在r中，如何基于列表元素对数据列的某些列降级和其他列升序进行排序？

问题描述 投票：2回答：2

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的同名名称，我需要添加新列，这些新列包含该数据框内每个列的排名，问题是我必须对Deslist1降序排列方式，并且具有相同名称的列与Asclist1升序匹配，因此最终所需的输出类似于this

我尝试了any(和within，但它不起作用tr<-any(Asclist1 %in% DF1同样，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
r dataframe shiny
2个回答
2
投票
另一个选择是通过创建1 -1s的列同时使用map来执行此操作

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` 

它还将作为警告通知未知列
更新
如果只有一个带有transmute_at的列，则不会在list中添加名称作为后缀。要绕过它，我们可以使用rename_if创建一个函数
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
2
投票
我们可以使用rank和mutate_if应用grepl

library(dplyr) DF1 %>% mutate_if(grepl(paste(Asclist1, collapse = "|"), names(.)), list(rank=~rank( .))) %>% mutate_if(grepl(paste(Deslist2, collapse = "|"), names(.)), list(rank=~rank(-.))) 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

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