按因子变量在数据框中进行Colmeans

Question

I'm trying to get the mean of some variables inside a dataframe for different factors. Say I have:

    time    geo var1    var2   var3    var4
1   1990    AT  1       7      13       19
2   1991    AT  2       8      14       20
3   1992    AT  3       9      15       21
4   1990    DE  4       10     16       22
5   1991    DE  5       11     17       23
6   1992    DE  6       12     18       24

And I want:

    time    geo var1    var2   var3    var4   m_var2   m_var3
1   1990    AT  1       7      13       19    8        14
2   1991    AT  2       8      14       20    8        14
3   1992    AT  3       9      15       21    8        14
4   1990    DE  4       10     16       22    11       17
5   1991    DE  5       11     17       23    11       17
6   1992    DE  6       12     18       24    11       17

I've tried a few things with by() and lapply() but I think this goes into the direction of ddply

require(plyr)
Dataset <- data.frame(time=rep(c(1990:1992),2),geo=c(rep("AT",3),rep("DE",3))
      ,var1=as.numeric(c(1:6)),var2=as.numeric(c(7:12)),var3=as.numeric(c(13:18)),
      var4=as.numeric(c(19:24)))

newvars <- c("var2","var3")
newData <- Dataset[,c("geo",newvars)]

Currently, I can choose between two errors:

ddply(newData,newData[,"geo"],colMeans) 
#where R apparently thinks AT is the variable?

ddply(newData,"geo",colMeans)
#where R worries about the factor variable not being numeric?

My lapply attempts got me quite far but then left me with a list I could not get back into the dataframe:

lapply(newvars,function(x){
       by(Dataset[x],Dataset[,"geo"],function(x) 
       rep(colMeans(x,na.rm=T),length(unique(Dataset[,"time"]))))
       })

I think this must even be able with merge and filters as here: Lapply in a dataframe over different variables using filters , but I can't get it together. Any help would be appreciated!

Answer 1

Other method with dplyr

library(dplyr)
df1 %>% group_by(geo) %>% mutate(m_var2=mean(var2), m_var3=mean(var3))

Answer 2

Another simple base R solution is just

transform(df, m_var2 = ave(var2, geo), m_var3 = ave(var3, geo))
#   time geo var1 var2 var3 var4 m_var2 m_var3
# 1 1990  AT    1    7   13   19      8     14
# 2 1991  AT    2    8   14   20      8     14
# 3 1992  AT    3    9   15   21      8     14
# 4 1990  DE    4   10   16   22     11     17
# 5 1991  DE    5   11   17   23     11     17
# 6 1992  DE    6   12   18   24     11     17

---

Couple years later, I think a more concise approach would be to both update the actual data set (instead of creating a new one) and operate on a vector of columns (instead of manually writing them)

vars <- paste0("var", 2:3) # Select desired cols
df[paste0("m_", vars)] <- lapply(df[vars], ave, df[["geo"]]) # Loop and update

Answer 3

One option would be to use data.table . We can convert the data.frame to data.table ( setDT(df1) ), get the mean ( lapply(.SD, mean) ) for the selected columns ('var2' and 'var3') by specifying the column index in .SDcols , grouped by 'geo'. Create new columns by assigning the output ( := ) to the new column names ( paste('m', names(df1)[4:5]) )

library(data.table)
setDT(df1)[, paste('m', names(df1)[4:5], sep="_") :=lapply(.SD, mean)
            ,by = geo, .SDcols=4:5]
#     time geo var1 var2 var3 var4 m_var2 m_var3
#1: 1990  AT    1    7   13   19      8     14
#2: 1991  AT    2    8   14   20      8     14
#3: 1992  AT    3    9   15   21      8     14
#4: 1990  DE    4   10   16   22     11     17
#5: 1991  DE    5   11   17   23     11     17
#6: 1992  DE    6   12   18   24     11     17

NOTE: This method is more general. We can create the mean columns even for 100s of variables without any major change in the code. ie. if we need to get the mean of columns 4:100, change the .SDcols=4:100 and in the paste('m', names(df1)[4:100] .

data

df1 <- structure(list(time = c(1990L, 1991L, 1992L, 1990L, 1991L, 1992L
), geo = c("AT", "AT", "AT", "DE", "DE", "DE"), var1 = 1:6, var2 = 7:12, 
var3 = 13:18, var4 = 19:24), .Names = c("time", "geo", "var1", 
"var2", "var3", "var4"), class = "data.frame", row.names = c("1", 
"2", "3", "4", "5", "6"))

Answer 4

在base R：

 cbind(df1,m_var2=ave(df1$var2,df1$geo),m_var3=ave(df1$var3,df1$geo))