Import Block of Mixed Data from Text File into Table or Cell Array

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This example reads a block of mixed text and numeric data from a text file, and then imports the block of data into a table or a cell array.

Data File Overview

The sample file bigfile.txt contains commented lines beginning with ##. The data is arranged in five columns: The first column contains text indicating timestamps. The second, third, and fourth columns contain numeric data indicating temperature, humidity and wind speed. The last column contains descriptive text. Display the contents of the file bigfile.txt.

type('bigfile.txt')
## A	ID = 02476
## YKZ Timestamp Temp Humidity Wind Weather
06-Sep-2013 01:00:00	6.6	89	4	clear
06-Sep-2013 05:00:00	5.9	95	1	clear
06-Sep-2013 09:00:00	15.6	51	5	mainly clear
06-Sep-2013 13:00:00	19.6	37	10	mainly clear
06-Sep-2013 17:00:00	22.4	41	9	mostly cloudy
06-Sep-2013 21:00:00	17.3	67	7	mainly clear
## B	ID = 02477
## YVR Timestamp Temp Humidity Wind Weather
09-Sep-2013 01:00:00	15.2	91	8	clear
09-Sep-2013 05:00:00	19.1	94	7	n/a
09-Sep-2013 09:00:00	18.5	94	4	fog
09-Sep-2013 13:00:00	20.1	81	15	mainly clear
09-Sep-2013 17:00:00	20.1	77	17	n/a
09-Sep-2013 18:00:00	20.0	75	17	n/a
09-Sep-2013 21:00:00	16.8	90	25	mainly clear
## C	ID = 02478
## YYZ Timestamp Temp Humidity Wind Weather

Import Block of Data as Table

To import the data as a table, use readtable with import options.

Create an import options object for the file using the detectImportOptions function. Specify the location of the data using the DataLines property. For example, lines 3 through 8 contain the first block of data. Optionally, you can specify the names of the variables using the VariableNames property. Finally import the first block of data using readtable with the opts object.

opts = detectImportOptions('bigfile.txt'); 
opts.DataLines = [3 8];
opts.VariableNames = {'Timestamp','Temp',...
                      'Humidity','Wind','Weather'};
T_first = readtable('bigfile.txt',opts) 
T_first=6×5 table
    06-Sep-2013 01:00:00    6.6000    89    4    'clear'
    06-Sep-2013 05:00:00    5.9000    95    1    'clear'
    06-Sep-2013 09:00:00    15.6000    51    5    'mainly clear'
    06-Sep-2013 13:00:00    19.6000    37    10    'mainly clear'
    06-Sep-2013 17:00:00    22.4000    41    9    'mostly cloudy'
    06-Sep-2013 21:00:00    17.3000    67    7    'mainly clear'

Read the second block by updating the DataLines property to the location of the second block.

opts.DataLines = [11 17];
T_second = readtable('bigfile.txt',opts)
T_second=7×5 table
    09-Sep-2013 01:00:00    15.2000    91    8    'clear'
    09-Sep-2013 05:00:00    19.1000    94    7    'n/a'
    09-Sep-2013 09:00:00    18.5000    94    4    'fog'
    09-Sep-2013 13:00:00    20.1000    81    15    'mainly clear'
    09-Sep-2013 17:00:00    20.1000    77    17    'n/a'
    09-Sep-2013 18:00:00    20    75    17    'n/a'
    09-Sep-2013 21:00:00    16.8000    90    25    'mainly clear'

Import Block of Data as Cell Array

You can import the data as a cell array using the readcell function with detectImportOptions, or by using the textscan function. First import the block of data using the readcell function and then perform the same import by using textscan.

To perform the import using the readcell function, create an import options object for the file using the detectImportOptions function. Specify the location of the data using the DataLines property. Then, perform the import operation using the readcell function and import options object opts.

opts = detectImportOptions('bigfile.txt'); 
opts.DataLines = [3 8]; % fist block of data
C = readcell('bigfile.txt',opts)
C=6×5 cell array
    1×1 datetime     6.6000    89     4            'clear'
    1×1 datetime     5.9000    95     1            'clear'
    1×1 datetime    15.6000    51     5     'mainly clear'
    1×1 datetime    19.6000    37    10     'mainly clear'
    1×1 datetime    22.4000    41     9    'mostly cloudy'
    1×1 datetime    17.3000    67     7     'mainly clear'

To perform the import using the textscan function, specify the size of block using N and the format of the data fields using formatSpec. For example, use '%s' for text variables, '%D' for date and time variables, or '%c' for categorical variables. Set the 'DateLocale' name-value argument to 'en_US' to ensure that the names of the months are interpreted in English. Use fopen to open the file. The function then returns a file identifier, fileID. Next, read from the file by using the textscan function. 

N = 6;
formatSpec = '%D %f %f %f %c';
fileID = fopen('bigfile.txt');

Read the first block and display the contents of the variable Humidity.

C_first = textscan(fileID,formatSpec,N,'CommentStyle','##','Delimiter','\t','DateLocale','en_US')
C_first=1×5 cell array
    6×1 datetime    [6.6000;NaN;5.9000;NaN;15.6000;NaN]    [89;NaN;95;NaN;51;NaN]    [4;NaN;1;NaN;5;NaN]    6×1 char


C_first{3}
ans = 6×1

     89
    NaN
     95
    NaN
     51
    NaN

Update the block size N, and read the second block. Display the contents of the fifth variable Weather.

N = 7;
C_second = textscan(fileID,formatSpec,N,'CommentStyle','##','Delimiter','\t','DateLocale','en_US')
C_second=1×5 cell array
    7×1 datetime    [19.6000;NaN;22.4000;NaN;17.3000;NaN;15.2000]    [37;NaN;41;NaN;67;NaN;91]    [10;NaN;9;NaN;7;NaN;8]    7×1 char


C_second{5}
ans = 7×1 char array
    'm'
    '↵'
    'm'
    '↵'
    'm'
    '↵'
    'c'

Close the file.

fclose(fileID);

See Also

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