL<-list(colnames(DF1[-c(1:3)]))
DFCXL
DFCX1<-lapply(DFCXL, paste0, "_fcx")
DFCX1
DFCXM<-colMeans(FCX[,unlist(DFCX1)],na.rm = FALSE)
DFCXM
DFCXMd<-colMedians(data.matrix(FCX[,unlist(DFCX1)]),na.rm = FALSE )
DFCXMddf<-as.data.frame(t(DFCXMd))
DFCXMddf
DFCX1l<-as.list(DFCX1)
colnames(DFCXMddf)<-unlist(DFCX1l)
DFCXMddf
#median repeated tibble
rDFCXMddf<-DFCXMddf[rep(seq_len(nrow(DFCXMddf)), each = nrow(DF1)), ]
rDFCXMddf
DFCX<-data.frame(t(DFCXM))
DFL<-as.vector(colnames(DF1))
DFL
DFCX
#mean repeated tibble
rDFCX<-DFCX[rep(seq_len(nrow(DFCX)), each = nrow(DF1)), ]
#rDFCX
#ascending rank form smallest to largest where the smallest is the most competitive
Asclist1<-list("Quantity","Price")
#Asclist1
#descending rank form largest to smallest where the largest is the most competitive
Deslist2<-list("xyz","amount")
#Deslist2
#DF3 contains orginal dataframe with rank for each column descending & ascending
DF3<-
DF1 %>% mutate_if(grepl(paste(Deslist2, collapse = "|"), names(.)), list(rank=~rank(-.))) %>%
mutate_if(grepl(paste(Asclist1, collapse = "|"), names(.)), list(rank=~rank( .)))
DF3
#DF4 contains only determinants columns
DF4<-DF3%>%select(-one_of(DFL))
DF4
#DF5 contains all deterements with their ranks columns
DF5<-cbind(rDFCX,DF4)
DF5
#getting final rank for each column based on multiplying CX columns "weight" * normal rank to get weighted ranking
dynamic_mutate = function(DF5,
col_names = gsub("(.*)_\\w+$", "\\1", names(DF5)),
expression = "({x}_rank*{x}_fcx)",
prefix = "FINAL"){
name_list = col_names %>% unique() %>% as.list()
expr_list = name_list %>% lapply(function(x) parse_quosure(glue(expression))) %>%
setNames(paste(prefix, name_list, sep = "_"))
DF5 %>% mutate(!!!expr_list)}
DF6<-DF5 %>% dynamic_mutate()
#DF6
#getting mean for ranks
DFL2<-as.vector(colnames(DF5))
DF7<-DF6%>%select(-one_of(DFL2))
#DF7
#final limit ranking
DF8<-mutate(DF7,fnl_scr=rowMeans(DF7))
#DF8
#final rank
Ranking<-rank(DF8$fnl_scr)
#Ranking
#final dataframe
DF9<-as_tibble(cbind(DF1,Ranking))
DF9
代码2
library(readr)
library(tidyr)
library(purrr)
library(rlang)
library(glue)
library(dplyr)
library(miscTools)
library(matrixStats)
library(shiny)
library(reshape2)
library(dplyr)
hotdrinks<-list("tea","green tea")
juices<-list("orange","mango")
energydrinks<-list("powerhorse","redbull")
drinks<-list("hotdrinks"=hotdrinks,"juices"=juices,"energydrinks"=energydrinks)
biscuits<-list("loacker","tuc")
choc<-list("aftereight","lindt")
gum<-list("trident","clortes")
sweets<-list("gum"=gum,"biscuits"=biscuits,"choc"=choc)
all_products<-list("sweets"=sweets,"drinks"=drinks)
mt<-melt(all_products)
mt2<-mt%>%mutate("Price"=c(23,34,23,23,54,32,45,23,12,56,76,43),
"Quantity"=c(10,20,26,22,51,52,45,23,12,56,76,43),
"amount"=c(23,34,23,23,54,32,45,23,12,56,76,43))
t1<-mt2[,c(4,3,1,5,6,7)]
t1
colnames(t1)<-c("CAT","PN","SP","Quantity","Price","amount")
t2<-list(unique(t1$CAT))
t2
QL<-c("Quantity","Price")
QD<-c("Quantity","amount")
QS<-c("amount","Price")
all <- list("drinks"=drinks, "sweets"=sweets)
FCX<-data.frame("sbo"=c("w","q","a"),
"Quantity_fcx"=c(3,2,5),
"Price_fcx"=c(7,8,5),
"amount_fcx"=c(4,7,3)
)
#DF1<-Y
DF1 <- t1
DF1
#print(DF1)
DFCXL<-list(colnames(DF1[-c(1:3)]))
DFCXL
DFCX1<-lapply(DFCXL, paste0, "_fcx")
DFCX1
DFCXM<-colMeans(FCX[,unlist(DFCX1)],na.rm = FALSE)
DFCXM
DFCXMd<-colMedians(data.matrix(FCX[,unlist(DFCX1)]),na.rm = FALSE )
DFCXMddf<-as.data.frame(t(DFCXMd))
DFCXMddf
DFCX1l<-as.list(DFCX1)
colnames(DFCXMddf)<-unlist(DFCX1l)
DFCXMddf
#median repeated tibble
rDFCXMddf<-DFCXMddf[rep(seq_len(nrow(DFCXMddf)), each = nrow(DF1)), ]
rDFCXMddf
DFCX<-data.frame(t(DFCXM))
DFL<-as.vector(colnames(DF1))
DFL
DFCX
#mean repeated tibble
rDFCX<-DFCX[rep(seq_len(nrow(DFCX)), each = nrow(DF1)), ]
#rDFCX
#ascending rank form smallest to largest where the smallest is the most competitive
Asclist1<-list("Quantity","Price","amount")
#Asclist1
#descending rank form largest to smallest where the largest is the most competitive
Deslist2<-list("xyz")
#Deslist2
#DF3 contains orginal dataframe with rank for each column descending & ascending
DF3<-
DF1 %>% mutate_if(grepl(paste(Deslist2, collapse = "|"), names(.)), list(rank=~rank(-.))) %>%
mutate_if(grepl(paste(Asclist1, collapse = "|"), names(.)), list(rank=~rank( .)))
DF3
#DF4 contains only determinants columns
DF4<-DF3%>%select(-one_of(DFL))
DF4
#DF5 contains all deterements with their ranks columns
DF5<-cbind(rDFCX,DF4)
DF5
#getting final rank for each column based on multiplying CX columns "weight" * normal rank to get weighted ranking
dynamic_mutate = function(DF5,
col_names = gsub("(.*)_\\w+$", "\\1", names(DF5)),
expression = "({x}_rank*{x}_fcx)",
prefix = "FINAL"){
name_list = col_names %>% unique() %>% as.list()
expr_list = name_list %>% lapply(function(x) parse_quosure(glue(expression))) %>%
setNames(paste(prefix, name_list, sep = "_"))
DF5 %>% mutate(!!!expr_list)}
DF6<-DF5 %>% dynamic_mutate()
#DF6
#getting mean for ranks
DFL2<-as.vector(colnames(DF5))
DF7<-DF6%>%select(-one_of(DFL2))
#DF7
#final limit ranking
DF8<-mutate(DF7,fnl_scr=rowMeans(DF7))
#DF8
#final rank
Ranking<-rank(DF8$fnl_scr)
#Ranking
#final dataframe
DF9<-as_tibble(cbind(DF1,Ranking))
DF9
阿克伦
另一种选择是map通过创建1，-1的列来同时执行此操作
library(dplyr)
library(tidyr)
library(purrr)
library(stringr)
tibble(col1 = list(Asclist1, Deslist2), col2 = c(1, -1)) %>%
unnest_longer(col1) %>%
group_split(col2) %>%
map_dfc(~ DF1 %>%
mutate(tmp = first(.x$col2)) %>%
select(one_of(.x$col1), tmp) %>%
transmute_at(vars(-tmp), list(rank = ~rank(tmp * .)))) %>%
bind_cols(DF1, .)
# name sex age grade income score age_rank grade_rank income_rank score_rank
#1 john m 99 96 59 99 1.0 1 4 5.0
#2 adam m 46 46 36 46 3.0 4 3 3.0
#3 leo m 23 63 93 23 4.5 2 5 1.5
#4 lena f 54 54 34 54 2.0 3 2 4.0
#5 Di f 23 23 23 23 4.5 5 1 1.5
#Warning message:
#Unknown columns: `spending`
它还将通知未知列作为警告
更新资料
如果只有一个带有的列transmute_at，则不会在list后缀中添加名称。为了绕开它，我们可以创建一个函数rename_if
f1 <- function(dat) {
nm1 <- setdiff(names(dat), "tmp")
n1 <- length(nm1)
dat %>%
transmute_at(vars(-tmp), list(rank = ~rank(tmp * .))) %>%
rename_if(rep(n1 == 1, n1), ~ str_c(nm1, "_", .))
}
tibble(col1 = list(Asclist1, Deslist2), col2 = c(1, -1)) %>%
unnest_longer(col1) %>%
group_split(col2) %>%
map_dfc(~ DF1 %>%
mutate(tmp = first(.x$col2)) %>%
select(one_of(.x$col1), tmp) %>%
f1(.)) %>%
bind_cols(DF1, .)
# CAT PN SP Quantity Price amount amount_rank Quantity_rank Price_rank
# 1 sweets gum trident 23 10 23 9.5 3.5 1
# 2 sweets gum clortes 34 20 34 6.0 7.0 3
# 3 sweets biscuits loacker 23 26 23 9.5 3.5 6
# 4 sweets biscuits tuc 23 22 23 9.5 3.5 4
# 5 sweets choc aftereight 54 51 54 3.0 10.0 9
# 6 sweets choc lindt 32 52 32 7.0 6.0 10
# 7 drinks hotdrinks tea 45 45 45 4.0 9.0 8
# 8 drinks hotdrinks green tea 23 23 23 9.5 3.5 5
# 9 drinks juices orange 12 12 12 12.0 1.0 2
# 10 drinks juices mango 56 56 56 2.0 11.0 11
# 11 drinks energydrinks powerhorse 76 76 76 1.0 12.0 12
# 12 drinks energydrinks redbull 43 43 43 5.0 8.0 7
本文收集自互联网，转载请注明来源。
如有侵权，请联系[email protected] 删除。
编辑于
相关文章
Related 相关文章
- 1
VB-如何按升序或降序对数据库中的结果进行排序
- 2
如何在Spring Data中按升序对一列进行排序，对降序进行排序？
- 3
如何按列值对数据帧进行排序？
- 4
按列对数据框中的某些行进行排序
- 5
按R中的特定顺序对数据帧中的列进行排序
- 6
如何在MapReduce中按降序对数据进行排序？
- 7
按一个因子列升序对数据框进行排序，另一个因子降序
- 8
R在数据帧列表的每个数据帧中按字母顺序对列进行排序
- 9
R在数据帧列表的每个数据帧中按字母顺序对列进行排序
- 10
按R中的ALL列对数据框进行排序
- 11
按R中的ALL列对数据框进行排序
- 12
F＃-如何按降序升序对列表进行排序？
- 13
按 R 中的最小列值对数据框列表进行排序
- 14
使用Perl按升序或降序对单个列进行排序
- 15
如何基于R中列向量的升序/降序对列表进行子集
- 16
在某些位置对一些列表元素进行排序，而其他位置保持不变
- 17
如何按R中的数据帧列表中的第二行从水平到最大的顺序对列进行排序？
- 18
SQL查询基于其他列值对数据进行排序
- 19
如何让我的快速排序算法按升序和降序对数组进行排序？
- 20
按列模式对数据帧进行排序/排序/聚类/排序
- 21
如何按升序对数值的浮动列表进行排序？
- 22
如何在Scala中按数字对中的第二对按降序对数字对列表进行排序？
- 23
如何按目标向量给定的列值对数据帧进行重新排序
- 24
如何按目标向量给定的列值对数据帧进行重新排序
- 25
如何按一个变量对R中的数据帧进行排序，同时对其他变量进行分组
- 26
在多张纸中按降序对数据进行排序
- 27
以第一列的降序对数据进行排序，对于相等的值，以升序使用第二列
- 28
R中按列对元素进行排序
- 29
如何从PHP中的Key按升序对数组进行排序？
我来说两句