Question

I have a a big dataframe in R that all looks about like this:

    name   amount   date1       date2  days_out year
    JEAN  318.5 1971-02-16 1972-11-27  650 days 1971
 GREGORY 1518.5       <NA>       <NA>   NA days 1971
    JOHN  318.5       <NA>       <NA>   NA days 1971
  EDWARD  318.5       <NA>       <NA>   NA days 1971
  WALTER  518.5 1971-07-06 1975-03-14 1347 days 1971
   BARRY 1518.5 1971-11-09 1972-02-09   92 days 1971
   LARRY  518.5 1971-09-08 1972-02-09  154 days 1971
   HARRY  318.5 1971-09-16 1972-02-09  146 days 1971
   GARRY 1018.5 1971-10-26 1972-02-09  106 days 1971

If someone’s days_out is less than 60, they get a 90% discount. 60-90, a 70% discount. I need to find out the discounted sum of all the amounts for each year. My utterly embarrassing workaround is to write a python script that writes an R script that reads like this for each relevant year:

tmp <- members[members$year==1971, ]
tmp90 <- tmp[tmp$days_out <= 60  & tmp$days_out > 0  & !is.na(tmp$days_out),  ]
tmp70 <- tmp[tmp$days_out <= 90  & tmp$days_out > 60 & !is.na(tmp$days_out),  ]
tmp50 <- tmp[tmp$days_out <= 120 & tmp$days_out > 90 & !is.na(tmp$days_out),  ]
tmp30 <- tmp[tmp$days_out <= 180 & tmp$days_out >120 & !is.na(tmp$days_out),  ]
tmp00 <- tmp[tmp$days_out > 180 | is.na(tmp$days_out), ]
details.1971 <- c(1971, nrow(tmp),
  nrow(tmp90), sum(tmp90$amount), sum(tmp90$amount) * .9,
    nrow(tmp70), sum(tmp70$amount), sum(tmp70$amount) * .7,
    nrow(tmp50), sum(tmp50$amount), sum(tmp50$amount) * .5,
    nrow(tmp30), sum(tmp30$amount), sum(tmp90$amount) * .9,
    nrow(tmp00), sum(tmp00$amount))
membership.for.chart <- rbind(membership.for.chart,details.1971)

It works just fine. The tmp frames and vectors get overwritten which is fine. But I know that I’ve utterly defeated everything that is elegant and efficient about R here. I launched R for the first time a month ago and I think I’ve come a long way. But I would really like to know how I should have gone about this?

Answer 1

Wow, you wrote a Python script that generates an R script? Consider my eyebrows raised…

Hopefully this will get you started:

#Import your data; add dummy column to separate 'days' suffix into its own column
dat <- read.table(text = "   name   amount   date1       date2  days_out dummy year
    JEAN  318.5 1971-02-16 1972-11-27  650 days 1971
 GREGORY 1518.5       <NA>       <NA>   NA days 1971
    JOHN  318.5       <NA>       <NA>   NA days 1971
  EDWARD  318.5       <NA>       <NA>   NA days 1971
  WALTER  518.5 1971-07-06 1975-03-14 1347 days 1971
   BARRY 1518.5 1971-11-09 1972-02-09   92 days 1971
   LARRY  518.5 1971-09-08 1972-02-09  154 days 1971
   HARRY  318.5 1971-09-16 1972-02-09  146 days 1971
   GARRY 1018.5 1971-10-26 1972-02-09  106 days 1971",header = TRUE,sep = "")

#Repeat 3 times
df <- rbind(dat,dat,dat)

#Create new year variable
df$year <- rep(1971:1973,each = nrow(dat))

#Breaks for discount levels
ct <- c(0,60,90,120,180,Inf)

#Cut into a factor
df$fac <- cut(df$days_out,ct)

#Create discount amounts for each row
df$discount <- c(0.9,0.7,0.5,0.9,1)[df$fac]
df$discount[is.na(df$discount)] <- 1

#Calc adj amount
df$amount_adj <- with(df,amount * discount)

#I use plyr a lot, but there are many, many
# alternatives
library(plyr)
ddply(df,.(year),summarise,
            amt = sum(amount_adj),
            total = length(year),
            d60 = length(which(fac == "(0,60]")))

I only calculated a few of your summary values in the last ddply command. I’m assuming you can extend it yourself.