Package 'dedupewider'

Dedupe across multiple columns

Description

Collapse many rows connected by duplicated data (which can exist in different rows and columns) into one, based on data in chosen columns, optionally putting non-consistent data into newly created additional columns.

Usage

dedupe_wide(
  x,
  cols_dedupe,
  cols_expand = NULL,
  max_new_cols = NULL,
  enable_drop = TRUE
)

Arguments

x	A data.frame without column named '....idx' and any column which ends by four dots and number (e.g. 'column....2').
cols_dedupe	A character vector of length min. 2 of columns' names in x used to dedupe. Deduplicated data from these columns will be saved into new columns, number of which is control by max_new_cols.
cols_expand	A character vector of columns' names in x or NULL (means: none except those used to dedupe) indicating columns with data to keep in case of non-consistent data, i.e. unique data from these columns will be saved into new columns, number of which is control by max_new_cols.
max_new_cols	A numeric vector length 1 or NULL (means: without limit) indicating how many new columns can be created to store unique data from columns passed to cols_dedupe and each column passed to cols_expand. Cannot be lower than 1.
enable_drop	A logical vector length 1: should given column be dropped if (after deduplication) contains only missing data (NA)? Applicable only to columns used to dedupe.

Details

Columns passed to cols_dedupe must be atomic.

Row names will always be removed. If you want to preserve row names, simply put in into separate column. Note that if this column won't be passed to cols_expand argument, only the one row name for duplicated rows will be preserved (row name closest to the top of the table).

Although duplicated or unique treats missing data (NA) as duplicated data, this function do not do this (see second example below).

Type of columns passed to cols_dedupe will be coerced to the most general type.

Value

If duplicated data found - data.frame with changed columns' names and optionally additional columns (in some cases less columns, depends on enable_drop argument). Otherwise data.frame without changes (except row names removed).

Note

To enable parallel computation, call setDTthreads before calling dedupe_wide function.

Examples

x <- data.frame(tel_1 = c(111, 222, 444, 555),
                tel_2 = c(222, 666, 666, 555),
                name = paste0("name", 1:4))
# rows 1, 2, 3 share the same phone numbers

dedupe_wide(x,
           cols_dedupe = c("tel_1", "tel_2"),
           cols_expand = "name")
# first three collapsed into one, for name4 kept only one phone number (555)
# 'name1', 'name2', 'name3' kept in new columns

y <- data.frame(tel_1 = c(777, 888, NA, NA),
                tel_2 = c(888, 777, NA, NA),
                name = paste0("name", 5:8))
# rows 3 and 4 has only missing data

dedupe_wide(y,
           cols_dedupe = c("tel_1", "tel_2"),
           cols_expand = "name")
# first two rows collapsed into one, nothing change for the rest of rows

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Move NA across columns or rows

Description

For chosen columns, move NA to right or left (i.e. across columns) or to top or bottom (i.e. across rows).

Usage

na_move(data, cols = names(data), direction = "right")

Arguments

data	A data.frame without column named "....idx".
cols	A character vector of columns' names in data across which function will be performed. If NULL, first column in data will be used. By default all columns will be used.
direction	A character vector of length 1 indicating where to move NA. Can be one of "top", "right", "bottom", "left". If NULL and also by default, "right" direction will be used.

Value

A data.frame with only these attributes preserved, which are returned by attributes function used on object passed to data parameter.

Type of columns passed to cols will be coerced to the most general type, although sometimes when column will contain only NA, that column will be of type logical.

Note

To enable parallel computation, call setDTthreads before calling na_move function.

Examples

data <- data.frame(col1 = c(1, 2, 3),
                   col2 = c(NA, NA, 4),
                   col3 = c(5, NA, NA),
                   col4 = c(6, 7, 8))
data
na_move(data, c("col2", "col3", "col4"), direction = "right")

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