memisc: recode – R documentation

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recode

Recode Items, Factors and Numeric Vectors

Description

recode substitutes old values of a factor or a numeric vector by new ones, just like the recoding facilities in some commercial statistical packages.

Usage

recode(x,...,
       copy=getOption("recode_copy",identical(otherwise,"copy")),
       otherwise=NA)
## S4 method for signature 'vector'
recode(x,...,
    copy=getOption("recode_copy",identical(otherwise,"copy")),
    otherwise=NA)
## S4 method for signature 'factor'
recode(x,...,
    copy=getOption("recode_copy",identical(otherwise,"copy")),
    otherwise=NA)
## S4 method for signature 'item'
recode(x,...,
    copy=getOption("recode_copy",identical(otherwise,"copy")),
    otherwise=NA)

Arguments

`x`	An object
`...`	One or more assignment expressions, each of the form `new.value <- old.values`. `new.value` should be a scalar numeric value or character string. If one of the `new.value`s is a character string, the return value of `recode` will be a factor and each `new.value` will be coerced to a character string that labels a level of the factor. Each `old.value` in an assignment expression may be a (numeric or character) vector. If `x` is numeric such an assignment expression may have the form `new.value <- range(lower,upper)` In that case, values between `lower` and `upper` are exchanged by `new.value`. If one of the arguments to `range` is `min`, it is substituted by the minimum of `x`. If one of the arguments to `range` is `max`, it is substituted by the maximum of `x`. In case of the method for `labelled` vectors, the tags of arguments of the form `tag = new.value <- old.values` will define the labels of the new codes. If the `old.values` of different assignment expressions overlap, an error will be raised because the recoding is ambigous.
`copy`	logical; should those values of `x` not given an explicit new code copied into the resulting vector?
`otherwise`	a character string or some other value that the result may obtain. If equal to `NA` or `"NA"`, original codes not given an explicit new code are recoded into `NA`. If equal to `"copy"`, original codes not given an explicit new code are copied.

Details

recode relies on the lazy evaluation mechanism of R: Arguments are not evaluated until required by the function they are given to. recode does not cause arguments that appear in ... to be evaluated. Instead, recode parses the ... arguments. Therefore, although expressions like 1 <- 1:4 would cause an error action, if evaluated at any place elsewhere in R, they will not cause an error action, if given to recode as an argument. However, a call of the form recode(x,1=1:4), would be a syntax error.

If John Fox' package "car" is installed, recode will also be callable with the syntax of the recode function of that package.

Value

A numerical vector, factor or an item object.

Examples

x <- as.item(sample(1:6,20,replace=TRUE),
        labels=c( a=1,
                  b=2,
                  c=3,
                  d=4,
                  e=5,
                  f=6))
print(x)

# A recoded version of x is returned
# containing the values 1, 2, 3, which are
# labelled as "A", "B", "C".
recode(x,
  A = 1 <- range(min,2),
  B = 2 <- 3:4,
  C = 3 <- range(5,max), # this last comma is ignored
  )

# This causes an error action: the sets
# of original values overlap.
try(recode(x,
  A = 1 <- range(min,2),
  B = 2 <- 2:4,
  C = 3 <- range(5,max)
  ))

recode(x,
  A = 1 <- range(min,2),
  B = 2 <- 3:4,
  C = 3 <- range(5,6),
  D = 4 <- 7
  )
  
# This results in an all-missing vector:
recode(x,
  D = 4 <- 7,
  E = 5 <- 8
  )

f <- as.factor(x)
x <- as.integer(x)

recode(x,
  1 <- range(min,2),
  2 <- 3:4,
  3 <- range(5,max)
  )

# This causes another error action:
# the third argument is an invalid
# expression for a recoding.
try(recode(x,
  1 <- range(min,2),
  3:4,
  3 <- range(5,max)
  ))

# The new values are character strings,
# therefore a factor is returned.
recode(x,
  "a" <- range(min,2),
  "b" <- 3:4,
  "c" <- range(5,6)
  )
  
recode(x,
  1 <- 1:3,
  2 <- 4:6
  )
  
recode(x,
  4 <- 7,
  5 <- 8,
  otherwise = "copy"
  )

recode(f,
  "A" <- c("a","b"),
  "B" <- c("c","d"),
  otherwise="copy"
  )

recode(f,
  "A" <- c("a","b"),
  "B" <- c("c","d"),
  otherwise="C"
  )
 
recode(f,
  "A" <- c("a","b"),
  "B" <- c("c","d")
  )

DS <- data.set(x=as.item(sample(1:6,20,replace=TRUE),
        labels=c( a=1,
                  b=2,
                  c=3,
                  d=4,
                  e=5,
                  f=6)))
print(DS)

DS <- within(DS,{
    xf <- recode(x,
                 "a" <- range(min,2),
                 "b" <- 3:4,
                 "c" <- range(5,6)
                 )
    xn <- x@.Data
    xc <- recode(xn,
                 "a" <- range(min,2),
                 "b" <- 3:4,
                 "c" <- range(5,6)
                 )
    xc <- as.character(x)
    xcc <- recode(xc,
                  1 <- letters[1:2],
                  2 <- letters[3:4],
                  3 <- letters[5:6]
                  )
})

DS

DS <- within(DS,{
    xf <- recode(x,
                 "a" <- range(min,2),
                 "b" <- 3:4,
                 "c" <- range(5,6)
                 )
    x1 <- recode(x,
                 1 <- range(1,2),
                 2 <- range(3,4),
                 copy=TRUE
                 )
    xf1 <- recode(x,
                 "A" <- range(1,2),
                 "B" <- range(3,4),
                 copy=TRUE
                 )
})
DS
codebook(DS)

DF <- data.frame(x=rep(1:6,4,replace=TRUE))
DF <- within(DF,{
    xf <- recode(x,
                 "a" <- range(min,2),
                 "b" <- 3:4,
                 "c" <- range(5,6)
                 )
    x1 <- recode(x,
                 1 <- range(1,2),
                 2 <- range(3,4),
                 copy=TRUE
                 )
    xf1 <- recode(x,
                 "A" <- range(1,2),
                 "B" <- range(3,4),
                 copy=TRUE
                 )
    xf2 <- recode(x,
                 "B" <- range(3,4),
                 "A" <- range(1,2),
                 copy=TRUE
                 )
})
DF
codebook(DF)

memisc

Management of Survey Data and Presentation of Analysis Results

v0.99.27.3

GPL-2

Authors

Martin Elff (with contributions from Christopher N. Lawrence, Dave Atkins, Jason W. Morgan, Achim Zeileis)

Initial release

2020-11-18