从data.frame中的现有变量创建几个新的派生变量 [英] Create several new derived variables from existing variables in data.frame

问题描述

在R中，我有一个data.frame，它有几个变量，这些变量已经在几年内被测量了。我想得出每个变量的月平均值（使用所有年份）。理想情况下，这些新的变量将全部在一个新的数据框架（携带ID）中，下面我只是将新的变量添加到data.frame。我现在知道如何做到这一点的唯一方法（下面）似乎是非常费力的，我希望在R中可能有一个更聪明的方式来做，这不需要像每个月一样打字和变量。

In R I have a data.frame that has several variables that have been measured monthly over several years. I would like to derive the monthly average (using all years) for each variable. Ideally these new variables would all be together in a new data.frame (carrying over the ID), below I am simply adding the new variable to the data.frame. The only way I know how to do this at the moment (below) seems quite laborious, and I was hoping there might be a smarter way to do this in R, that would not require typing out each month and variable as I did below.

# Example data.frame with only two years, two month, and two variables
# In the real data set there are always 12 months per year 
# and there are at least four variables
df<- structure(list(ID = 1:4, ABC.M1Y2001 = c(10, 12.3, 45, 89), ABC.M2Y2001 = c(11.1, 
          34, 67.7, -15.6), ABC.M1Y2002 = c(-11.1, 9, 34, 56.5), ABC.M2Y2002 = c(12L,
          13L, 11L, 21L), DEF.M1Y2001 = c(14L, 14L, 14L, 16L), DEF.M2Y2001 = c(15L,
          15L, 15L, 12L), DEF.M1Y2002 = c(5, 12, 23.5, 34), DEF.M2Y2002 = c(6L,
          34L, 61L, 56L)), .Names = c("ID", "ABC.M1Y2001", "ABC.M2Y2001","ABC.M1Y2002", 
          "ABC.M2Y2002", "DEF.M1Y2001", "DEF.M2Y2001", "DEF.M1Y2002", 
          "DEF.M2Y2002"), class = "data.frame", row.names = c(NA, -4L))


# list variable to average for ABC Month 1 across years
ABC.M1.names <- c("ABC.M1Y2001", "ABC.M1Y2002") 
df <- transform(df,  ABC.M1 = rowMeans(df[,ABC.M1.names], na.rm = TRUE))

# list variable to average for ABC Month 2 across years
ABC.M2.names <- c("ABC.M2Y2001", "ABC.M2Y2002") 
df <- transform(df,  ABC.M2 = rowMeans(df[,ABC.M2.names], na.rm = TRUE))

# and so forth for ABC
# ...

# list variables to average for DEF Month 1 across years
DEF.M1.names <- c("DEF.M1Y2001", "DEF.M1Y2002") 
df <- transform(df,  DEF.M1 = rowMeans(df[,DEF.M1.names], na.rm = TRUE))

# and so forth for DEF
# ...

推荐答案

这里是一个使用 reshape2 ，当您有大量数据并使用正则表达式来提取变量名称和月份时，它将更加自动化。此解决方案将为您提供一个不错的汇总表。

Here is a solution using reshape2 that is more automated when you have lots of data and uses regular expressions to extract the variable name and the month. This solution will give you a nice summary table.

#  Load required package
require(reshape2)

#  Melt your wide data into long format
mdf <- melt(df , id = "ID" )

#  Extract relevant variable names from the variable colum
mdf$Month <- gsub( "^.*\\.(M[0-9]{1,2}).*$" , "\\1" , mdf$variable )
mdf$Var <- gsub( "^(.*)\\..*" , "\\1" , mdf$variable )   

#  Aggregate by month and variable
dcast( mdf , Var ~ Month , mean  )
#  Var      M1     M2
#1 ABC 30.5875 19.275
#2 DEF 16.5625 26.750

或与其他解决方案兼容，并返回表格 ID ...

Or to be compatible with the other solutions, and return the table by ID as well...

dcast( mdf , ID ~ Var + Month , mean  )
#  ID ABC_M1 ABC_M2 DEF_M1 DEF_M2
#1  1  -0.55  11.55   9.50   10.5
#2  2  10.65  23.50  13.00   24.5
#3  3  39.50  39.35  18.75   38.0
#4  4  72.75   2.70  25.00   34.0

这篇关于从data.frame中的现有变量创建几个新的派生变量的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！