1. It is not restricted to work basically with numeric data matrices like the bigmemory set of packages but allows relatively easy access to character vectors through factors
2. It has efficient data loading functionality from flat/csv files, you can interact with SQL databases as explained here.
3. You don't need to use the sqldf package to get and insert all the time your data in the right (RAM-limited) sized to and from an SQLite database
4. It has higher level functionality (e.g. apply set of family, sorting, …) which package mmap seems to be lacking
5. And it allows you to work with datasets which you can not put in memory which the datatable package does not allow. 

1. Basic operations (c, unique, duplicated, ffmatch, ffdfmatch, %in%, is.na, all, any, cut, ffwhich, ffappend, ffdfappend)
2. Standard operators (+, -, *, /, ^, %%, %/%, ==, !=, <, <=, >=, >, &, |, !) on ff vectors
3. Math operators (abs, sign, sqrt, ceiling, floor, trunc, round, signif, log, log10, log2, log1p, exp, expm1, acos, acosh, asin, asinh, atan, atanh, cos, cosh, sin, sinh, tan, tanh, gamma, lgamma, digamma, trigamma)
4. Selections & data manipulations (subset, transform, with, within, ffwhich)
5. Summary statistics (sum, min, max, range, quantile, hist)
6. Data transformations (cumsum, cumprod, cummin, cummax, table, tabulate, merge, ffdfdply)

This makes it a lot more easy to work with the ff package and large data.

 

> require(ffbase)

> hhp <- read.table.ffdf(file="/home/jan/Work/RForgeBNOSAC/github/RBelgium_HeritageHealthPrize/Data/Claims.csv", FUN = "read.csv", na.strings = "")

> class(hhp)

[1] “ffdf”

> dim(hhp)

[1] 2668990      14

> str(hhp[1:10,])

'data.frame': 10 obs. of  14 variables:

 $ MemberID             : int  42286978 97903248 2759427 73570559 11837054 45844561 99829076 54666321 60497718 72200595

 $ ProviderID           : int  8013252 3316066 2997752 7053364 7557061 1963488 6721023 9932074 363858 6251259

 $ Vendor               : int  172193 726296 140343 240043 496247 4042 265273 35565 293107 791272

 $ PCP                  : int  37796 5300 91972 70119 68968 55823 91972 27294 64913 49465

 $ Year                 : Factor w/ 3 levels “Y1″,”Y2″,”Y3”: 1 3 3 3 2 3 1 1 2 3

 $ Specialty            : Factor w/ 12 levels “Anesthesiology”,..: 12 5 5 6 12 10 11 2 11 5

 $ PlaceSvc             : Factor w/ 8 levels “Ambulance”,”Home”,..: 5 5 5 3 7 5 5 5 5 5

 $ PayDelay             : Factor w/ 163 levels “0”,”10″,”101″,..: 52 76 29 48 51 49 40 53 67 82

 $ LengthOfStay         : Factor w/ 10 levels “1 day”,”2- 4 weeks”,..: NA NA NA NA NA NA NA NA NA NA

 $ DSFS                 : Factor w/ 12 levels “0- 1 month”,”10-11 months”,..: 11 10 1 8 7 6 1 1 4 10

 $ PrimaryConditionGroup: Factor w/ 45 levels “AMI”,”APPCHOL”,..: 26 26 21 21 10 26 38 33 19 22

 $ CharlsonIndex        : Factor w/ 4 levels “0”,”1-2″,”3-4″,..: 1 2 1 2 2 1 1 1 1 2

 $ ProcedureGroup       : Factor w/ 17 levels “ANES”,”EM”,”MED”,..: 3 2 2 7 2 2 3 5 2 7

 $ SupLOS               : int  0 0 0 0 0 0 0 0 0 0

> ## Some basic showoff

> result <- list()

> ## Unique members, Unique combination of members and health care providers, find unexpected duplicated records

> result$members <- unique(hhp$MemberID)

> result$members.providers <- unique(hhp[c("MemberID","ProviderID")])

> sum(duplicated(hhp[c(“MemberID”,”ProviderID”,”Year”,”DSFS”)]))

[1] 936859

> ## c operator

> sum(duplicated(c(result$members, result$members))) # == length(result$members)

[1] 113000

> ## Basic example of operators is.na.ff, the ! operator and sum.ff

> sum(!is.na(hhp$LengthOfStay))

[1] 71598

> sum(is.na(hhp$LengthOfStay))

[1] 2597392

> ## all and any

> any(is.na(hhp$LengthOfStay))

[1] TRUE

> all(!is.na(hhp$PayDelay))

[1] TRUE

> ## Frequency table of Specialities and example of a 2-way table

> result$speciality <- table.ff(hhp$Specialty, exclude=NA)

> options(scipen = 1)

> barplot(result$speciality[order(result$speciality)], col = “steelblue”, horiz = FALSE, cex.names=0.6, main=”Frequency table”)

> hhp$ProviderFactor <- with(data=hhp[c("ProviderID")], expr = as.character(ProviderID))

> result$providerspeciality <- table.ff(hhp$Specialty, hhp$ProviderFactor, exclude=NA)

> ## Let's see if the member id's are uniformly distributed

> hist(hhp$MemberID, col = “steelblue”, main = “MemberID's histogram”, xlab = “MemberID”)

> ## %in% operator is overloaded

> hhp$gp.laboratory <- hhp$Specialty %in% ff(factor(c("General Practice","Laboratory")))

> hhp$gp.laboratory

ff (open) logical length=2668990 (2668990)

      [1]       [2]       [3]       [4]       [5]       [6]       [7]       [8] 

    FALSE     FALSE     FALSE      TRUE     FALSE     FALSE     FALSE     FALSE 

          [2668983] [2668984] [2668985] [2668986] [2668987] [2668988] [2668989] 

        :     FALSE      TRUE     FALSE     FALSE     FALSE     FALSE     FALSE 

[2668990] 

    FALSE 

> ## Some data cleaning

> hhp$pdelay <- with(hhp[c("PayDelay")], as.numeric(PayDelay))

> ## Summary stats

> mean(hhp$pdelay)

[1] 59.78436

> range(hhp$pdelay)

[1]   1 163

> quantile(hhp$pdelay)

  0%  25%  50%  75% 100% 

   1   45   55   73  163 

> ## cumsum

> hist(cumsum.ff(hhp$pdelay), col = “steelblue”)

> max(cumsum.ff(hhp$pdelay))

[1] 159556245

> ## cut

> table.ff(cut(hhp$pdelay, breaks = c(-Inf, 1, 10, 100, +Inf)), exclude=NA)

  (-Inf,1]     (1,10]   (10,100] (100, Inf] 

    141451      46720    2237723     243096 

> ## apply a function to a group of data – ddply type of logic from package plyr

> hhp$MemberIDFactor <- with(data=hhp[c("MemberID")], expr = as.character(MemberID))

> require(doBy)

> result$delaybyperson <- ffdfdply(hhp[c("MemberID","pdelay")], split = hhp$MemberIDFactor, FUN=function(x){

+ summaryBy(pdelay ~ MemberID, data=x, FUN=sum, keep.names=FALSE)

+ }, trace=FALSE)

> result$delaybyperson[1:5,]

  MemberID pdelay.sum

1    18190       3158

2    20072       4915

3    20482       4189

4    21207       2548

5    32317       1064

> ## merging (in fact joining based on ffmatch or ffdfmatch)

> hhp <- merge(hhp, result$delaybyperson, by = "MemberID", all.x=TRUE, all.y=FALSE)

> names(hhp)

 [1] “MemberID”              “ProviderID”            “Vendor”               

 [4] “PCP”                   “Year”                  “Specialty”            

 [7] “PlaceSvc”              “PayDelay”              “LengthOfStay”         

[10] “DSFS”                  “PrimaryConditionGroup” “CharlsonIndex”        

[13] “ProcedureGroup”        “SupLOS”                “ProviderFactor”       

[16] “gp.laboratory”         “pdelay”                “MemberIDFactor”       

[19] “pdelay.sum”           

> ## Let's add a key (in version 0.6 of the package for download here)

> hhp$key <- key(hhp[c("MemberID","ProviderID")])

> max(hhp$key) == nrow(result$members.providers)

[1] TRUE

> ## A small example of operators

> idx <- ffwhich(hhp[c("Specialty","PlaceSvc")], Specialty == "General Practice")

> idx

ff (open) integer length=473655 (473655)

     [1]      [2]      [3]      [4]      [5]      [6]      [7]      [8] 

      18       19       20       31       35       42       45       62 

         [473648] [473649] [473650] [473651] [473652] [473653] [473654] 

       :  2668922  2668929  2668930  2668947  2668952  2668965  2668974 

[473655] 

 2668984 

> hhp.gp <- hhp[idx, ]

> class(hhp.gp)

[1] “ffdf”

> nrow(hhp.gp)

[1] 473655

> sum(is.na(idx))

[1] 0

> sum(hhp$Specialty == “General Practice”, na.rm=TRUE)

[1] 473655

> ## Or apply subset

> table.ff(hhp$Year, exclude=NA)

    Y1     Y2     Y3 

865689 898872 904429 

> hhp.y1 <- subset(hhp, hhp$Year == "Y1")

> nrow(hhp.y1)

[1] 865689

We welcome you to use the package if it suits your applications and if you have any requests, do post some comments to see how the package can be extended for your needs.