Filter a table by 2 columns

Hi, I'm using 'patients' (load patients) to learn more about tables. I'm trying to filter the table by gender and smoker status by following

load patients
T = table(LastName,Age,Smoker,Gender)
Tsmokermale=(T.Smoker==true| T.Gender=='Male');

but get the error

Undefined operator '==' for input arguments of type 'cell'.

How can I filter the table by male and smoker, and still keep the information of LastName and Age?

[madhan ravi]

please show how you are forming a table

[Orongo]

T = table(LastName,Age,Smoker,Gender)

Answer 1

[Peter Perkins]

Orongo, do this:

T.Gender = categorical(T.Gender);

Then this

Tsmokermale=(T.Smoker==true| T.Gender=='Male');

works as you expect, and what you are really asking about is this

T(Tsmokermale,:)

Long story short: Gender in the mat file is what's called a cell array of char vectors (a.k.a. a "cellstr"). Those don't support testing using ==, thus KSSV's suggestion of strcmp. But if you convert them to categorical, you can use ==. You could also convert to string, but categorical is more appropriate in this case.

[Ishwor Koirala]

That worked for me. Thank you very much :)

Answer 2

[KSSV]

idx = strcmp(T.Gender,'Male') ;

[Orongo]

This works fine for filtering the gender, just what I want. However, I'm missing the information about the row data. So, in my table below with heading {LastName, Age, Smoker, Gender} I get answer 1s and 0s with the command, but I want the last name, age and gender to filter through too.

'Smith'	38	1	'Male'
'Johnson'	43	0	'Male'
'Williams'	38	0	'Female'
'Jones'	40	0	'Female'
'Brown'	49	0	'Female'
'Davis'	46	0	'Female'
'Miller'	33	1	'Female'
'Wilson'	40	0	'Male'
'Moore'	28	0	'Male'
'Taylor'	31	0	'Female'
'Anderson'	45	0	'Female'
'Thomas'	42	0	'Female'
'Jackson'	25	0	'Male'
'White'	39	1	'Male'
'Harris'	36	0	'Female'
'Martin'	48	1	'Male'
'Thompson'	32	1	'Male'
'Garcia'	27	1	'Female'
'Martinez'	37	0	'Male'
'Robinson'	50	0	'Male'
'Clark'	48	0	'Female'
'Rodriguez'	39	0	'Female'
'Lewis'	41	0	'Female'
'Lee'	44	1	'Female'
'Walker'	28	1	'Female'
'Hall'	25	0	'Male'
'Allen'	39	0	'Female'
'Young'	25	0	'Female'
'Hernandez'	36	0	'Male'
'King'	30	1	'Male'
'Wright'	45	1	'Female'
'Lopez'	40	0	'Female'
'Hill'	25	0	'Female'
'Scott'	47	0	'Male'
'Green'	44	0	'Male'
'Adams'	48	0	'Female'
'Baker'	44	1	'Male'
'Gonzalez'	35	0	'Female'
'Nelson'	33	1	'Male'
'Carter'	38	0	'Female'
'Mitchell'	39	1	'Male'
'Perez'	44	0	'Male'
'Roberts'	44	1	'Male'
'Turner'	37	1	'Male'
'Phillips'	45	0	'Male'
'Campbell'	37	0	'Female'
'Parker'	30	0	'Male'
'Evans'	39	0	'Female'
'Edwards'	42	0	'Male'
'Collins'	42	1	'Male'
'Stewart'	49	1	'Male'
'Sanchez'	44	1	'Female'
'Morris'	43	1	'Female'
'Rogers'	47	0	'Female'
'Reed'	50	1	'Male'
'Cook'	38	0	'Female'
'Morgan'	41	0	'Female'
'Bell'	45	1	'Male'
'Murphy'	36	0	'Male'
'Bailey'	38	0	'Female'
'Rivera'	29	0	'Female'
'Cooper'	28	0	'Female'
'Richardson'	30	0	'Female'
'Cox'	28	0	'Female'
'Howard'	29	0	'Female'
'Ward'	36	0	'Male'
'Torres'	45	0	'Female'
'Peterson'	32	0	'Female'
'Gray'	31	0	'Female'
'Ramirez'	48	1	'Female'
'James'	25	0	'Male'
'Watson'	40	1	'Female'
'Brooks'	39	0	'Male'
'Kelly'	41	0	'Female'
'Sanders'	33	1	'Female'
'Price'	31	1	'Male'
'Bennett'	35	0	'Female'
'Wood'	32	0	'Male'
'Barnes'	42	0	'Male'
'Ross'	48	0	'Female'
'Henderson'	34	0	'Male'
'Coleman'	39	0	'Male'
'Jenkins'	28	1	'Male'
'Perry'	29	0	'Female'
'Powell'	32	0	'Female'
'Long'	39	1	'Male'
'Patterson'	37	1	'Female'
'Hughes'	49	1	'Female'
'Flores'	31	1	'Female'
'Washington'	37	0	'Female'
'Butler'	38	1	'Male'
'Simmons'	45	0	'Male'
'Foster'	30	0	'Female'
'Gonzales'	48	0	'Male'
'Bryant'	48	0	'Female'
'Alexander'	25	1	'Male'
'Russell'	44	1	'Male'
'Griffin'	49	0	'Male'
'Diaz'	45	1	'Male'
'Hayes'	48	0	'Male'