How To Insert Multiple Variables For A Model In R

Introduction

 
In this article I am going to demonstrate how to insert relevant variables from dataset for a model in R. Using a combination of function aggregate with certain variables and dataset, we can select and include relevant variables from a dataset which can be used to create models in R. Using the combination function, we can combine several important variables to create a model.
 

Inserting the variables

 
In order to append multiple variables together to fit a model, we can use a combination function along with mathematical operators so as to combine two or more than two variables together. For example, we can use addition operator inside aggregate function to insert multiple variables for the creation of a model. To create a model in R, we can use various mathematical operators to insert relevant variables and to remove unnecessary variables.
 
We can define a formula along with arithmetic operators in lots of functions in R. One such function is the aggregate() function in which we append different relevant variables to create a model.
 
Now I will demonstrate the use of cluster function to incorporate several variables together. We will be using gscars dataset to demonstrate the use of aggregate function.
  1. > gscars  
  2.                      mg cyl  disp  hp drat    wt  qsec vs bn gr carb  
  3. Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4  
  4. Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4  
  5. Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1  
  6. Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1  
  7. Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2  
  8. Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1  
  9. Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4  
  10. Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2  
  11. Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2  
  12. Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4  
  13. Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4  
  14. Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3  
  15. Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3  
  16. Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3  
  17. Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4  
  18. Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4  
  19. Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4  
  20. Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1  
  21. Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2  
  22. Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1  
  23. Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1  
  24. Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2  
  25. AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2  
  26. Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4  
  27. Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2  
  28. Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1  
  29. Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2  
  30. Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2  
  31. Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4  
  32. Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6  
  33. Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8  
  34. Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2  
  35. >  
We will be using ~ operator inside aggregate function along with addition operator to insert multiple relevant variables in R. For example if there are three variables named a, b and c, then a ~ b + c means that aggregate function will create a model a, which will contain both variables b as well as c.
  1. > aggregate(mg ~ gr + bn, data = data, mean)  
  2.   gr bn      mg  
  3. 1    4  0 19.10667  
  4. 2    3  1 23.05000  
  5. 3    3  0 23.27500  
  6. 4    6  1 26.38000  
In the above aggregate function, there are three arguments. The first argument in the formula indicates that aggregate function represents mg as a function of gr and bn variables by appending gr and bn variables in the model calculating the mean and the second argument is the variable depicting the dataset. Using the above code, aggregate function creates a model in which gr and bn variables are included in the model to create a best fit model in R.
  1. > aggregate(wt ~ gr + disp, data = data, mean)  
  2.   wt gr      disp  
  3. 1    3  0 16.10667  
  4. 2    4  0 21.05000  
  5. 3    4  1 26.27500  
  6. 4    5  1 21.38000  
In the above aggregate function, there are three arguments. The first argument in the formula indicates that aggregate function represents wt as a function of gr and disp variables by appending gr and disp variables in the model calculating the mean and the second argument is the variable depicting the dataset. Using above code, aggregate function creates a model in which gr and disp variables are included in the model to create a best fit model in R.
  1. > aggregate(mg ~ disp + hp, data = data, mean)  
  2.     disp  hp  mg  
  3. 1   75.7  52 30.4  
  4. 2  146.7  62 24.4  
  5. 3   71.1  65 33.9  
  6. 4   78.7  66 32.4  
  7. 5   79.0  66 27.3  
  8. 6  120.3  91 26.0  
  9. 7  108.0  93 22.8  
  10. 8  140.8  95 22.8  
  11. 9  120.1  97 21.5  
  12. 10 225.0 105 18.1  
  13. 11 121.0 109 21.4  
  14. 12 160.0 110 21.0  
  15. 13 258.0 110 21.4  
  16. 14  95.1 113 30.4  
  17. 15 167.6 123 18.5  
  18. 16 304.0 150 15.2  
  19. 17 318.0 150 15.5  
  20. 18 145.0 175 19.7  
  21. 19 360.0 175 18.7  
  22. 20 400.0 175 19.2  
  23. 21 275.8 180 16.3  
  24. 22 472.0 205 10.4  
  25. 23 460.0 215 10.4  
  26. 24 440.0 230 14.7  
  27. 25 350.0 245 13.3  
  28. 26 360.0 245 14.3  
  29. 27 351.0 264 15.8  
  30. 28 301.0 335 15.0  
In the above aggregate function, there are three arguments. First argument in the formula indicates that aggregate function represents mg as a function of disp and hp variables by appending disp and hp variables in the model calculating the mean and the second argument is the variable depicting the dataset. Using the above code, aggregate function creates a model in which disp and hp variables are included in the model to create a best fit model in R.
  1. > aggregate(hp ~ mg + cyl, data = data, mean)  
  2.     mg cyl    hp  
  3. 1  21.4   4 109.0  
  4. 2  21.5   4  97.0  
  5. 3  22.8   4  94.0  
  6. 4  24.4   4  62.0  
  7. 5  26.0   4  91.0  
  8. 6  27.3   4  66.0  
  9. 7  30.4   4  82.5  
  10. 8  32.4   4  66.0  
  11. 9  33.9   4  65.0  
  12. 10 17.8   6 123.0  
  13. 11 18.1   6 105.0  
  14. 12 19.2   6 123.0  
  15. 13 19.7   6 175.0  
  16. 14 21.0   6 110.0  
  17. 15 21.4   6 110.0  
  18. 16 10.4   8 210.0  
  19. 17 13.3   8 245.0  
  20. 18 14.3   8 245.0  
  21. 19 14.7   8 230.0  
  22. 20 15.0   8 335.0  
  23. 21 15.2   8 165.0  
  24. 22 15.5   8 150.0  
  25. 23 15.8   8 264.0  
  26. 24 16.4   8 180.0  
  27. 25 17.3   8 180.0  
  28. 26 18.7   8 175.0  
  29. 27 19.2   8 175.0  
In the above aggregate function, there are three arguments. The first argument in the formula indicates that aggregate function represents hp as a function of m and cyl variables by appending mpg and cyl variables in the model calculating the mean and the second argument is the variable depicting the dataset. Using the above code, aggregate function creates a model in which mg and cyl variables are included in the model to create a best fit model in R.
 

Summary

 
In this article I demonstrated how to insert relevant variables from dataset for a model in R. Using a combination of function aggregate with certain variables and dataset, we can select and include relevant variables from a dataset which can be used to create models in R.