# From: http://archive.ics.uci.edu/ml/datasets/Statlog+(German+Credit+Data)
d <- read.table('german.data',
sep = " ",
stringsAsFactors = FALSE, header = FALSE)
colnames(d) <- c('Status_of_existing_checking_account', 'Duration_in_month',
'Credit_history', 'Purpose', 'Credit_amount', 'Savings_account_bonds',
'Present_employment_since',
'Installment_rate_in_percentage_of_disposable_income',
'Personal_status_and_sex', 'Other_debtors_guarantors',
'Present_residence_since', 'Property', 'Age_in_years',
'Other_installment_plans', 'Housing',
'Number_of_existing_credits_at_this_bank', 'Job',
'Number_of_people_being_liable_to_provide_maintenance_for',
'Telephone', 'foreign_worker', 'Good_Loan')
str(d)## 'data.frame': 1000 obs. of 21 variables:
## $ Status_of_existing_checking_account : chr "A11" "A12" "A14" "A11" ...
## $ Duration_in_month : int 6 48 12 42 24 36 24 36 12 30 ...
## $ Credit_history : chr "A34" "A32" "A34" "A32" ...
## $ Purpose : chr "A43" "A43" "A46" "A42" ...
## $ Credit_amount : int 1169 5951 2096 7882 4870 9055 2835 6948 3059 5234 ...
## $ Savings_account_bonds : chr "A65" "A61" "A61" "A61" ...
## $ Present_employment_since : chr "A75" "A73" "A74" "A74" ...
## $ Installment_rate_in_percentage_of_disposable_income : int 4 2 2 2 3 2 3 2 2 4 ...
## $ Personal_status_and_sex : chr "A93" "A92" "A93" "A93" ...
## $ Other_debtors_guarantors : chr "A101" "A101" "A101" "A103" ...
## $ Present_residence_since : int 4 2 3 4 4 4 4 2 4 2 ...
## $ Property : chr "A121" "A121" "A121" "A122" ...
## $ Age_in_years : int 67 22 49 45 53 35 53 35 61 28 ...
## $ Other_installment_plans : chr "A143" "A143" "A143" "A143" ...
## $ Housing : chr "A152" "A152" "A152" "A153" ...
## $ Number_of_existing_credits_at_this_bank : int 2 1 1 1 2 1 1 1 1 2 ...
## $ Job : chr "A173" "A173" "A172" "A173" ...
## $ Number_of_people_being_liable_to_provide_maintenance_for: int 1 1 2 2 2 2 1 1 1 1 ...
## $ Telephone : chr "A192" "A191" "A191" "A191" ...
## $ foreign_worker : chr "A201" "A201" "A201" "A201" ...
## $ Good_Loan : int 1 2 1 1 2 1 1 1 1 2 ...
d$Good_Loan <- as.factor(ifelse(d$Good_Loan == 1, 'GoodLoan', 'BadLoan'))
mapping <- c('A11' = '... < 0 DM',
'A12' = '0 < = ... < 200 DM',
'A13' = '... > = 200 DM / salary assignments for at least 1 year',
'A14' = 'no checking account',
'A30' = 'no credits taken/all credits paid back duly',
'A31' = 'all credits at this bank paid back duly',
'A32' = 'existing credits paid back duly till now',
'A33' = 'delay in paying off in the past',
'A34' = 'critical account/other credits existing (not at this bank)',
'A40' = 'car (new)',
'A41' = 'car (used)',
'A42' = 'furniture/equipment',
'A43' = 'radio/television',
'A44' = 'domestic appliances',
'A45' = 'repairs',
'A46' = 'education',
'A47' = '(vacation - does not exist?)',
'A48' = 'retraining',
'A49' = 'business',
'A410' = 'others',
'A61' = '... < 100 DM',
'A62' = '100 < = ... < 500 DM',
'A63' = '500 < = ... < 1000 DM',
'A64' = '.. > = 1000 DM',
'A65' = 'unknown/ no savings account',
'A71' = 'unemployed',
'A72' = '... < 1 year',
'A73' = '1 < = ... < 4 years',
'A74' = '4 < = ... < 7 years',
'A75' = '.. > = 7 years',
'A91' = 'male : divorced/separated',
'A92' = 'female : divorced/separated/married',
'A93' = 'male : single',
'A94' = 'male : married/widowed',
'A95' = 'female : single',
'A101' = 'none',
'A102' = 'co-applicant',
'A103' = 'guarantor',
'A121' = 'real estate',
'A122' = 'if not A121 : building society savings agreement/life insurance',
'A123' = 'if not A121/A122 : car or other, not in attribute 6',
'A124' = 'unknown / no property',
'A141' = 'bank',
'A142' = 'stores',
'A143' = 'none',
'A151' = 'rent',
'A152' = 'own',
'A153' = 'for free',
'A171' = 'unemployed/ unskilled - non-resident',
'A172' = 'unskilled - resident',
'A173' = 'skilled employee / official',
'A174' = 'management/ self-employed/highly qualified employee/ officer',
'A191' = 'none',
'A192' = 'yes, registered under the customers name',
'A201' = 'yes',
'A202' = 'no')
for(ci in colnames(d)) {
if(is.character(d[[ci]])) {
d[[ci]] <- as.factor(mapping[d[[ci]]])
}
}
vars <- setdiff(colnames(d), 'Good_Loan')
creditdata <- d
saveRDS(creditdata, "creditdata.RDS")
# not part of GCD data- notional example for listing 1.3
tab1 <- as.table(matrix(data = c(50, 6, 0, 44), nrow = 2, ncol = 2))
dimnames(tab1) <- list('loan_as_pct_disposable_income' = c('LT.15pct','GT.15pct'),
'loan_quality_pop1' = c('goodloan', 'badloan'))
tab2 <- as.table(matrix(data = c(34,18,16,32), nrow = 2, ncol = 2))
dimnames(tab2) <- list('loan_as_pct_disposable_income' = c('LT.15pct', 'GT.15pct'),
'loan_quality_pop2' = c('goodloan', 'badloan'))
save(list = ls(), file = 'GCDData.RData')