How to extract Datetime string to separate columns of yyyy dd mm hh mm

Am a noob, so bear with me, I need to extract the date string and separate the year day month hour and minute to separate column within the dataframe. AM getting an error with ectractBetween. What could I be doing wrong? Ive started with just the year in this code

ararat = importdata("ararat.txt");
Q1 = [ararat(:,1)];
resQ1 = num2str(Q1)


for N=1:length(resQ1)
    year = extractBetween(resQ1, 1,4)
end

Answer 1

[Stephen23]

That is a very unfortunate date format. Best avoided.

T = readtable('ararat.txt', 'Format','%{yyyyddMMHHmm}D%f'); % ugh.
T.Properties.VariableNames = {'DT','Val'};
T.DT.Format = 'yyyy-MM-dd HH:mm:ss'; % aaah, much better :)
[T.Year,T.Month,T.Day] = ymd(T.DT);
[T.Hour,T.Minute,T.Second] = hms(T.DT);
display(T)

T = 52560×8 table
            DT              Val      Year    Month    Day    Hour    Minute    Second
    ___________________    ______    ____    _____    ___    ____    ______    ______

    2020-03-21 12:00:00    3.9798    2020      3      21      12        0        0   
    2020-03-21 12:10:00    4.3032    2020      3      21      12       10        0   
    2020-03-21 12:20:00    4.2702    2020      3      21      12       20        0   
    2020-03-21 12:30:00    4.0128    2020      3      21      12       30        0   
    2020-03-21 12:40:00    4.9302    2020      3      21      12       40        0   
    2020-03-21 12:50:00     5.445    2020      3      21      12       50        0   
    2020-03-21 13:00:00     4.983    2020      3      21      13        0        0   
    2020-03-21 13:10:00    3.8544    2020      3      21      13       10        0   
    2020-03-21 13:20:00    7.2072    2020      3      21      13       20        0   
    2020-03-21 13:30:00     7.557    2020      3      21      13       30        0   
    2020-03-21 13:40:00    7.6098    2020      3      21      13       40        0   
    2020-03-21 13:50:00    7.0686    2020      3      21      13       50        0   
    2020-03-21 14:00:00     6.666    2020      3      21      14        0        0   
    2020-03-21 14:10:00    6.0588    2020      3      21      14       10        0   
    2020-03-21 14:20:00    5.8674    2020      3      21      14       20        0   
    2020-03-21 14:30:00    5.7156    2020      3      21      14       30        0   

[Dennis]

this is awesome, way easier to implement and extract the timeframes

[Siddharth Bhutiya]

You dont even need to call ymd or hms here. For example if DT is a datetime then doing DT.Year would give you the year of all datetime, DT.Month, the month and so on. So this could be done like this.

T.Year = T.DT.Year;
T.Month = T.DT.Month; % and so on

Answer 2

[Mohammad Sami]

varNames = {'dt','val'} ;
varTypes = {'datetime','double'} ;
delimiter = '\t';
opts = delimitedTextImportOptions('VariableNames',varNames,...
                                'VariableTypes',varTypes,...
                                'Delimiter',delimiter);
% specify the datetime input format to read the column as datetime value
opts = setvaropts(opts,{'dt'},'InputFormat','yyyyddMMHHmm');
a = readtable('https://www.mathworks.com/matlabcentral/answers/uploaded_files/1256912/ararat.txt',opts);
head(a);

         dt          val  
    ____________    ______

    202021031200    3.9798
    202021031210    4.3032
    202021031220    4.2702
    202021031230    4.0128
    202021031240    4.9302
    202021031250     5.445
    202021031300     4.983
    202021031310    3.8544

% use datevec function to extract year, month, date, hour and minute values 
[a.y,a.M,a.d,a.H,a.m] = datevec(a.dt);
head(a);

         dt          val       y      M    d     H     m 
    ____________    ______    ____    _    __    __    __

    202021031200    3.9798    2020    3    21    12     0
    202021031210    4.3032    2020    3    21    12    10
    202021031220    4.2702    2020    3    21    12    20
    202021031230    4.0128    2020    3    21    12    30
    202021031240    4.9302    2020    3    21    12    40
    202021031250     5.445    2020    3    21    12    50
    202021031300     4.983    2020    3    21    13     0
    202021031310    3.8544    2020    3    21    13    10