# Documentation

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## Resample and Aggregate Data in Timetable

This example shows how to resample and aggregate data in a timetable. A timetable is a type of table that associates a time with each row. A timetable can store column-oriented data variables that have different data types and sizes, so long as each variable has the same number of rows. With the `retime` function, you can resample timetable data, or aggregate timetable data into time bins you specify.

### Import Timetable

Load a timetable containing weather measurements taken from November 15, 2015, to November 19, 2015. The timetable contains humidity, temperature, and pressure readings taken over this time period.

```load outdoors outdoors(1:5,:)```
```ans=5x3 timetable Time Humidity TemperatureF PressureHg ___________________ ________ ____________ __________ 2015-11-15 00:00:24 49 51.3 29.61 2015-11-15 01:30:24 48.9 51.5 29.61 2015-11-15 03:00:24 48.9 51.5 29.61 2015-11-15 04:30:24 48.8 51.5 29.61 2015-11-15 06:00:24 48.7 51.5 29.6 ```

Determine if the timetable is regular. A regular timetable is one in which the differences between all consecutive row times are the same. `outdoors` is not a regular timetable.

`TF = isregular(outdoors)`
```TF = logical 0 ```

Find the differences in the time steps. They vary between half a minute and an hour and a half.

`dt = unique(diff(outdoors.Time))`
```dt = 3x1 duration array 00:00:24 01:29:36 01:30:00 ```

### Resample Timetable with Interpolation

Adjust the data in the timetable with the `retime` function. Specify an hourly time vector. Interpolate the timetable data to the new row times.

```TT = retime(outdoors,'hourly','spline'); TT(1:5,:)```
```ans=5x3 timetable Time Humidity TemperatureF PressureHg ___________________ ________ ____________ __________ 2015-11-15 00:00:00 49.001 51.298 29.61 2015-11-15 01:00:00 48.909 51.467 29.61 2015-11-15 02:00:00 48.902 51.51 29.61 2015-11-15 03:00:00 48.9 51.5 29.61 2015-11-15 04:00:00 48.844 51.498 29.611 ```

### Resample Timetable with Nearest Neighbor Values

Specify an hourly time vector for `TT`. For each row in `TT`, copy values from the corresponding row in `outdoors` whose row time is nearest.

```TT = retime(outdoors,'hourly','nearest'); TT(1:5,:)```
```ans=5x3 timetable Time Humidity TemperatureF PressureHg ___________________ ________ ____________ __________ 2015-11-15 00:00:00 49 51.3 29.61 2015-11-15 01:00:00 48.9 51.5 29.61 2015-11-15 02:00:00 48.9 51.5 29.61 2015-11-15 03:00:00 48.9 51.5 29.61 2015-11-15 04:00:00 48.8 51.5 29.61 ```

### Aggregate Timetable Data and Calculate Daily Mean

The `retime` function provides aggregation methods, such as `mean`. Calculate the daily means for the data in `outdoors`.

```TT = retime(outdoors,'daily','mean'); TT```
```TT=4x3 timetable Time Humidity TemperatureF PressureHg ___________________ ________ ____________ __________ 2015-11-15 00:00:00 48.931 51.394 29.607 2015-11-16 00:00:00 47.924 51.571 29.611 2015-11-17 00:00:00 48.45 51.238 29.613 2015-11-18 00:00:00 49.5 50.8 29.61 ```

### Aggregate Timetable Data to Different Time Vector

Calculate the means over six-hour time intervals. Specify a time vector to use with the `retime` function. Specify a format for the time vector to display both date and time when you display the timetable.

```tv = datetime(2015,11,15):hours(6):datetime(2015,11,18); tv.Format = 'dd-MMM-yyyy HH:mm:ss'; TT = retime(outdoors,tv,'mean'); TT(1:5,:)```
```ans=5x3 timetable Time Humidity TemperatureF PressureHg ____________________ ________ ____________ __________ 15-Nov-2015 00:00:00 48.9 51.45 29.61 15-Nov-2015 06:00:00 48.9 51.45 29.6 15-Nov-2015 12:00:00 49.025 51.45 29.61 15-Nov-2015 18:00:00 48.9 51.225 29.607 16-Nov-2015 00:00:00 48.5 51.4 29.61 ```