Introduction to Matlab

Contents

First see what is in the workspace. This command lists all variables in the workspace.

who

The workspace is empty and nothing is returned. Now load the iris.mat file.

load iris

What is in the workspace now?

who
Your variables are:

setosa      versicolor  virginica   

Now save the variables in another file. We will save just the setosa variable object. Use save filename varname.

save setosa setosa

See what files are in the current directory.

dir
.               Chapter4.m      Chapter8.m      
..              Chapter5.m      Introduction.m  
Chapter2.m      Chapter6.m      html            
Chapter3.m      Chapter7.m      setosa.mat      

%Remove objects from workspace to clean it up.
clear
%Load the earth data.
%

load DensityEarth.txt -ascii

See what is in the workspace.

who
Your variables are:

DensityEarth  

Create a vector x.

x = [2, 4, 6];

Delete the second element.

x(2) = [];

Display the vector x.

disp(x)
     2     6

Find the elements that are negative.

ind = find(x < 0);

Print the vector ind to the command window.

ind
ind =

   Empty matrix: 1-by-0

Create a cell array, where one cell contains

numbers and another cell element is a string.

cell_arry2 = {[1,2], 'This is a string'};

Let’s check the size of the cell array

size(cell_arry2)
ans =

     1     2

Create a structure called employee with three fields.

employee = struct(...
    'name',{{'Wendy','MoonJung'}},...
    'area',{{'Visualization','Inference'}},...
    'deg',{{'PhD','PhD'}},...
    'score',[90 100])

all_names = employee.name
employee = 

     name: {'Wendy'  'MoonJung'}
     area: {'Visualization'  'Inference'}
      deg: {'PhD'  'PhD'}
    score: [90 100]


all_names = 

    'Wendy'    'MoonJung'

load UStemps

Create a table using all four variables.

UTs_tab = table(City,JanTemp,Lat,Long)
UTs_tab = 

            City            JanTemp    Lat     Long 
    ____________________    _______    ____    _____

    'Mobile, AL'            44         31.2     88.5
    'Montgomery, AL'        38         32.9     86.8
    'Phoenix, AZ'           35         33.6    112.5
    'Little Rock, AR'       31         35.4     92.8
    'Los Angeles, CA'       47         34.3    118.7
    'San Francisco, CA'     42         38.4      123
    'Denver, CO'            15         40.7    105.3
    'New Haven, CT'         22         41.7     73.4
    'Wilmington, DE'        26         40.5     76.3
    'Washington, DC'        30         39.7     77.5
    'Jacksonville, FL'      45           31     82.3
    'Key West, FL'          65           25       82
    'Miami, FL'             58         26.3     80.7
    'Atlanta, GA'           37         33.9       85
    'Boise, ID'             22         43.7    117.1
    'Chicago, IL'           19         42.3       88
    'Indianapolis, IN'      21         39.8     86.9
    'Des Moines, IA'        11         41.8     93.6
    'Wichita, KS'           22         38.1     97.6
    'Louisville, KY'        27           39     86.5
    'New Orleans, LA'       45         30.8     90.2
    'Portland, ME'          12         44.2     70.5
    'Baltimore, MD'         25         39.7     77.3
    'Boston, MA'            23         42.7     71.4
    'Detroit, MI'           21         43.1     83.9
    'Minneapolis, MN'        2         45.9     93.9
    'St. Louis, MO'         24         39.3     90.5
    'Helena, MT'             8         47.1    112.4
    'Omaha, NE'             13         41.9     96.1
    'Concord, NH'           11         43.5     71.9
    'Atlantic City, NJ'     27         39.8     75.3
    'Albuquerque, NM'       24         35.1    106.7
    'Albany, NY'            14         42.6     73.7
    'New York, NY'          27         40.8     74.6
    'Charlotte, NC'         34         35.9     81.5
    'Raleigh, NC'           31         36.4     78.9
    'Bismarck, ND'           0         47.1      101
    'Cincinnati, OH'        26         39.2       85
    'Cleveland, OH'         21         42.3     82.5
    'Oklahoma City, OK'     28         35.9     97.5
    'Portland, OR'          33         45.6    123.2
    'Harrisburg, PA'        24         40.9     77.8
    'Philadelphia, PA'      24         40.9     75.5
    'Charleston, SC'        38         33.3     80.8
    'Nashville, TN'         31         36.7     87.6
    'Amarillo, TX'          24         35.6    101.9
    'Galveston, TX'         49         29.4     95.5
    'Houston, TX'           44         30.1     95.9
    'Salt Lake City, UT'    18         41.1    112.3
    'Burlington, VT'         7           45     73.9
    'Norfolk, VA'           32           37     76.6
    'Seattle, WA'           33         48.1    122.5
    'Spokane, WA'           19         48.1    117.9
    'Madison, WI'            9         43.4     90.2
    'Milwaukee, WI'         13         43.3     88.1
    'Cheyenne, WY'          14         41.2    104.9

See what is in the workspace.

whos
  Name               Size            Bytes  Class     Attributes

  City              56x1              7702  cell                
  DensityEarth      29x1               232  double              
  JanTemp           56x1               448  double              
  Lat               56x1               448  double              
  Long              56x1               448  double              
  UTs_tab           56x4             11232  table               
  all_names          1x2               250  cell                
  ans                1x2                16  double              
  cell_arry2         1x2               272  cell                
  employee           1x1              1474  struct              
  ind                1x0                 0  double              
  x                  1x2                16  double              

Extract the employee's area of interest.

e_area = employee.area;

Display the contents in the window.

e_area
e_area = 

    'Visualization'    'Inference'

Display Wendy's score.

employee.score(1)
ans =

    90

Get MoonJung's area.

employee.area{2}
ans =

Inference

Get a partial table by extracting the first

three rows.

U1 = UTs_tab(1:3,:)
U1 = 

          City          JanTemp    Lat     Long 
    ________________    _______    ____    _____

    'Mobile, AL'        44         31.2     88.5
    'Montgomery, AL'    38         32.9     86.8
    'Phoenix, AZ'       35         33.6    112.5

Get the JanTemp variable.

jt = UTs_tab.JanTemp;

See if it is equal to the JanTemp variable.

isequal(jt,JanTemp)
ans =

     1

We get an answer of 1, indicating they are the same.

We can extract the Lat and Long data for the first three cities using the variable names.

U2 = UTs_tab{1:3,{'Lat','Long'}}
U2 =

   1.0e+02 *

   0.312000000000000   0.885000000000000
   0.329000000000000   0.868000000000000
   0.336000000000000   1.125000000000000

First load the data back into the workspace.

load iris

Now, we use the semicolon to stack

setosa, versicolor, and virginica data objects.

irisAll = [setosa; versicolor; virginica];

Now look at the workspace to see what is there now.

who
Your variables are:

City          U1            cell_arry2    jt            
DensityEarth  U2            e_area        setosa        
JanTemp       UTs_tab       employee      versicolor    
Lat           all_names     ind           virginica     
Long          ans           irisAll       x             

Check on the size of irisAll.

size(irisAll)
ans =

   150     4

Load the data if not in the workspace.

load UStemps

Use commas to concatenate as a row.

UStemps = [JanTemp, Lat, Long];

Check the workspace.

who
Your variables are:

City          U1            ans           irisAll       x             
DensityEarth  U2            cell_arry2    jt            
JanTemp       UStemps       e_area        setosa        
Lat           UTs_tab       employee      versicolor    
Long          all_names     ind           virginica     

Verify the size of UStemps:

size(UStemps)
ans =

    56     3

Save the data for the UStemps to a .mat file.

save UStemps City JanTemp Lat Long

That used the command syntax to call a function.

Now use the function syntax.

save('USt.mat','City','JanTemp','Lat','Long')

See what is in our directory now.

dir
.               Chapter4.m      Chapter8.m      html            
..              Chapter5.m      Introduction.m  setosa.mat      
Chapter2.m      Chapter6.m      USt.mat         
Chapter3.m      Chapter7.m      UStemps.mat