R中的单元均值 [英] Mean of Cell in R

问题描述

我的初始数据如下:

ID<-c(1,2,3,4)
Value<-c("1,2","0,-1",1,"","")
Data<-data.frame(ID, Value)

我想从每行的Value中创建一个MeanValue.如果值中没有值，我想取值的均值.

I want to create a MeanValue from Value for every Row. And if the Value is having no Value in it, i would like to take the Mean for the Value.

我计算第一步的想法是:

My Idea to Compute the Mean for the first Step was:

library(stringr)
AverageMean<-mean(as.numeric(str_split(Data$Value, ",")))

但是它正在创建错误

最终数据应类似于:

ID<-c(1,2,3,4)
Value<-c("1,2","0,-1",1,"","")
AverageMean<-c(1.5,-0.5,1,0.666,0.666)
FinalData<-data.frame(ID, Value, AverageMean)

推荐答案

基于该信息并处理您的代码，首先在相关列上执行str_split，输出为list.要获取单个列表元素的mean，可以将lapply与mean结合使用.然后unlist并将最后一个值Val[length(Val)]替换为所有其他值的mean，并根据上述内容创建一个新列AverageMean.

Based on the info, and working on your code, first you do str_split on the concerned column and the output is a list. For getting the mean for individual list elements, you can use lapply with mean. Then unlist it and replace the last value Val[length(Val)] with the mean of all other values and create a new column AverageMean based on the above.

 Val <- unlist(lapply(str_split(Data$Value, ","),
                  function(x) mean(as.numeric(x), na.rm=TRUE)))
 Val[length(Val)] <- mean(Val[-length(Val)], na.rm=TRUE)
 Data$AverageMean <- Val
  Data
  #  ID Value AverageMean
  #1  1   1,2   1.5000000
  #2  2  0,-1  -0.5000000
  #3  3     1   1.0000000
  #4  4         0.6666667

更新

如果您有多个missing values，并希望将其替换为列的mean，

Update

If you have multiple missing values and want to replace that with the mean of the column,

   Data <- data.frame(ID=1:5, Value=c("1,2", "0,-1", 1, "", ""))
   Val <- unlist(lapply(str_split(Data$Value, ","),
               function(x) mean(as.numeric(x), na.rm=TRUE)))

以上步骤相同.用is.na创建一个逻辑索引，并通过取消逻辑索引!is.na将所有那些缺失的值替换为没有缺失的值的平均值.

The above steps are the same. Create a logical index with is.na and replace all those missing values by the mean of values that are not missing by negating the logical index !is.na.

   Val[is.na(Val)] <- mean(Val[!is.na(Val)])
   Data$AverageMean <- Val
   Data
   # ID Value AverageMean
   #1  1   1,2   1.5000000
   #2  2  0,-1  -0.5000000
   #3  3     1   1.0000000
   #4  4         0.6666667
   #5  5         0.6666667

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