Credit
> Credit = read.csv("Credit.csv", header = T, na.strings = "?", stringsAsFactors = T)
> head(Credit)
Income Limit Rating Cards Age Education Own Student Married Region Balance
1 14.891 3606 283 2 34 11 No No Yes South 333
2 106.025 6645 483 3 82 15 Yes Yes Yes West 903
3 104.593 7075 514 4 71 11 No No No West 580
4 148.924 9504 681 3 36 11 Yes No No West 964
5 55.882 4897 357 2 68 16 No No Yes South 331
6 80.180 8047 569 4 77 10 No No No South 1151
To get the correlation of “Own”, “Student”, “Married”, and “Region”, these need to be converted to dummy columns with numbers.
Credit = read.csv("Credit.csv", header = T, na.strings = "?", stringsAsFactors = T)
Credit$Own <- as.integer(Credit$Own == "Yes")
Credit$Student <- as.integer(Credit$Student == "Yes")
Credit$Married <- as.integer(Credit$Married == "Yes")
Credit$Region <- which(c("North", "East", "South", "West") == Credit$Region)
cor(Credit)
Income Limit Rating Cards Age
Income 1.00000000 0.792088341 0.791377625 -0.018272610 0.175338403
Limit 0.79208834 1.000000000 0.996879737 0.010231333 0.100887922
Rating 0.79137763 0.996879737 1.000000000 0.053239030 0.103164996
Cards -0.01827261 0.010231333 0.053239030 1.000000000 0.042948288
Age 0.17533840 0.100887922 0.103164996 0.042948288 1.000000000
Education -0.02769198 -0.023548534 -0.030135627 -0.051084217 0.003619285
Own -0.01073751 0.009396678 0.008884590 -0.022658021 0.004015496
Student 0.01963214 -0.006015094 -0.002027646 -0.026164127 -0.029844426
Married 0.03565236 0.031154829 0.036750773 -0.009695060 -0.073135503
Region -0.02066518 -0.032402812 -0.032141719 0.004715417 -0.023056881
Balance 0.46365646 0.861697267 0.863625161 0.086456347 0.001835119
Education Own Student Married Region
Income -0.027691982 -0.010737514 0.019632138 0.03565236 -0.020665175
Limit -0.023548534 0.009396678 -0.006015094 0.03115483 -0.032402812
Rating -0.030135627 0.008884590 -0.002027646 0.03675077 -0.032141719
Cards -0.051084217 -0.022658021 -0.026164127 -0.00969506 0.004715417
Age 0.003619285 0.004015496 -0.029844426 -0.07313550 -0.023056881
Education 1.000000000 -0.005049071 0.072085400 0.04891059 -0.010934927
Own -0.005049071 1.000000000 0.055033718 0.01245171 -0.014388917
Student 0.072085400 0.055033718 1.000000000 -0.07697370 -0.074657367
Married 0.048910587 0.012451711 -0.076973701 1.00000000 -0.019947897
Region -0.010934927 -0.014388917 -0.074657367 -0.01994790 1.000000000
Balance -0.008061576 0.021474007 0.259017547 -0.00567349 -0.037271955
Balance
Income 0.463656457
Limit 0.861697267
Rating 0.863625161
Cards 0.086456347
Age 0.001835119
Education -0.008061576
Own 0.021474007
Student 0.259017547
Married -0.005673490
Region -0.037271955
Balance 1.000000000