通过多个条件删除重复项

Question

我有数据，其中个人（姓名）在蛋阶段类别中多次出现。我希望每个人只有一个样本，但我不只是想保留 R 找到的第一个样本。我想保留该组在所有其他类别中出现最多的那个。希望我的示例有助于说明这一点。

library(tidyverse)
myDF <- read.table(text="Tissue Food Eggphase Name Group
  wb fl after Kia a
  wb fl after Kia c
  wb wf before Kia b
  wb fl before Lucy c
  wb fl after Lucy b
  wb fl after Lucy c
  wb fl yolkdep Jess c
  wb fl yolkdep Betty a
  wb fl yolkdep Betty b", header = TRUE)

我只想保留 Name 出现一次的行，按 Tissue、Food 和 Eggphase 分组，但我想选择 Group 出现在大多数（如果不是所有不同的 eggphase）的行（具有相同的 Tissue 和 Food 组合）。

   #results I want
  Tissue Food Eggphase  Name Group
1     wb   fl    after   Kia     c
2     wb   wf   before   Kia     b
3     wb   fl   before  Lucy     c
4     wb   fl    after  Lucy     c
5     wb   fl  yolkdep  Jess     c
6     wb   fl  yolkdep Betty     b

我试过了

one_bird <- myDF %>% 
  distinct(Tissue, Food, Eggphase, Name, .keep_all = TRUE)

但它只保留第一个条目

  Tissue Food Eggphase  Name Group
1     wb   fl    after   Kia     a
2     wb   wf   before   Kia     b
3     wb   fl   before  Lucy     c
4     wb   fl    after  Lucy     b
5     wb   fl  yolkdep  Jess     c
6     wb   fl  yolkdep Betty     b

关于如何告诉它选择Group 出现在TissueFood 组合中的大多数（如果不是全部）蛋相中的行的任何想法？ 在我的示例中，在wb 和fl 的Tissue 和Food 组合中出现最多的组是c 和b，但Kia 没有出现在Group b所以c 是一个更好的选择。像这个例子一样，我的数据有重复，这些重复来自不是最常见的Group 组，我如何让它为该行选择下一个最常见的？

我希望我已经说得够明白了。

Answer 1

一种选择是创建一个按“组织”、“食物”、“组”分组的频率列，然后在“n”上执行降序arrange 并使用distinct

library(dplyr)
myDF %>%
     group_by(Tissue, Food, Group) %>%
     mutate(n = n()) %>% arrange(Tissue, Food, Eggphase, Name, desc(n)) %>% 
     ungroup %>%
     distinct(Tissue, Food, Eggphase, Name, .keep_all = TRUE) %>%
     select(-n)

Answer 2

我想这篇文章和答案应该让我有理由学习 dplyr 和 tidyverse，但是由于我已经努力给出一个有效的答案，所以它是：

myDF <- read.table(text="Tissue Food Eggphase Name Group
  wb fl after Kia a
  wb fl after Kia c
  wb wf before Kia b
  wb fl before Lucy c
  wb fl after Lucy b
  wb fl after Lucy c
  wb fl yolkdep Jess c
  wb fl yolkdep Betty a
  wb fl yolkdep Betty b", header = TRUE)

# I usually have the following setting active: options(stringsAsFactors=F)
# The following might error without such a setting

# Create a var that indicates a duplicate or a record with a duplicate
myDF$duplicate <- duplicated(myDF[,c('Name','Eggphase','Tissue','Food')])
myDF$duplicate <- ifelse(duplicated(myDF[,c('Name','Eggphase','Tissue','Food')],fromLast=T),yes=T, no=myDF$duplicate)

# Count eggphases by group 
eggphaseCount <- with(myDF,aggregate(x=list(Group_phaseCt=Eggphase),by=list(Group=Group),FUN=function(x) length(unique(x))))
# Merge to DF
myDF <- merge(myDF,eggphaseCount,by='Group',all=T)

# Get the max # of egphases by name
scale <- with(myDF,aggregate(x=list(PhaseMax=Group_phaseCt),by=list(Name=Name),FUN=max))
# Add to DF
myDF <- merge(myDF,scale,by='Name',all=T)

# Take the ratio
myDF$bestRatio <- with(myDF,Group_phaseCt/PhaseMax)
# Keep only those that aren't a duplicate, or are a duplicate and have the highest ratio
myDF2 <- myDF[with(myDF,which(duplicate==FALSE | (duplicate==TRUE & bestRatio==1))),]

Answer 3

嘿，谢谢你们的帮助！！您建议的组合似乎有效：

# Create a var that indicates a duplicate or a record with a duplicate
myDF$duplicate <- duplicated(myDF[,c('Name','Eggphase','Tissue','Food')])
#this won't tell you that the first entry og the combination is double
# so need to make them check against the previous row
myDF$duplicate <- ifelse(duplicated(myDF[,c('Name','Eggphase','Tissue','Food')],fromLast=T),yes=T, no=myDF$duplicate)

# Count eggphases by group 
eggphaseCount <- with(myDF,aggregate(x=list(Group_phaseCt=Eggphase),by=list(Group=Group),FUN=function(x) length(unique(x))))
# Merge to DF
myDF <- merge(myDF,eggphaseCount,by='Group',all=T)

# Get the max # of egphases by name
scale <- with(myDF,aggregate(x=list(PhaseMax=Group_phaseCt),by=list(Name=Name),FUN=max))
# Add to DF
myDF <- merge(myDF,scale,by='Name',all=T)

# Take the ratio
myDF$bestRatio <- with(myDF,Group_phaseCt/PhaseMax)

# make new df without duplicates
myDF2 <- myDF %>% 
#arrange in a way that the first duplicate is from the group with the most eggphases
#and the name appears in the most egg phases 
  arrange(Tissue, Food, Eggphase, Name, Group, desc(Group_phaseCt), desc(PhaseMax)) %>% 
#select only distinct rows according to specified var keep all others
  distinct(Tissue, Food, Eggphase, Name, .keep_all = TRUE)