misty (version 0.8.3)

df.duplicated: Extract Duplicated or Unique Rows

Description

The function df.duplicated extracts duplicated rows and the function df.unique extracts unique rows from a matrix or data frame.

Usage

df.duplicated(data, ..., first = TRUE, keep.all = TRUE, from.last = FALSE,
              keep.row.names = TRUE, check = TRUE)
df.unique(data, ..., keep.all = TRUE, from.last = FALSE,
          keep.row.names = TRUE, check = TRUE)

Value

Returns duplicated or unique rows of the data frame in ... or data.

Arguments

data

a data frame.

...

an expression indicating the variable names in data used to determine duplicated or unique rows.e.g., df.duplicated(x1, x2, data = dat). Note that the operators ., +, -, ~, :, ::, and ! can also be used to select variables, see Details in the df.subset function.

first

logical: if TRUE (default), the df.duplicated() function will return duplicated rows including the first of identical rows.

keep.all

logical: if TRUE (default), the function will return all variables in data after extracting duplicated or unique rows based on the variables specified in the argument ....

from.last

logical: if TRUE, duplication will be considered from the reversed side, i.e., the last of identical rows would correspond to duplicated = FALSE. Note that this argument is only used when first = FALSE.

keep.row.names

logical: if TRUE (default), the row names from data are kept, otherwise they are set to NULL.

check

logical: if TRUE (default), argument specification is checked.

Author

Takuya Yanagida

Details

Note that df.unique(x) is equivalent to unique(x). That is, the main difference between the df.unique() and the unique() function is that the df.unique() function provides the ... argument to specify a variable or multiple variables which are used to determine unique rows.

References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

See Also

df.check, df.head, df.tail, df.long, df.wide, df.merge, df.move, df.rbind, df.rename, df.sort, df.subset

Examples

dat <- data.frame(x1 = c(1, 1, 2, 1, 4), x2 = c(1, 1, 2, 1, 6),
                  x3 = c(2, 2, 3, 2, 6), x4 = c(1, 1, 2, 2, 4),
                  x5 = c(1, 1, 4, 4, 3))

#————————————————————————————————————————————————————————————————————————————
# df.duplicated() Function

# Example 1a: Extract duplicated rows based on all variables
df.duplicated(dat)

# Example 1b: Extract duplicated rows based on 'x4'
df.duplicated(dat, x4)

# Example 1c: Extract duplicated rows based on 'x2' and 'x3'
df.duplicated(dat, x2, x3)

# Example 1d: Extract duplicated rows based on all variables
# exclude first of identical rows
df.duplicated(dat, first = FALSE)

# Example 1e: Extract duplicated rows based on 'x2' and 'x3'
# do not return all variables
df.duplicated(dat, x2, x3, keep.all = FALSE)

# Example 1f: Extract duplicated rows based on 'x4'
# consider duplication from the reversed side
df.duplicated(dat, x4, first = FALSE, from.last = TRUE)

# Example 1g: Extract duplicated rows based on 'x2' and 'x3'
# set row names to NULL
df.duplicated(dat, x2, x3, keep.row.names = FALSE)

#————————————————————————————————————————————————————————————————————————————
# df.unique() Function

# Example 2a: Extract unique rows based on all variables
df.unique(dat)

# Example 2b: Extract unique rows based on 'x4'
df.unique(dat, x4)

# Example 2c: Extract unique rows based on 'x1', 'x2', and 'x3'
df.unique(dat, x1, x2, x3)

# Example 2d: Extract unique rows based on 'x2' and 'x3'
# do not return all variables
df.unique(dat, x2, x3, keep.all = FALSE)

# Example 2e: Extract unique rows based on 'x4'
# consider duplication from the reversed side
df.unique(dat, x4, from.last = TRUE)

# Example 2f: Extract unique rows based on 'x2' and 'x3'
# set row names to NULL
df.unique(dat, x2, x3, keep.row.names = FALSE)