DataExplorer (version 0.8.3)

set_missing: Set all missing values to indicated value

Description

Quickly set all missing values to indicated value.

Usage

set_missing(data, value, exclude = NULL)

Arguments

data

input data, in data.table format only.

value

a single value or a list of two values to be set to. See 'Details'.

exclude

column index or name to be excluded.

Details

The class of value will determine what type of columns to be set, e.g., if value is 0, then missing values for continuous features will be set. When supplying a list of two values, only one numeric and one non-numeric is allowed.

This function updates data.table object directly. Otherwise, output data will be returned matching input object class.

Examples

# Load packages
library(data.table)

# Generate missing values in iris data
dt <- data.table(iris)
for (j in 1:4) set(dt, i = sample.int(150, j * 30), j, value = NA_integer_)
set(dt, i = sample.int(150, 25), 5L, value = NA_character_)

# Set all missing values to 0L and unknown
dt2 <- copy(dt)
set_missing(dt2, list(0L, "unknown"))

# Set missing numerical values to 0L
dt3 <- copy(dt)
set_missing(dt3, 0L)

# Set missing discrete values to unknown
dt4 <- copy(dt)
set_missing(dt4, "unknown")

# Set missing values excluding some columns
dt5 <- copy(dt)
set_missing(dt4, 0L, 1L:2L)
set_missing(dt4, 0L, names(dt5)[3L:4L])

# Return from non-data.table input
set_missing(airquality, 999999L)