rmmissing

Remove missing entries

Syntax

R = rmmissing(A)

R = rmmissing(A,dim)

R = rmmissing(___,Name,Value)

[R,TF] = rmmissing(___)

Description

R = rmmissing(A) removes missing entries from an array or table. If A is a vector, then rmmissing removes any entry that contains missing data. If A is a matrix or table, then rmmissing removes any row that contains missing data. Missing values are defined according to the data type of A:

• NaN — double, single, duration, and calendarDuration

• NaT — datetime

• <missing> — string

• <undefined> — categorical

• ' ' — char

• {''} — cell of character arrays

R = rmmissing(A,dim) specifies the dimension of A to operate along. By default, rmmissing operates along the first dimension whose size does not equal 1.

R = rmmissing(___,Name,Value) specifies additional parameters for removing missing entries using one or more name-value pair arguments. For example, you can use rmmissing(A,'MinNumMissing',n) to remove rows of A that contain at least n missing values.

[R,TF] = rmmissing(___) also returns a logical vector corresponding to the rows or columns of A that were removed.

Examples

Vector with NaN Values

Create a vector with NaN values and remove each NaN.

A = [1 3 NaN 6 NaN];
R = rmmissing(A)

R = 1×3

     1     3     6

Table with Multiple Data Types

Remove incomplete rows from a table with multiple data types.

First, create a table whose variables include categorical, double, and char data types.

A = table(categorical({'';'F';'M'}),[45;32;NaN],{'';'CA';'MA'},[6051;7234;NaN],...
    'VariableNames',{'Gender' 'Age' 'State' 'ID'})

A=3×4 table
      Gender       Age      State        ID 
    ___________    ___    __________    ____

    <undefined>     45    {0x0 char}    6051
    F               32    {'CA'    }    7234
    M              NaN    {'MA'    }     NaN

Remove any row of the table that contains missing data.

R = rmmissing(A)

R=1×4 table
    Gender    Age    State      ID 
    ______    ___    ______    ____

      F       32     {'CA'}    7234

Only remove rows with missing values in the Age or ID table variables.

R = rmmissing(A,'DataVariables',{'Age','ID'})

R=2×4 table
      Gender       Age      State        ID 
    ___________    ___    __________    ____

    <undefined>    45     {0x0 char}    6051
    F              32     {'CA'    }    7234

Alternatively, use the isnumeric function to identify the numeric variables to operate on.

R = rmmissing(A,'DataVariables',@isnumeric)

R=2×4 table
      Gender       Age      State        ID 
    ___________    ___    __________    ____

    <undefined>    45     {0x0 char}    6051
    F              32     {'CA'    }    7234

Matrix with Missing Data

Create a matrix with missing data and remove any column (second dimension) containing two or more missing values. Return the new matrix and the logical row vector that indicates which columns of A were removed.

A = [NaN NaN 5 3 NaN 5 7 NaN 9 2;
     8 9 NaN 1 4 5 6 5 NaN 5;
     NaN 4 9 8 7 2 4 1 NaN 3]

A = 3×10

   NaN   NaN     5     3   NaN     5     7   NaN     9     2
     8     9   NaN     1     4     5     6     5   NaN     5
   NaN     4     9     8     7     2     4     1   NaN     3

[R,TF] = rmmissing(A,2,'MinNumMissing',2)

R = 3×8

   NaN     5     3   NaN     5     7   NaN     2
     9   NaN     1     4     5     6     5     5
     4     9     8     7     2     4     1     3

TF = 1x10 logical array

   1   0   0   0   0   0   0   0   1   0

Input Arguments

A — Input data

vector | matrix | cell array of character vectors | table | timetable

Input data, specified as a vector, matrix, table, or timetable. If A is a timetable, then rmmissing(A) removes any row of A containing missing data and also removes the corresponding time vector element. If the time vector contains a NaT or NaN, then rmmissing(A) removes it from the time vector and also removes the corresponding row of A.

Data Types: double | single | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | logical | char | string | cell | table | timetable | categorical | datetime | duration | calendarDuration

dim — Dimension to operate along

1 | 2

Dimension to operate along, specified as 1 or 2. By default, rmmissing operates along the first dimension whose size does not equal 1.

Consider a two-dimensional input array A.

• If dim=1, then rmmissing removes rows of A.

  

• If dim=2, then rmmissing removes columns of A.

  

Data Types: double | single | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: rmmissing(A,'DataVariables',{'Temperature','Altitude'}) removes rows of A that contain missing data in the Temperature or Altitude variables

'MinNumMissing' — Minimum number missing

non-negative scalar

Minimum number of missing entries required to remove a row or column, specified as the comma-separated pair consisting of 'MinNumMissing' and a non-negative scalar, which is 1 by default.

Example: 'MinNumMissing',6

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

'DataVariables' — Table variables to operate on

scalar | vector | cell array | function handle | table vartype subscript

Table variables to operate on, specified as the comma-separated pair consisting of 'DataVariables' and one of the options in this table. The 'DataVariables' value indicates which variables of the input table to examine for missing values. Other variables in the table not specified by 'DataVariables' pass through to the output without being examined for missing values. When operating on the rows of A, rmmissing removes any row that has missing data in the columns corresponding to the variables specified. When operating on the columns of A, rmmissing removes the specified variables from the table.

Option	Description	Examples
Variable name	A character vector or scalar string specifying a single table variable name	'Var1' "Var1"
Vector of variable names	A cell array of character vectors or string array where each element is a table variable name	{'Var1' 'Var2'} ["Var1" "Var2"]
Scalar or vector of variable indices	A scalar or vector of table variable indices	1 [1 3 5]
Logical vector	A logical vector whose elements each correspond to a table variable, where true includes the corresponding variable and false excludes it	[true false true]
Function handle	A function handle that takes a table variable as input and returns a logical scalar	@isnumeric
vartype subscript	A table subscript generated by the vartype function	vartype('numeric')

Example: rmmissing(T,'DataVariables',["Var1" "Var2" "Var4"])

Output Arguments

R — Data with missing entries removed

vector | matrix | table | timetable

Data with missing entries removed, returned as a vector, matrix, table, or timetable. The size of R depends on the number of removed rows or columns.

Data Types: double | single | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | logical | char | string | cell | table | timetable | categorical | datetime | duration | calendarDuration

TF — Removed entry indicator

vector

Removed entry indicator, returned as a logical vector. The value 1 (true) corresponds to rows or columns in R that were removed. The value 0 (false) corresponds to unchanged rows and columns. The orientation and size of TF depends on A and the dimension of operation.

Data Types: logical

Extended Capabilities

Tall Arrays

Calculate with arrays that have more rows than fit in memory.

Usage notes and limitations:

• The 'DataVariables' name-value pair cannot specify a function handle.

• rmmissing(A,2) is not supported for tall tables.

For more information, see Tall Arrays.

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

• For table input, dim must equal 1.

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

This function fully supports GPU arrays. For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Distributed Arrays

Partition large arrays across the combined memory of your cluster using Parallel Computing Toolbox™.

This function fully supports distributed arrays. For more information, see Run MATLAB Functions with Distributed Arrays (Parallel Computing Toolbox).