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R Data Set

Data Set

A data set is a set of data, usually presented in a table.

There is a popular built-in data set on R called "mtcars" (Motor Trend Car Road Tests), available in 1974 Motor Trend US Magazine.

In the examples below (and in the following chapters), we will use a data set for mtcars, for mathematical purposes:

Example

Result

                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
    Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
    Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
    Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
    Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
    Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
    Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
    Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
    Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
    Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
    Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
    Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
    Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
    Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
    Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
    Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
    Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
    Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
    Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
    Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
    Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
    Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
    Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
    AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
    Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
    Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
    Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
    Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
    Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
    Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
    Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
    Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
    Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
    

Information About the Data Set

You can use the question mark (?) To get information about a mtcars data set:

Example

Result

mtcars

require(graphics)
    pairs(mtcars, main = "mtcars data", gap = 1/4)
    coplot(mpg ~ disp | as.factor(cyl), data = mtcars,
           panel = panel.smooth, rows = 1)
    ## possibly more meaningful, e.g., for summary() or bivariate plots:
    mtcars2 <- within(mtcars, {
       vs <- factor(vs, labels = c("V", "S"))
       am <- factor(am, labels = c("automatic", "manual"))
       cyl  <- ordered(cyl)
       gear <- ordered(gear)
       carb <- ordered(carb)
    })
    summary(mtcars2)
    

    

Get Information

Use the dim() function to determine the size of the data set, and the function of the names() to view dynamic words:

Example

Result

[1] 32 11
    [1] "mpg"  "cyl"  "disp" "hp"   "drat" "wt"   "qsec" "vs"   "am"   "gear"
   [11] "carb"
   

Use the rownames() function to find the name of each row in the first column, which is the name of each car:

Example

Result

 [1] "Mazda RX4"           "Mazda RX4 Wag"       "Datsun 710"         
    [4] "Hornet 4 Drive"      "Hornet Sportabout"   "Valiant"            
    [7] "Duster 360"          "Merc 240D"           "Merc 230"           
   [10] "Merc 280"            "Merc 280C"           "Merc 450SE"         
   [13] "Merc 450SL"          "Merc 450SLC"         "Cadillac Fleetwood" 
   [16] "Lincoln Continental" "Chrysler Imperial"   "Fiat 128"           
   [19] "Honda Civic"         "Toyota Corolla"      "Toyota Corona"      
   [22] "Dodge Challenger"    "AMC Javelin"         "Camaro Z28"         
   [25] "Pontiac Firebird"    "Fiat X1-9"           "Porsche 914-2"      
   [28] "Lotus Europa"        "Ford Pantera L"      "Ferrari Dino"       
   [31] "Maserati Bora"       "Volvo 142E"         

From the examples above, we found that the data set has 32 variables (Mazda RX4, Mazda RX4 Wag, Datsun 710, etc.) and 11 variables (mpg, cyl, disp, etc.).

Variables are defined as something that can be measured or calculated.

Here is a brief description of the variations from the mtcars data set:

Print Variable Value

If you want to print all the variable values, access the data frame using the $ symbol, and the variable name (for example cyl (cylinders)):

Example

Result

 [1] 6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4

Sort Variable Values

To sort values, use the sort() function:

Example

Result

 [1] 4 4 4 4 4 4 4 4 4 4 4 6 6 6 6 6 6 6 8 8 8 8 8 8 8 8 8 8 8 8 8 8

In the examples above, we see that most cars have 4 and 8 cylinders.

Analyzing the Data

Now that we have some information about the data set, we can begin to analyze it with other statistical numbers.

For example, we can use the summary() function to obtain a mathematical summary:

Example

Don't worry if you do not understand the numbers coming out. You will know better soon.

The summary() function returns six statistical numbers for each variable:

• Min
• First quantile (percentile)
• Median
• Mean
• Third quantile (percentile)
• Max

We will cover them all, along with other mathematical numbers in the following chapters.

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