变量的多模式

Question

我似乎找不到问题的答案。

这是示例数据

Credit Card Type  Bank   Year   Total Balance
MASTER CARD       BOFA   2017   0
MASTER CARD       BOFA   2017   0
MASTER CARD       BOFA   2017   0
VISA              Wells  2018    
VISA              Wells  2018   
VISA              Wells  2018

等等

我想弄清楚如何通过所有变量的总余额来获得模式所以最后会变成这样

期望的输出：

Credit Card Type  Bank   Year   Mode
MASTER CARD       BOFA   2017   0
VISA              Wells  2018

Answer 1

按照 Frank 的建议，使用 whosebug.com/q/2547402 中的 Mode，使用 dplyr 很容易做到这一点。

library(dplyr)

df %>% 
    group_by(CreditCardType, Bank, Year) %>%
    summarise(mode = Mode(TotalBalance))

其中 df 是：

df <- read.table(text = 'CreditCardType  Bank   Year   TotalBalance
MASTERCARD       BOFA   2017   0
MASTERCARD       BOFA   2017   0
MASTERCARD       BOFA   2017   0
VISA              Wells  2018    
VISA              Wells  2018   
VISA              Wells  2018   ', header = T, stringsAsFactors = F)

Answer 2

来自这个问题

library(plyr)
getmode<- function(origtable,groupby,columnname) {
  data <- ddply (origtable, groupby, .fun = function(xx){
    c(m1 = paste(names(sort(table(xx[,columnname]),decreasing=TRUE)[1]))
    ) } ) 
  return(data)
}

getmode(df,c("CreditCardType","Bank","Year"),"TotalBalance")

df<-read.table(text="CreditCardType  Bank   Year   TotalBalance
MASTERCARD       BOFA   2017   0
MASTERCARD       BOFA   2017   0
MASTERCARD       BOFA   2017   0
VISA              Wells  2018    
VISA              Wells  2018   
VISA              Wells  2018   ", header=T, stringsAsFactors=F)

Answer 3

不同的dplyr解决方案：

df %>%
  add_count(Credit_Card_Type, Bank, Year, Total_Balance) %>%
  filter(n == max(n)) %>%
  distinct() %>%
  select(-n)

考虑关系并选择第一个模式值：

df %>%
  add_count(Credit_Card_Type, Bank, Year, Total_Balance) %>%
  filter(n == max(n)) %>%
  distinct() %>%
  select(-n) %>%
  group_by(Credit_Card_Type, Bank, Year) %>%
  summarise(Total_Balance = first(Total_Balance))

数据：

df <- read.table(text = "Credit_Card_Type Bank Year Total_Balance
           MASTER_CARD BOFA 2017 100
           MASTER_CARD BOFA 2017 100
           MASTER_CARD BOFA 2017 700
           VISA Wells 2018 60
           VISA Wells 2018 50
           VISA Wells 2018 60", header = TRUE)

Answer 4

我找到了使用 data.table 和 modeest 包的解决方案。

library(data.table)
library(modeest)
dt <- data.table("Type"=c(rep("MASTERCARD",3),rep("VISA",3)),"Bank"=c(rep("BOFA",3),rep("Wells",3)),"Year"=c(rep(2017,3),rep(2018,3)),"TotalBalance"=c(100,100,700,60,50,60))
dt[,mfv(TotalBalance)[1],by=c("Type","Bank","Year")]

          Type  Bank Year  V1
 1: MASTERCARD  BOFA 2017 100
 2:       VISA Wells 2018  60

变量的多模式

Multiple Modes by variables

variables

r

mode