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rmmissing

Remove missing entries

Description

example

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:

  • NaNdouble, single, duration, and calendarDuration

  • NaTdatetime

  • <missing>string

  • <undefined>categorical

  • ' 'char

  • {''}cell of character vectors

If A is a table, then the data type of each column defines the missing value for that column.

example

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.

example

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

example

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

Examples

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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

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

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

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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.

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

Consider an m-by-n input matrix array, A:

  • rmmissing(A,1) removes rows of A that contain missing data.

    rmmissing(A,1) row removal

  • rmmissing(A,2) removes columns of A that contain missing data.

    rmmissing(A,2) column removal

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

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

Minimum number of missing entries required to remove a row or column, specified as a non-negative scalar, which is 1 by default.

Example: rmmissing(A,'MinNumMissing',6)

Table variables to operate on, specified as 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.

OptionDescriptionExamples
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

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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.

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

Version History

Introduced in R2016b

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Behavior changed in R2022a