Question

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faster way to create variable that aggregates a column by id

I am having trouble with a project. I created a dataframe (called dat) in long format (i copied the first 3 rows below) and I want to calculate for example the mean of the Pretax Income of all Banks in the United States for the years 2000 to 2011. How would I do that? I have hardly any experience in R. I am sorry if the answer is too obvious, but I couldn’t find anything and i already spent a lot of time on the project. Thank you in advance!

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                            

Answer 1

The following should get you started. You basically need to do two things: subset, and aggregate. I’ll demonstrate a base R solution and a data.table solution.

First, some sample data.

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))

Let’s aggregate mean of the “Pretax” values by “Country” and “Year”, but only for the years 2001 to 2005.

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

Here’s the same thing in data.table

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