Dataset reshape and create new columns

Hello all,

I am have problems with dataset reshaping and wrangling. I have a file with dataset in the form as shown in the example data attached, every 4 columns are in one group, the first row is the sample ID, and the second row contains the information of the variables. I am wondering whether there are any ways to concatenate all the groups vertically, and create new column names with the information from the sample ID (specifically, text before underscore as "SampleType", and that after the underscore as "replicateID"). In the end the output dataset should have the original 4 columns and the newly added two columns.

Thank you very much for the help!

Answer 1

[Voss]

Here's one way to do it:

% read the file:
data = readcell('example_data.csv');
data(1:10,:)

ans = 10×8 cell array
    {'nonyeasted_19'}    {'nonyeasted_19'}    {'nonyeasted_19'}    {'nonyeasted_19'}    {'yeasted_01'}    {'yeasted_01'  }    {'yeasted_01'}    {'yeasted_01'}
    {'Force (N)'    }    {'Distance (m)' }    {'Time (sec)'   }    {'Status'       }    {'Force (N)' }    {'Distance (m)'}    {'Time (sec)'}    {'Status'    }
    {[            0]}    {[            0]}    {[            0]}    {[          101]}    {[         0]}    {[           0]}    {[         0]}    {[       101]}
    {[      -0.0077]}    {[            0]}    {[       0.0020]}    {[            1]}    {[    0.0024]}    {[           0]}    {[    0.0020]}    {[         1]}
    {[       0.0023]}    {[            0]}    {[       0.0040]}    {[            1]}    {[    0.0307]}    {[           0]}    {[    0.0040]}    {[         1]}
    {[      -0.0707]}    {[            0]}    {[       0.0060]}    {[            1]}    {[   -0.0487]}    {[           0]}    {[    0.0060]}    {[         1]}
    {[      -0.2155]}    {[            0]}    {[       0.0080]}    {[            1]}    {[   -0.2063]}    {[           0]}    {[    0.0080]}    {[         1]}
    {[      -0.2026]}    {[            0]}    {[       0.0100]}    {[            1]}    {[   -0.1928]}    {[           0]}    {[    0.0100]}    {[         1]}
    {[      -0.0628]}    {[   2.0000e-06]}    {[       0.0120]}    {[            1]}    {[   -0.0421]}    {[           0]}    {[    0.0120]}    {[         1]}
    {[      -0.0481]}    {[   4.0000e-06]}    {[       0.0140]}    {[            1]}    {[   -0.0278]}    {[  1.0000e-06]}    {[    0.0140]}    {[         1]}

% get the types and ids:
C = regexp(data(1,:),'(.+)_(.+)','tokens');
C = [C{:}];
type_id = vertcat(C{:})

type_id = 8×2 cell array
    {'nonyeasted'}    {'19'}
    {'nonyeasted'}    {'19'}
    {'nonyeasted'}    {'19'}
    {'nonyeasted'}    {'19'}
    {'yeasted'   }    {'01'}
    {'yeasted'   }    {'01'}
    {'yeasted'   }    {'01'}
    {'yeasted'   }    {'01'}

% do the reshaping and column-prepending operations:
[nrows,ncols] = size(data);
new_data = ['SampleType' 'replicateID' data(2,1:4); ... % first row: variable names
    repelem(type_id,(nrows-2)/4,1) ... % first two columns: types and IDs
    reshape(permute(reshape(data(3:end,:),[],4,ncols/4),[1 3 2]),[],4)]; % columns 3-6: numeric data cells

% fill "missing" cells with a scalar NaN value:
new_data(cellfun(@(x)isa(x,'missing'),new_data)) = {NaN};

new_data(1:10,:) % check the top

ans = 10×6 cell array
    {'SampleType'}    {'replicateID'}    {'Force (N)'}    {'Distance (m)'}    {'Time (sec)'}    {'Status'}
    {'nonyeasted'}    {'19'         }    {[        0]}    {[           0]}    {[         0]}    {[   101]}
    {'nonyeasted'}    {'19'         }    {[  -0.0077]}    {[           0]}    {[    0.0020]}    {[     1]}
    {'nonyeasted'}    {'19'         }    {[   0.0023]}    {[           0]}    {[    0.0040]}    {[     1]}
    {'nonyeasted'}    {'19'         }    {[  -0.0707]}    {[           0]}    {[    0.0060]}    {[     1]}
    {'nonyeasted'}    {'19'         }    {[  -0.2155]}    {[           0]}    {[    0.0080]}    {[     1]}
    {'nonyeasted'}    {'19'         }    {[  -0.2026]}    {[           0]}    {[    0.0100]}    {[     1]}
    {'nonyeasted'}    {'19'         }    {[  -0.0628]}    {[  2.0000e-06]}    {[    0.0120]}    {[     1]}
    {'nonyeasted'}    {'19'         }    {[  -0.0481]}    {[  4.0000e-06]}    {[    0.0140]}    {[     1]}
    {'nonyeasted'}    {'19'         }    {[  -0.0601]}    {[  6.0000e-06]}    {[    0.0160]}    {[     1]}

new_data(nrows+(-5:4),:) % check the transition from nonyeasted to yeasted

ans = 10×6 cell array
    {'nonyeasted'}    {'19'}    {[-0.2483]}    {[-0.0809]}    {[90.6540]}    {[  1]}
    {'nonyeasted'}    {'19'}    {[-0.2420]}    {[-0.0809]}    {[90.6560]}    {[  1]}
    {'nonyeasted'}    {'19'}    {[-0.2355]}    {[-0.0809]}    {[90.6580]}    {[  1]}
    {'nonyeasted'}    {'19'}    {[-0.2441]}    {[-0.0809]}    {[90.6600]}    {[  1]}
    {'nonyeasted'}    {'19'}    {[-0.2484]}    {[-0.0809]}    {[90.6620]}    {[  1]}
    {'yeasted'   }    {'01'}    {[      0]}    {[      0]}    {[      0]}    {[101]}
    {'yeasted'   }    {'01'}    {[ 0.0024]}    {[      0]}    {[ 0.0020]}    {[  1]}
    {'yeasted'   }    {'01'}    {[ 0.0307]}    {[      0]}    {[ 0.0040]}    {[  1]}
    {'yeasted'   }    {'01'}    {[-0.0487]}    {[      0]}    {[ 0.0060]}    {[  1]}
    {'yeasted'   }    {'01'}    {[-0.2063]}    {[      0]}    {[ 0.0080]}    {[  1]}

% write the new file:
writecell(new_data,'example_data_modified.csv');

Read the documentation for reshape, permute, and repelem to understand how they are used here.

[Shengyue Shan]

Hello _, thank you so much for your help!!!! It works perfectly!

[Voss]

Excellent! You're welcome!