SSBtools (version 1.7.0)

HierarchicalWildcardGlobbing: Find variable combinations by advanced wildcard/globbing specifications.

Description

Find combinations present in an input data frame or, when input is a list, find all possible combinations that meet the requirements.

Usage

HierarchicalWildcardGlobbing(
  z,
  wg,
  useUnique = NULL,
  useFactor = FALSE,
  makeWarning = TRUE,
  printInfo = FALSE,
  useMatrixToDataFrame = TRUE
)

Value

data.frame

Arguments

z

list or data.frame

wg

data.frame with data globbing and wildcards

useUnique

Logical variable about recoding within the algorithm. By default (NULL) an automatic decision is made.

useFactor

When TRUE, internal factor recoding is used.

makeWarning

When TRUE, warning is made in cases of unused variables. Only variables common to z and wg are used.

printInfo

When TRUE, information is printed during the process.

useMatrixToDataFrame

When TRUE, special functions (DataFrameToMatrix/MatrixToDataFrame) for improving speed and memory is utilized.

Author

Øyvind Langsrud

Details

The final variable combinations must meet the requirements in each positive sign group and must not match the requirements in the negative sign groups.The function is implemented by calling WildcardGlobbing several times within an algorithm that uses hierarchical clustering (hclust).

Examples

                  
# useUnique=NULL betyr valg ut fra antall rader i kombinasjonsfil
data(precip)
data(mtcars)
codes <- as.character(c(100, 200, 300, 600, 700, 101, 102, 103, 104, 134, 647, 783, 
                        13401, 13402, 64701, 64702))


# Create list input
zList <- list(car = rownames(mtcars), wt = as.character(1000 * mtcars$wt), 
              city = names(precip), code = codes)

# Create data.frame input
m <- cbind(car = rownames(mtcars), wt = as.character(1000 * mtcars$wt))
zFrame <- data.frame(m[rep(1:NROW(m), each = 35), ], 
                     city = names(precip), code = codes, stringsAsFactors = FALSE)

# Create globbing/wildcards input
wg <- data.frame(rbind(c("Merc*", ""    , ""    , "?00"  ), 
                       c("F*"   , ""    , ""    , "?????"), 
                       c(""     , "???0", "C*"  , ""     ), 
                       c(""     , ""    , "!Co*", ""     ), 
                       c(""     , ""    , "?i*" , "????2"), 
                       c(""     , ""    , "?h*" , "????1")), 
           sign = c("+", "+", "+", "+", "-", "-"), stringsAsFactors = FALSE)
names(wg)[1:4] <- names(zList)



# =================================================================== 
#   Finding unique combinations present in the input data frame
# ===================================================================


# Using first row of wg. Combinations of car starting with Merc 
# and three-digit code ending with 00
HierarchicalWildcardGlobbing(zFrame[, c(1, 4)], wg[1, c(1, 4, 5)])

# Using first row of wg. Combinations of all four variables
HierarchicalWildcardGlobbing(zFrame, wg[1, ])

# More combinations when using second row also
HierarchicalWildcardGlobbing(zFrame, wg[1:2, ])

# Less combinations when using third row also 
# since last digit of wt must be 0 and only cities starting with C
HierarchicalWildcardGlobbing(zFrame, wg[1:3, ])


# Less combinations when using fourth row also since city cannot start with Co
HierarchicalWildcardGlobbing(zFrame, wg[1:4, ])

# Less combinations when using fourth row also 
# since specific combinations of city and code are removed
HierarchicalWildcardGlobbing(zFrame, wg)


# =================================================================== 
#  Using list input to create all possible combinations
# ===================================================================

dim(HierarchicalWildcardGlobbing(zList, wg))

# same result with as.list since same unique values of each variable
dim(HierarchicalWildcardGlobbing(as.list(zFrame), wg))