长格式函数

时间:2012-11-02 11:24:30

标签: r dataframe aggregate

  

可能重复:
  faster way to create variable that aggregates a column by id

我在项目上遇到了麻烦。我创建了一个长格式的数据框(称为dat)(我复制了下面的前三行),我想计算一下2000年到2011年美国所有银行税前收入的平均值。我怎么办?去做?我几乎没有R的经验。如果答案太明显,我很抱歉,但我找不到任何东西,而且我已经花了很多时间在这个项目上。先感谢您!

KeyItem     Bank    Country Year    Value  
1   Pretax Income   WELLS_FARGO_&_COMPANY   UNITED STATES   2011    2.365600e+10                            
2   Total Assets    WELLS_FARGO_&_COMPANY   UNITED STATES   2011    1.313867e+12                            
3   Total Liabilities   WELLS_FARGO_&_COMPANY   UNITED STATES 2011  1.172180e+12                            

1 个答案:

答案 0 :(得分:1)

以下内容应该让您入门。你基本上需要做两件事:子集和聚合。我将演示基础R解决方案和data.table解决方案。

首先,一些样本数据。

set.seed(1) # So you can reproduce my results
dat <- data.frame(KeyItem = rep(c("Pretax", "TotalAssets", "TotalLiabilities"), 
                                times = 30),
                  Bank = rep(c("WellsFargo", "BankOfAmerica", "ICICI"), 
                             each = 30),
                  Country = rep(c("UnitedStates", "India"), times = c(60, 30)),
                  Year = rep(c(2000:2009), each = 3, times = 3),
                  Value = runif(90, min=300, max=600))

让我们将“Pretax”值的平均值按“国家”和“年份”汇总,但仅限于2001年至2005年。

aggregate(Value ~ Country + Year, 
          dat[dat$KeyItem == "Pretax" & dat$Year >= 2001 & dat$Year <=2005, ], 
          mean)
#         Country Year    Value
# 1         India 2001 399.7184
# 2  UnitedStates 2001 464.1638
# 3         India 2002 443.5636
# 4  UnitedStates 2002 560.8373
# 5         India 2003 562.5964
# 6  UnitedStates 2003 370.9591
# 7         India 2004 404.0050
# 8  UnitedStates 2004 520.4933
# 9         India 2005 567.6595
# 10 UnitedStates 2005 493.0583

data.table

中的情况与此相同
library(data.table)
DT <- data.table(dat, key = "Country,Bank,Year")
subset(DT, KeyItem == "Pretax")[Year %between% c(2001, 2005), 
    mean(Value), by = list(Country, Year)]
#          Country Year       V1
#  1:        India 2001 399.7184
#  2:        India 2002 443.5636
#  3:        India 2003 562.5964
#  4:        India 2004 404.0050
#  5:        India 2005 567.6595
#  6: UnitedStates 2001 464.1638
#  7: UnitedStates 2002 560.8373
#  8: UnitedStates 2003 370.9591
#  9: UnitedStates 2004 520.4933
# 10: UnitedStates 2005 493.0583