How do I only keep the rows that match the values of the rows in a different list?

15 vues (au cours des 30 derniers jours)
lil brain
lil brain le 17 Juil 2022
Commenté : Voss le 18 Juil 2022
Hi,
I have this large cell array (low_condition) that contains participant IDs in the first row. I have a second list of IDs (valid_IDs) that I would like to compare witht he cell array and then only keep those participant IDs that are found in the second list. All of the remaining IDs (rows) I want to have deleted. How do I do this?
Thanks

Réponses (1)

Voss
Voss le 17 Juil 2022
load low_condition
load valid_low_IDs
valid_low_IDs contains character vectors that appear to be file names:
valid_low_IDs
valid_low_IDs = 17×1 cell array
{'5beeea64a79efb0001faf30a.csv' } {'5db9e0044a8eb40431d5e782.csv' } {'5e8ef2059eeb7a1af7502f3c - edited.csv'} {'5e67640d82b4784eb93f1a32.csv' } {'5ed5aeea88fecc14779b4a29 - edited.csv'} {'5f8998f6bfe9d6067c4b8a72.csv' } {'5fc959ba27fabf4111b918ec - edited.csv'} {'5ff0a3d22c2ee6fa5d47c312 - edited.csv'} {'59a9c0291b7a550001d6392e.csv' } {'60b43c11dcee60f547e013ba.csv' } {'60fdf891d19fd767ebc5eefc.csv' } {'598fa1b80675b100014dacf3.csv' } {'614e51ba8bd7590a59e92f9c.csv' } {'6019ec157baab31e1ad590fc.csv' } {'6079c41a26b231701ceb1e0a.csv' } {'6089aa8120d7418a70f3eba7.csv' } {'60243c357e64550d96eb47f6.csv' }
So the non-ID parts of those file names need to be removed. Here's one way:
IDs = strrep(strrep(valid_low_IDs,' - edited',''),'.csv','')
IDs = 17×1 cell array
{'5beeea64a79efb0001faf30a'} {'5db9e0044a8eb40431d5e782'} {'5e8ef2059eeb7a1af7502f3c'} {'5e67640d82b4784eb93f1a32'} {'5ed5aeea88fecc14779b4a29'} {'5f8998f6bfe9d6067c4b8a72'} {'5fc959ba27fabf4111b918ec'} {'5ff0a3d22c2ee6fa5d47c312'} {'59a9c0291b7a550001d6392e'} {'60b43c11dcee60f547e013ba'} {'60fdf891d19fd767ebc5eefc'} {'598fa1b80675b100014dacf3'} {'614e51ba8bd7590a59e92f9c'} {'6019ec157baab31e1ad590fc'} {'6079c41a26b231701ceb1e0a'} {'6089aa8120d7418a70f3eba7'} {'60243c357e64550d96eb47f6'}
On the other hand, low_condition(:,1) is a cell array of (scalar) string arrays:
disp(low_condition(:,1))
{["59a9c0291b7a550001d6392e" ]} {["60243c357e64550d96eb47f6" ]} {["6019ec157baab31e1ad590fc" ]} {["614e51ba8bd7590a59e92f9c" ]} {["60fdf891d19fd767ebc5eefc" ]} {["5beeea64a79efb0001faf30a" ]} {["5fc959ba27fabf4111b918ec" ]} {["6079c41a26b231701ceb1e0a" ]} {["5d6069f53f69fd0001c0e77f" ]} {["6033acf84fec17043ffef374" ]} {["5efd8bbe8f20d51cd2ab9327" ]} {["608d4e91d08ff4d32dbba3ad" ]} {["60b43c11dcee60f547e013ba" ]} {["615d8b5b98addf4b3d65c41d" ]} {["5ff0a3d22c2ee6fa5d47c312" ]} {["60cef6bc855784dee78ffffd" ]} {["6089aa8120d7418a70f3eba7" ]} {["5c6400cbcb937d00012d8866" ]} {["611cf405ca55c42b27807c3c" ]} {["60b65dd419636fbf792d4470" ]} {["5db9e0044a8eb40431d5e782" ]} {["5dc32687547c1f24648b7c23" ]} {["61742bd3cd78a9f853168cef" ]} {["615c343a80f9bb6fd7362271" ]} {["615db68d9d021708b6e64603" ]} {["5e67640d82b4784eb93f1a32" ]} {["5f8998f6bfe9d6067c4b8a72" ]} {["60ac8807aaad354f950dd131" ]} {["608ac09acb49bef04cade936" ]} {["https://we.tl/t-eeVxbz35Vq?src=dnl"]} {["5e83b43765a5720e30b71b37" ]} {["5ed5aeea88fecc14779b4a29" ]} {["5d175bfe7a5f6d001a64b545" ]} {["598fa1b80675b100014dacf3" ]} {["5aa67d76f053610001726e65" ]} {["61133a6bb907d515d390d08b" ]} {["5e8ef2059eeb7a1af7502f3c" ]} {["" ]}
Convert it to a cell array of character vectors, stored here as cond:
cond = cellstr(low_condition(:,1));
disp(cond)
{'59a9c0291b7a550001d6392e' } {'60243c357e64550d96eb47f6' } {'6019ec157baab31e1ad590fc' } {'614e51ba8bd7590a59e92f9c' } {'60fdf891d19fd767ebc5eefc' } {'5beeea64a79efb0001faf30a' } {'5fc959ba27fabf4111b918ec' } {'6079c41a26b231701ceb1e0a' } {'5d6069f53f69fd0001c0e77f' } {'6033acf84fec17043ffef374' } {'5efd8bbe8f20d51cd2ab9327' } {'608d4e91d08ff4d32dbba3ad' } {'60b43c11dcee60f547e013ba' } {'615d8b5b98addf4b3d65c41d' } {'5ff0a3d22c2ee6fa5d47c312' } {'60cef6bc855784dee78ffffd' } {'6089aa8120d7418a70f3eba7' } {'5c6400cbcb937d00012d8866' } {'611cf405ca55c42b27807c3c' } {'60b65dd419636fbf792d4470' } {'5db9e0044a8eb40431d5e782' } {'5dc32687547c1f24648b7c23' } {'61742bd3cd78a9f853168cef' } {'615c343a80f9bb6fd7362271' } {'615db68d9d021708b6e64603' } {'5e67640d82b4784eb93f1a32' } {'5f8998f6bfe9d6067c4b8a72' } {'60ac8807aaad354f950dd131' } {'608ac09acb49bef04cade936' } {'https://we.tl/t-eeVxbz35Vq?src=dnl'} {'5e83b43765a5720e30b71b37' } {'5ed5aeea88fecc14779b4a29' } {'5d175bfe7a5f6d001a64b545' } {'598fa1b80675b100014dacf3' } {'5aa67d76f053610001726e65' } {'61133a6bb907d515d390d08b' } {'5e8ef2059eeb7a1af7502f3c' } {0×0 char }
Now you can compare the two using ismember, and keep the relevant rows of low_condition:
to_keep = ismember(cond,IDs);
low_condition_keep = low_condition(to_keep,:)
low_condition_keep = 17×49 cell array
{["59a9c0291b7a550001d6392e"]} {[2]} {[4]} {[3]} {[3]} {[4]} {[5]} {[5]} {[4]} {[4]} {[5]} {[5]} {[4]} {[5]} {[4]} {[5]} {[4]} {[3]} {[4]} {[4]} {[5]} {[4]} {[4]} {[3]} {[4]} {[4]} {[4]} {[5]} {[4]} {[2]} {[2]} {[3]} {[2]} {[4]} {[3]} {[4]} {[4]} {[2]} {[4]} {[3]} {[2]} {[4]} {[3]} {[4]} {[5]} {[4]} {[2]} {[4]} {[4]} {["60243c357e64550d96eb47f6"]} {[2]} {[1]} {[4]} {[4]} {[1]} {[5]} {[1]} {[4]} {[4]} {[4]} {[4]} {[3]} {[4]} {[4]} {[3]} {[2]} {[2]} {[4]} {[4]} {[3]} {[4]} {[2]} {[4]} {[4]} {[5]} {[4]} {[4]} {[4]} {[1]} {[2]} {[2]} {[5]} {[5]} {[4]} {[3]} {[4]} {[2]} {[5]} {[4]} {[4]} {[2]} {[4]} {[5]} {[5]} {[4]} {[2]} {[3]} {[5]} {["6019ec157baab31e1ad590fc"]} {[2]} {[3]} {[1]} {[3]} {[3]} {[5]} {[5]} {[4]} {[4]} {[5]} {[5]} {[3]} {[3]} {[3]} {[5]} {[4]} {[1]} {[4]} {[4]} {[5]} {[1]} {[2]} {[2]} {[3]} {[5]} {[3]} {[5]} {[5]} {[1]} {[2]} {[3]} {[3]} {[5]} {[1]} {[4]} {[4]} {[2]} {[5]} {[2]} {[2]} {[5]} {[5]} {[5]} {[3]} {[4]} {[1]} {[4]} {[5]} {["614e51ba8bd7590a59e92f9c"]} {[2]} {[2]} 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  2 commentaires
lil brain
lil brain le 17 Juil 2022
Wow this is great thank you!
Voss
Voss le 18 Juil 2022
You're welcome! Any questions, let me know. Otherwise, please "Accept This Answer". Thanks!

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