Dear all,
Is there a single function that returns the basic descriptive statistics from a dataset array?
Something like the summary function but more complete
Thanks in advance,
Diego
MATLAB: Descriptive Statistics Function for a Dataset Array
dataset arraydescriptive statisticsStatistics and Machine Learning Toolbox
Related Solutions
I wrote a function that does this, here you go:
function Tdummy = dummytable(T)% Tdummy = dummytable(T) - convert categorical variables in table to dummy
% variables
%
% This function takes the categorical variables in a table and converts
% them to separate dummy variables with intelligent names. This way they
% can be used in the Classification Learner App and the variable names make
% sense for feature selection, etc.
%% Usage:
%% Tdummy = dummytable(T)
%% Inputs:
%% T: Table with categoricals or categorical variable
%% Outputs:
%% Tdummy: T with categorical variables turned into dummy variables with
% intelligent names
%% Example:
%% % Simple Table
% T = table(rand(10,1),categorical(cellstr('rbbgbgbbgr'.')),...
% 'VariableNames',{'Percent','Color'});
% disp(T)
%
% % Turn it into a dummy table
% Tdummy = dummytable(T);
% disp(Tdummy)
%% See Also: dummyvar, table, categorical, classificationLearner
% Copyright 2015 The MathWorks, Inc.
% Sean de Wolski Apr 13, 2014
% Error checking
narginchk(1,1) validateattributes(T,{'categorical', 'table'},{},mfilename,'T',1); % If it's a categorical, do out best to convert it to a table with an
% intelligent variable name
if iscategorical(T) % Try to use existing variable name
cname = inputname(1); if isempty(cname) % It's a MATLAB Expression, default to Var1
cname = 'Var1'; end T = table(T,'VariableNames',{cname}); end % Identify categoricals and their names
cats = varfun(@iscategorical,T,'OutputFormat','uniform'); % Short circuit if there are no categoricals
if ~any(cats) Tdummy = T; return end % Store everything in a cell. w will be the total width of the table
% with each variable dummyvar'd
w = nnz(~cats)+sum(varfun(@(x)numel(categories(x)),T(:,cats),'OutputFormat','uniform')); % Preallocate storage
datastorage = cell(1,w); namestorage = cell(1,w); % Engine
idx = 0; % Start nowhere in cell
for ii = 1:width(T) idx = idx+1; % Loop over table deciding what to do with each variable
if cats(ii) % It's a categorical,
% Extract it and build keep its categories and dummyvar
Tii = T{:,ii}; categoriesii = categories(Tii)'; ncatii = numel(categoriesii); % How many?
% Build dummy var as a row cell with columns in each
dvii = num2cell(dummyvar(Tii), 1); % Dummy var then cell
% Build names
namesii = strcat(T.Properties.VariableNames{ii}, '_', categoriesii); % Insert
datastorage(idx:(idx+ncatii-1)) = dvii; namestorage(idx:(idx+ncatii-1)) = namesii; % Increment
idx = idx+ncatii-1; else % Extract non categorical into current storage location
datastorage{idx} = T{:,ii}; namestorage(idx) = T.Properties.VariableNames(ii); end end % Build Tdummy with comma separated list expansion
Tdummy = table(datastorage{:},'VariableNames',matlab.lang.makeValidName(namestorage));endThe reason behind the error is that the content of your dataset, under the variable name 'key1' must be of type 'char", and not "double", as in your case.
To circumvent this issue, you need to do a data-type conversion. Here is an example that illustrates that. Say, your variable name tagged "key1" is located in column "idx1" in "dataset1" and in column "idx2" in "dataset2".
>> varName1 = dataset1.Properties.VarNames(idx1);>> cellData1 = dataset2cell(dataset1(:,idx1));>> data1 = cellData1(2:end,2);>> data1 = cellfun(@num2str,data1,'UniformOutput',false);>> ds1 = dataset(data1,'Varnames',varName1,'ObsNames',dataset1.Properties.ObsNames);>> dataset1(:,idx1) = ds1;>> varName2 = StatSet.Properties.VarNames(idx2);>> cellData2 = dataset2cell(dataset2(:,idx2));>> data2 = cellData2(2:end,2);>> data2 = cellfun(@num2str,data2,'UniformOutput',false);>> ds2 = dataset(data2,'Varnames',varName2,'ObsNames',dataset2.Properties.ObsNames);dataset2(:,idx2) = ds2;C = join(dataset1,dataset2,'Key','key1');
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