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Split Table Data Variables and Apply Functions

This example shows how to split power outage data from a table into groups by region and cause of the power outages. Then it shows how to apply functions to calculate statistics for each group and collect the results in a table.

Load Power Outage Data

The sample file, outages.csv, contains data representing electric utility outages in the United States. The file contains six columns: Region, OutageTime, Loss, Customers, RestorationTime, and Cause. Read outages.csv into a table.

T = readtable('outages.csv');

Convert Region and Cause to categorical arrays, and OutageTime and RestorationTime to datetime arrays. Display the first five rows.

T.Region = categorical(T.Region);
T.Cause = categorical(T.Cause);
T.OutageTime = datetime(T.OutageTime);
T.RestorationTime = datetime(T.RestorationTime);
T(1:5,:)
ans=5×6 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   
    West         2004-04-06 05:44    434.81    3.4037e+05    2004-04-06 06:10    equipment fault
    MidWest      2002-03-16 06:18    186.44    2.1275e+05    2002-03-18 23:23    severe storm   

Calculate Maximum Power Loss

Determine the greatest power loss due to a power outage in each region. The findgroups function returns G, a vector of group numbers created from T.Region. The splitapply function uses G to split T.Loss into five groups, corresponding to the five regions. splitapply applies the max function to each group and concatenates the maximum power losses into a vector.

G = findgroups(T.Region);
maxLoss = splitapply(@max,T.Loss,G)
maxLoss = 5×1
104 ×

    2.3141
    2.3418
    0.8767
    0.2796
    1.6659

Calculate the maximum power loss due to a power outage by cause. To specify that Cause is the grouping variable, use table indexing. Create a table that contains the maximum power losses and their causes.

T1 = T(:,'Cause');
[G,powerLosses] = findgroups(T1);
powerLosses.maxLoss = splitapply(@max,T.Loss,G)
powerLosses=10×2 table
         Cause          maxLoss
    ________________    _______

    attack              582.63 
    earthquake          258.18 
    energy emergency     11638 
    equipment fault      16659 
    fire                872.96 
    severe storm        8767.3 
    thunder storm        23418 
    unknown              23141 
    wind                  2796 
    winter storm        2883.7 

powerLosses is a table because T1 is a table. You can append the maximum losses as another table variable.

Calculate the maximum power loss by cause in each region. To specify that Region and Cause are the grouping variables, use table indexing. Create a table that contains the maximum power losses and display the first 15 rows.

T1 = T(:,{'Region','Cause'});
[G,powerLosses] = findgroups(T1);
powerLosses.maxLoss = splitapply(@max,T.Loss,G);
powerLosses(1:15,:)
ans=15×3 table
     Region           Cause          maxLoss
    _________    ________________    _______

    MidWest      attack                   0 
    MidWest      energy emergency    2378.7 
    MidWest      equipment fault     903.28 
    MidWest      severe storm        6808.7 
    MidWest      thunder storm        15128 
    MidWest      unknown              23141 
    MidWest      wind                2053.8 
    MidWest      winter storm        669.25 
    NorthEast    attack              405.62 
    NorthEast    earthquake               0 
    NorthEast    energy emergency     11638 
    NorthEast    equipment fault     794.36 
    NorthEast    fire                872.96 
    NorthEast    severe storm        6002.4 
    NorthEast    thunder storm        23418 

Calculate Number of Customers Impacted

Determine power-outage impact on customers by cause and region. Because T.Loss contains NaN values, wrap sum in an anonymous function to use the 'omitnan' input argument.

osumFcn = @(x)(sum(x,'omitnan'));
powerLosses.totalCustomers = splitapply(osumFcn,T.Customers,G);
powerLosses(1:15,:)
ans=15×4 table
     Region           Cause          maxLoss    totalCustomers
    _________    ________________    _______    ______________

    MidWest      attack                   0                0  
    MidWest      energy emergency    2378.7       6.3363e+05  
    MidWest      equipment fault     903.28       1.7822e+05  
    MidWest      severe storm        6808.7       1.3511e+07  
    MidWest      thunder storm        15128       4.2563e+06  
    MidWest      unknown              23141       3.9505e+06  
    MidWest      wind                2053.8       1.8796e+06  
    MidWest      winter storm        669.25       4.8887e+06  
    NorthEast    attack              405.62           2181.8  
    NorthEast    earthquake               0                0  
    NorthEast    energy emergency     11638       1.4391e+05  
    NorthEast    equipment fault     794.36       3.9961e+05  
    NorthEast    fire                872.96       6.1292e+05  
    NorthEast    severe storm        6002.4       2.7905e+07  
    NorthEast    thunder storm        23418       2.1885e+07  

Calculate Mean Durations of Power Outages

Determine the mean durations of all U.S. power outages in hours. Add the mean durations of power outages to powerLosses. Because T.RestorationTime has NaT values, omit the resulting NaN values when calculating the mean durations.

D = T.RestorationTime - T.OutageTime;
H = hours(D);
omeanFcn = @(x)(mean(x,'omitnan'));
powerLosses.meanOutage = splitapply(omeanFcn,H,G);
powerLosses(1:15,:)
ans=15×5 table
     Region           Cause          maxLoss    totalCustomers    meanOutage
    _________    ________________    _______    ______________    __________

    MidWest      attack                   0                0        335.02  
    MidWest      energy emergency    2378.7       6.3363e+05        5339.3  
    MidWest      equipment fault     903.28       1.7822e+05        17.863  
    MidWest      severe storm        6808.7       1.3511e+07        78.906  
    MidWest      thunder storm        15128       4.2563e+06        51.245  
    MidWest      unknown              23141       3.9505e+06        30.892  
    MidWest      wind                2053.8       1.8796e+06        73.761  
    MidWest      winter storm        669.25       4.8887e+06        127.58  
    NorthEast    attack              405.62           2181.8        5.5117  
    NorthEast    earthquake               0                0             0  
    NorthEast    energy emergency     11638       1.4391e+05        77.345  
    NorthEast    equipment fault     794.36       3.9961e+05        87.204  
    NorthEast    fire                872.96       6.1292e+05        4.0267  
    NorthEast    severe storm        6002.4       2.7905e+07        2163.5  
    NorthEast    thunder storm        23418       2.1885e+07        46.098  

See Also

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