r - 在 r 中，如何根据列表元素对数据框的一些列降序排列，而其他列升序排列？

Question

现在我有一个数据框和两个列表，每个列表都包含一些数据框名称，我需要添加新列，其中包含数据框内每一列的排名问题是我必须对 Deslist1 中的列进行排名降序方式和 colnames 与 Asclist1 匹配的列以升序方式，因此最终需要的输出类似于

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根据列表元素进行排名，如您所见 Asclist1包含 DF1 中不存在的元素列

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

Answer 1

另一种选择是使用 map 通过创建 1, -1s 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` 

它还会通知未知列作为警告

更新

如果只有一个列带有transmute_at，它不会将list 中的名称添加为后缀。为了绕过它，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