Data Analysis in Time Interval


I am working huge amount of data which I have recently collected for my research. I would like to get to know what time of the day user activity happened. Here is the small amount of my dataset snapshot:

<img alt="enter image description here" class="b-lazy" data-src="https://i.stack.imgur.com/iipz2.png" data-original="https://i.stack.imgur.com/iipz2.png" src="https://etrip.eimg.top/images/2019/05/07/timg.gif" />

I applied to following code to get to know time interval login activity.


The result is written in the Column N. Everything so far, works I believe. However my question is what if I want to analyze in every 10 min rather than every one hour.

for example how many login happened between 8.00 am-8.10 am. My current approach to find every hour interval. I would be glad if you could help me out in excel or matlab.

<img alt="enter image description here" class="b-lazy" data-src="https://i.stack.imgur.com/Bol7N.png" data-original="https://i.stack.imgur.com/Bol7N.png" src="https://etrip.eimg.top/images/2019/05/07/timg.gif" />


this is relatively simple in excel if you use the array function Frequency()

some steps to take:

i assume that you want to sort by time of the day for every day

so the time column is the data_array, the first parameter for Frequency (check out excel help)

for the bin data you will have to get a with a start time at its head add for the following bins below 10 minutes each (= 1/24/6) to make 6th of an hour out of days

so a total of 144 bins for a full day

then select the column segment next to your bin and array edit it with =Frequency(data_array, bin_array) and don't forget to enter it with


Ok, I have got the answer in the following screenshoot.

<img alt="enter image description here" class="b-lazy" data-src="https://i.stack.imgur.com/9gdHb.png" data-original="https://i.stack.imgur.com/9gdHb.png" src="https://etrip.eimg.top/images/2019/05/07/timg.gif" />


Here is a possible MATLAB solution.

<h2>1) Fake data</h2>

To test my code, I first had to generate some fake data resembling your actual one. You can skip this section when working with the real data.

len = 50; %# random dates inbetween these two dt_start = datenum('2013-06-22 19:17:00'); dt_end = datenum('2013-06-22 19:23:00'); dt = sort(dt_start + rand(len,1)*(dt_end-dt_start)); %# create the columns ID = repmat(int32(104885), [len 1]); Date = cellstr(datestr(dt, 'mm/dd/yyyy')); Time = cellstr(datestr(dt, 'HH:MM:SS')); EE = rand(len,1); %# combine columns into a cell array C = [num2cell(ID), Date, Time, num2cell(EE)]; %# create a "table" for convenience, and export to CSV file t = cell2table(C, 'VariableNames',{'ID', 'Date', 'Time', 'EE'}) writetable(t, 'data.csv') %# cleanup clear len dt_start dt_end dt ID Date Time EE C t

Here is an extract of the data I generated for this example:

t = ID Date Time EE ______ ____________ __________ _________ 104885 '06/22/2013' '19:17:19' 0.95808 104885 '06/22/2013' '19:17:22' 0.72305 104885 '06/22/2013' '19:17:31' 0.86481 104885 '06/22/2013' '19:17:33' 0.52325 . . 104885 '06/22/2013' '19:22:37' 0.5167 104885 '06/22/2013' '19:22:39' 0.53815 104885 '06/22/2013' '19:22:41' 0.27151 104885 '06/22/2013' '19:22:54' 0.37826 104885 '06/22/2013' '19:22:59' 0.51215 <h2>2) Frequency count in time intervals</h2>

I read the data from CSV file, then apply a process similarly to what was used in <a href="https://stackoverflow.com/a/18820691/97160" rel="nofollow">the post</a> you linked to. In this case, the time interval length is a parameter you can specify (10 minutes interval, 1 hour, etc..)

%# load data from CSV t = readtable('data.csv', 'Format','%d %s %s %f', 'Delimiter',',', ... 'ReadVariableNames',true, 'FileType','text'); %# convert date/time columns to serial date number dt = datenum(strcat(t.Date , {' '}, t.Time), 'mm/dd/yyyy HH:MM:SS'); %# desired interval window-size (expressed in units of days). %# Here I am using a 2 minutes interval interval = 2/(24*60); % (24 hours per day, 60 min per hour) % bin datetimes into specified intervals dt_binned = fix(dt/interval)*interval; % count frequencies in each interval [dt_unique,~,dt_unique_idx] = unique(dt_binned); counts = accumarray(dt_unique_idx, 1); freq = [cellstr(datestr(dt_unique)) num2cell(counts)]

The result:

freq = '22-Jun-2013 19:16:00' [ 7] '22-Jun-2013 19:18:00' [21] '22-Jun-2013 19:20:00' [12] '22-Jun-2013 19:22:00' [10]

So we had 7 events occurred in the first interval (from 19:16 to 19:18), 21 events in the second event, and so on. You can easily adapt the code to change the interval length.


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