Hmisc (version 4.4-0)

ffCompress: Transform Data Frame Into a Compact ff Object

Description

The ff package implements a wide variety of atomic data types down to 2 bits, allowing for compact storage of large datasets and requiring memory usage in R for only those rows and columns of the dataset that are needed at one time. It is best to create a compact ffdf data frame object while initially reading the external data file, using for example an input .csv file and specifying the ff vmodes for all the columns. If you can get enough memory to read the entire dataset into an R data.frame you can use ffCompress after the fact to analyze the data frame and use the most compact data representation possible. This entails using single precision for floating point numbers (which can be easily overridden to use R's usual double precision) and a variety of integer types depend on the number of bits used by the maximum absolute value of the variable, whether or not NAs exist in the data, and whether or not negative values are possible.

Since ff does not allow variable labels and units, any such attributes are stripped off of variables and stored as attributes on the entire ffdf object. An as.data.frame and subscripting method retrieves these attributes and restores them to individual variables when needed.

Usage

ffCompress(obj, float=c('single', 'double'), print=FALSE)
# S3 method for ffdflabel
as.data.frame(x, …)

Arguments

obj

a data frame

float

representation to use for floating point vectors. The default is single precision (4 bytes, 7 significant digits). Specify float='double' to use double precision (8 bytes, 15 significant digits)

print

set to TRUE to get progress output and passed as the VERBOSE argument to ff functions

x

an ffdf object

…

ignored

Value

an ffdf object for ffCompress, a data.frame for as.data.frame, and either one of these for subscripting. If subscripting results in a single variable and drop=FALSE is not specified, the result is of ff type.

See Also

cleanup.import

Examples

# NOT RUN {
require(ff)
require(survival)
n <- 1e6
d <- data.frame(x=rnorm(n), y=sample(0:1, n, TRUE),
                i=as.Date('2013-01-02'), S=Surv(runif(n)),
                z=factor(sample(1:3, n, TRUE), 1:3,
                  c('elephant','giraffe','dog')))
## Cannot have labels for variables; ff will reject as non-atomic vectors
storage.mode(d$y)
object.size(d)
n * (8 + 4 + 4 + 4)
f <- as.ffdf(d, vmode=c('single', 'quad', 'integer', 'single', 'quad'))
vmode(f)
n * (4 + 0.25 + 4 + 0.25)
object.size(as.data.frame(f))
f[1:10,]
hist(d[,'x'] - f[,'x'], nclass=100)
table(d[,'z'], f[,'z'])

system.time(subset(f, z == 'dog'))
system.time({i <- ffwhich(f, z == 'dog'); f[i,]})
table(subset(f, z == 'dog')[,'z'])
class(subset(f, z == 'dog'))

ffsave(f, file='/tmp/f')  # creates /tmp/f.ffData /tmp/f.RData
## To load: ffload('/tmp/f')

d <- upData(d, labels=c(y='Y'), units=c(z='units z'))
f <- ffCompress(d)
vmode(f)

load('ras.rda')   # dataset is not available
r <- ffCompress(ras)
vmode(r)
attr(r, 'label')
attr(r, 'units')
all.equal(ras, as.data.frame(r))
dr <- as.data.frame(r)
g <- function(x) names(attributes(x))
nam <- names(dr)
for(i in 1 : ncol(dr)) {
  a <- ras[[i]]
  b <- dr[[i]]
  cat(nam[i], '\n')
  cat(g(a), '\n', g(b), '\n')
  cat(max(w <- abs(unclass(a) - unclass(b)), na.rm=TRUE), '\n')
  if(nam[i] == 'ldl') {
    j <- which.max(abs(w))
    cat(a[j], b[j], '\n')
  }
}

dr <- as.data.frame(r)
xless(contents(dr))
xless(contents(r[1:10,]))
xless(contents(r[,1:10]))

table(r[, 'gender'])
## subset invokes [] so uses method from ffdflabel
m <- subset(r, gender == 'Male')
class(m)
dim(m)
attr(m, 'label')
attributes(m[,'age'])
df <- as.data.frame(m)
class(df$age)
label(df$age)
## But if subset again things are not OK
k <- subset(m, age < 3)
class(k)
contents(k[, 'age', drop=FALSE])
invisible(ffsave(r, file='/tmp/r'))

## w <- read.csv.ffdf(file='/tmp/data.csv', first.rows=10000)
## table(vmode(w))

## From ff manual:  vmode definitions
# boolean 1 bit logical without NA
# logical 2 bit logical with NA
# quad 2 bit unsigned integer without NA
# nibble 4 bit unsigned integer without NA
# byte 8 bit signed integer with NA
# ubyte 8 bit unsigned integer without NA
# short 16 bit signed integer with NA
# ushort 16 bit unsigned integer without NA
# integer 32 bit signed integer with NA
# single 32 bit float
# double 64 bit float
# complex 2x64 bit float
# raw 8 bit unsigned char
# character character
# }