How can i create a descriptive statistics table for columns

Hey Guys!

My MatLab skills are pretty pretty basic and now i need your help with something, because i can't find the answer here. So i have a 15x10 data.mat where there are 10 variables with 15 values. Now i want some descreptive statistics of those variables.

The perfect solution would be a group that looks kind of like this:

            Mean  Max  Std  ...
Variable 1   x     x    x
Variable 2   x     x    x 
...

Is there any way to achieve a table/group like this?

Thank you very much in advance!

Answer 1

[David Fletcher]

The default behavior for most of the statistical function is for column-wise operation and since that is what you want you can just apply them to your data matrix. So, if you want to collate the results together in a matrix:

results=[mean(data)' max(data)' std(data)']

If you specically want a table then:

dummyData=randi(10,15,10)

results=table()

results.Mean=mean(dummyData)'
results.Max=max(dummyData)'
results.Std_dev=std(dummyData)'

results =

10×3 table

     Mean     Max    Std_dev
    ______    ___    _______

       5.6    10     3.4184 
    5.0667     9     2.7894 
    4.6667    10     3.2878 
    6.0667    10     2.7894 
       5.2    10     2.8082 
    5.0667    10     2.5765 
         5    10     2.9032 
       5.6    10     2.7464 
    5.5333    10     2.8502 
       5.2     9     2.5967

[Stefano Döring]

Thank you very much! This helped alot!

Answer 2

[Peter Perkins]

Another possibility:

>> t = array2table(rand(10,5))
t =
  10×5 table
      Var1        Var2       Var3        Var4        Var5   
    ________    ________    _______    ________    _________
     0.69989     0.96865    0.28101     0.67612      0.78052
     0.63853     0.53133    0.44009     0.28906      0.67533
    0.033604     0.32515    0.52714     0.67181    0.0067153
    0.068806     0.10563    0.45742     0.69514      0.60217
      0.3196     0.61096    0.87537    0.067993      0.38677
     0.53086      0.7788    0.51805     0.25479      0.91599
     0.65445     0.42345    0.94362     0.22404    0.0011511
     0.40762    0.090823    0.63771     0.66783      0.46245
     0.81998     0.26647    0.95769     0.84439      0.42435
     0.71836     0.15366    0.24071     0.34446      0.46092
>> t2 = varfun(@(x) [mean(x); max(x); std(x)],t)
t2 =
  3×5 table
    Fun_Var1    Fun_Var2    Fun_Var3    Fun_Var4    Fun_Var5
    ________    ________    ________    ________    ________
    0.48917     0.42549     0.58788     0.47356     0.47164 
    0.81998     0.96865     0.95769     0.84439     0.91599 
    0.27434     0.29609     0.26046     0.26458     0.29787 
>> t2.Properties.RowNames = {'mean' 'max' 'std'}
t2 =
  3×5 table
            Fun_Var1    Fun_Var2    Fun_Var3    Fun_Var4    Fun_Var5
            ________    ________    ________    ________    ________
    mean    0.48917     0.42549     0.58788     0.47356     0.47164 
    max     0.81998     0.96865     0.95769     0.84439     0.91599 
    std     0.27434     0.29609     0.26046     0.26458     0.29787 
>> t2.Properties.VariableNames = extractAfter(t2.Properties.VariableNames,'Fun_')
t2 =
  3×5 table
             Var1       Var2       Var3       Var4       Var5  
            _______    _______    _______    _______    _______
    mean    0.48917    0.42549    0.58788    0.47356    0.47164
    max     0.81998    0.96865    0.95769    0.84439    0.91599
    std     0.27434    0.29609    0.26046    0.26458    0.29787

That's the wrong orientation for what you asked. In R2018a, you could use rows2vars to "flip" it the other way 'round.

Answer 3

[Jennifer Rebbin]

Starting in R2024b, you can use the summary function and specify the statistics to compute using the Statistics name-value argument.

A = rand(15,10);
summary(A,Statistics=["mean" "max" "std"])

A: 15×10 double

    Mean      0.5868        0.4791        0.4498        0.4893        0.3601        0.4134        0.4774        0.4449        0.4877        0.4195  
    Max       0.9065        0.8000        0.9323        0.9274        0.9326        0.9495        0.8417        0.9956        0.9812        0.8821  
    Std       0.1783        0.2277        0.2968        0.2769        0.2859        0.3100        0.2320        0.3047        0.2570        0.2650