# New Ways to Arrange and Plot Data in Tables 6

Posted by **Loren Shure**,

Today I'd like to introduce a guest blogger, Stephen Doe, who works for the MATLAB Documentation team here at MathWorks. In today's post, Stephen shows us new functions for displaying, arranging, and plotting data in tables and timetables.

### Contents

#### Tables, Then and Now

In R2013b, MATLAB® introduced the `table` data type, as a convenient container for column-oriented data. And in R2016b, MATLAB introduced the `timetable` data type, which is a table that has timestamped rows.

From the beginning, these data types offered advantages over cell arrays and structures. But over the course of several releases, the table and graphics development teams have added many new functions for tables and timetables. These functions add convenient ways to display and arrange tabular data. Also, they offer new ways to make plots or charts directly from tables, without the intermediate step of peeling out variables. As of R2018b, MATLAB boasts many new functions to help you make more effective use of tables and timetables.

#### Read Table and Display First Few Rows

To begin, I will use the `readtable` function to read data from a sample file that ships with MATLAB. The file `outages.csv` contains simulated data for electric power outages over a period of 12 years in the United States. The call to `readtable` returns a table, `T`, with six variables and 1468 rows, so I will suppress the output using a semicolon.

```
T = readtable('outages.csv');
```

One typical way to examine the data in a large table is to display the first few rows of the table. You can use indexing to access a subset of rows (and/or a subset of variables, for that matter). For example, this syntax returns the first three rows of `T`.

T(1:3,:)

ans = 3×6 table Region OutageTime Loss Customers RestorationTime Cause ___________ ________________ ______ __________ ________________ ______________ 'SouthWest' 2002-02-01 12:18 458.98 1.8202e+06 2002-02-07 16:50 'winter storm' 'SouthEast' 2003-01-23 00:49 530.14 2.1204e+05 NaT 'winter storm' 'SouthEast' 2003-02-07 21:15 289.4 1.4294e+05 2003-02-17 08:14 'winter storm'

I have a confession to make: I have written many table examples, using that syntax. And occasionally, I **still** catch myself starting with code like `T(3,:)`, which accesses only one row.

Happily, in R2016b we added the `head` function to return the top rows of a table. Here's the call to return the first three rows using the `head` function.

head(T,3)

ans = 3×6 table Region OutageTime Loss Customers RestorationTime Cause ___________ ________________ ______ __________ ________________ ______________ 'SouthWest' 2002-02-01 12:18 458.98 1.8202e+06 2002-02-07 16:50 'winter storm' 'SouthEast' 2003-01-23 00:49 530.14 2.1204e+05 NaT 'winter storm' 'SouthEast' 2003-02-07 21:15 289.4 1.4294e+05 2003-02-17 08:14 'winter storm'

Similarly, `tail` returns the bottom rows of a table. (If you do not specify the number of rows, then `head` and `tail` return eight rows.)

#### Move, Add, and Delete Table Variables

After examining your table, you might find that you want to organize your table by moving related variables next to each other. For example, in `T` you might want to move `Region` and `Cause` so that they are together.

One way to move table variables is by indexing. But if you use indexing and want to keep all the variables, then you must specify them all in order, as shown in this syntax.

T = T(:,{'OutageTime','Loss','Customers','RestorationTime','Region','Cause'})

You also can use numeric indices. While more compact, this syntax is less readable.

T = T(:,[2:5 1 6])

When your table has many variables, it is awkward to move variables using indexing. Starting in R2018a, you can use the `movevars` function instead. Using `movevars`, you only have to specify the variables of interest. Move the `Region` variable so it is before `Cause`.

T = movevars(T,'Region','Before','Cause'); head(T,3)

ans = 3×6 table OutageTime Loss Customers RestorationTime Region Cause ________________ ______ __________ ________________ ___________ ______________ 2002-02-01 12:18 458.98 1.8202e+06 2002-02-07 16:50 'SouthWest' 'winter storm' 2003-01-23 00:49 530.14 2.1204e+05 NaT 'SouthEast' 'winter storm' 2003-02-07 21:15 289.4 1.4294e+05 2003-02-17 08:14 'SouthEast' 'winter storm'

It is also likely that you want to add data to your table. For example, let's calculate the duration of the power outages in `T`. Specify the format to display the duration in days.

```
OutageDuration = T.RestorationTime - T.OutageTime;
OutageDuration.Format = 'dd:hh:mm:ss';
```

It is easy to add `OutageDuration` to the end of a table using dot notation.

T.OutageDuration = OutageDuration;

However, you might want to add it at another location in `T`. In R2018a, you can use the `addvars` function. Add `OutageDuration` so that it is after `OutageTime`.

T = addvars(T,OutageDuration,'After','OutageTime'); head(T,3)

ans = 3×7 table OutageTime OutageDuration Loss Customers RestorationTime Region Cause ________________ ______________ ______ __________ ________________ ___________ ______________ 2002-02-01 12:18 06:04:32:00 458.98 1.8202e+06 2002-02-07 16:50 'SouthWest' 'winter storm' 2003-01-23 00:49 NaN 530.14 2.1204e+05 NaT 'SouthEast' 'winter storm' 2003-02-07 21:15 09:10:59:00 289.4 1.4294e+05 2003-02-17 08:14 'SouthEast' 'winter storm'

Now, let's remove `RestorationTime`. You can easily remove variables using dot notation and an empty array.

T.RestorationTime = [];

However, in R2018a there is also a function to remove table variables. To remove `RestorationTime`, use the `removevars` function.

```
T = removevars(T,'RestorationTime');
head(T,3)
```

ans = 3×6 table OutageTime OutageDuration Loss Customers Region Cause ________________ ______________ ______ __________ ___________ ______________ 2002-02-01 12:18 06:04:32:00 458.98 1.8202e+06 'SouthWest' 'winter storm' 2003-01-23 00:49 NaN 530.14 2.1204e+05 'SouthEast' 'winter storm' 2003-02-07 21:15 09:10:59:00 289.4 1.4294e+05 'SouthEast' 'winter storm'

#### Convert to Timetable

If your table contains dates and times in a `datetime` array, you can easily convert it to a timetable using the `table2timetable` function. In this example, `table2timetable` converts the values in `OutageTime` to *row times*. Row times are time stamps that label the rows of a timetable.

TT = table2timetable(T); head(TT,3)

ans = 3×5 timetable OutageTime OutageDuration Loss Customers Region Cause ________________ ______________ ______ __________ ___________ ______________ 2002-02-01 12:18 06:04:32:00 458.98 1.8202e+06 'SouthWest' 'winter storm' 2003-01-23 00:49 NaN 530.14 2.1204e+05 'SouthEast' 'winter storm' 2003-02-07 21:15 09:10:59:00 289.4 1.4294e+05 'SouthEast' 'winter storm'

When you display a timetable, it looks very similar to a table. One important difference is that a timetable has fewer variables than you might expect by glancing at the display. `TT` has five variables, not six. The vector of row times, `OutageTime`, is not considered a timetable variable, since its values label the rows. However, you can still access the row times using dot notation, as in `T.OutageTime`. You can use the vector of row times as an input argument to a function. For example, you can use it as the *x*-axis of a plot.

The row times of a timetable do not have to be ordered. If you want to be sure that the rows of a timetable are sorted by the row times, use the `sortrows` function.

TT = sortrows(TT); head(TT,3)

ans = 3×5 timetable OutageTime OutageDuration Loss Customers Region Cause ________________ ______________ ______ __________ ___________ ______________ 2002-02-01 12:18 06:04:32:00 458.98 1.8202e+06 'SouthWest' 'winter storm' 2002-03-05 17:53 04:20:48:00 96.563 2.8666e+05 'MidWest' 'wind' 2002-03-16 06:18 02:17:05:00 186.44 2.1275e+05 'MidWest' 'severe storm'

#### Make Stacked Plot of Variables

Now I will show you why I converted `T` to a timetable. Starting in R2018b, you can plot the variables of a table or timetable in a *stacked plot*. In a stacked plot, the variables are plotted in separate *y*-axes, but using a common *x*-axis. And if you make a stacked plot from a timetable, the *x*-values are the row times.

To plot the variables of `TT`, use the `stackedplot` function. The function plots variables that can be plotted (such as numeric, `datetime`, and `categorical` arrays) and ignores variables that cannot be plotted. `stackedplot` also returns properties of the stacked plot as an object that allows customization of the stacked plot.

s = stackedplot(TT)

s = StackedLineChart with properties: SourceTable: [1468×5 timetable] DisplayVariables: {'OutageDuration' 'Loss' 'Customers'} Color: [0 0.4470 0.7410] LineStyle: '-' LineWidth: 0.5000 Marker: 'none' MarkerSize: 6 Use GET to show all properties

One thing you can tell right away from this plot is that there must be a few timetable rows with bad data. There is one point for a power outage that supposedly lasted for over 9,000 days (or 24 years), which would mean it ended some time in the 2040s.

#### Convert Variables in Place

The `stackedplot` function ignored the `Region` and `Cause` variables, because these variables are cell arrays of character vectors. You might want to convert these variables to a different, and more useful, data type. While you can convert variables one at a time, there is now a more convenient way to convert all table variables of a specified data type.

Starting in R2018b, you can convert table variables in place using the `convertvars` function. For example, identify all the cell arrays of character vectors in `TT` (using `iscellstr`) and convert them to `categorical` arrays. Now `Region` and `Cause` contain discrete values assigned to categories. Categorical values are displayed without any quotation marks.

```
TT = convertvars(TT,@iscellstr,'categorical');
head(TT,3)
```

ans = 3×5 timetable OutageTime OutageDuration Loss Customers Region Cause ________________ ______________ ______ __________ _________ ____________ 2002-02-01 12:18 06:04:32:00 458.98 1.8202e+06 SouthWest winter storm 2002-03-05 17:53 04:20:48:00 96.563 2.8666e+05 MidWest wind 2002-03-16 06:18 02:17:05:00 186.44 2.1275e+05 MidWest severe storm

#### Plots of Discrete Data

If your table or timetable has variables with values that belong to a finite set of discrete categories, then there are other interesting plots that you can make. Starting in R2017a, you can make a *heat map* of any two variables that contain discrete values using the `heatmap` function. For example, make a heat map of the `Region` and `Cause` variables to visualize where and why outages occur. Again, `heatmap` returns an object so you can customize the plot.

h = heatmap(TT,'Region','Cause')

h = HeatmapChart (Count of Cause vs. Region) with properties: SourceTable: [1468×5 timetable] XVariable: 'Region' YVariable: 'Cause' ColorVariable: '' ColorMethod: 'count' Use GET to show all properties

You also can make a pie chart of any `categorical` variable (as of R2014b), using the `pie` function. However, you cannot call `pie` on a table. So, to make a pie chart of the power outages by region, use dot notation to access the `Region` variable.

pie(TT.Region)

#### Other Functions to Rearrange or Join Tables

MATLAB also has other functions to reorganize variables in more complex ways, and to join tables. I won't show them all in action, but I will describe some of them briefly. All these functions work with both tables and timetables.

R2018a includes functions to:

- Reorient rows to become variables (
`rows2vars`)

- Invert a nested table-in-table (
`inner2outer`)

And from the original release of tables in R2013b, there are functions to:

#### Tabled for Discussion

Let's table discussion of these new functions for now. But we are eager to hear about your reactions to them. Do they help you make more effective use of tables and timetables? Please let us know here.

Get the MATLAB code

Published with MATLAB® R2018b

**Category:**- Data types,
- New Feature

## 6 CommentsOldest to Newest

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TT = synchronize(TTobs,TTsim,TTobs.Time,'linear')If linear interpolation won't do, then you can specify some other method, such as spline or cubic interpolation. This call to synchronize should modify only your simulated data. The data points that were in TTobs should be unchanged, because those points already corresponded to the times in TTobs.Time. If you want to be absolutely sure that the data in TTobs remains untouched, then another way to do this is to call the retime function on TTsim only, using the times in TTobs.Time. Then you can horizontally concatenate the timetables together.

TTsim2 = retime(TTsim,TTobs.Time,'linear'); TT = [TTobs TTsim2]Here I am doing the exact same thing that I did in my previous call to the synchronize function. I'm just doing it in two steps instead of one. Now there is one timetable that contains observed and simulated data points corresponding to the same time vector. This is when I would call some statistical function to check whether the simulated data points match the observed data points well. Without knowing more about your data, I'm not sure which function would be appropriate. MATLAB comes with some useful functions you will find described in the documentation for Descriptive Statistics. Or it might be appropriate to use the Statistics and Machine Learning Toolbox. Finally, you can use the stackedplot function to display observed and simulated data together.

TT = synchronize(TTobs,TTsim,TTobs.Time,'linear'); s = stackedplot(TT)The stackedplot function plots on a common x-axis. But that x-axis does not have to be the times from TTobs.Time. Suppose you don't want to modify the simulated data points, and you want all the times from both TTobs and TTsim. To do that, call synchronize so that the output timetable has a time vector that is the union of the times from TTobs and TTsim. And instead of specifying an interpolation method, you can let synchronize fill gaps in the output timetable with NaNs. When you plot the resulting timetable, stackedplot ignores the NaNs. (You also can change the marker and line style to show points, instead of connecting them with lines.)

TT = synchronize(TTobs,TTsim,'union'); s = stackedplot(TT); s.LineStyle = 'none'; s.Marker = '+'I hope these small examples are helpful. Please get back in touch if you have any other comments or questions. – Stephen

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