Exercise 1 Download the dataframe credit.csv from http://nathanieldphillips.com/wp-content/uploads/2015/05/credit.txt. The data are stored in a comma-separated text file with headers. Load the dataframe into an object called credit.
credit <- read.table(file = "http://nathanieldphillips.com/wp-content/uploads/2015/05/credit.txt", header = T,
sep = ",", stringsAsFactors = F)Exercise 2 How many rows and columns are in credit?
dim(credit)## [1] 1000 17nrow(credit)## [1] 1000ncol(credit)## [1] 17Exercise 3 What are the names of the columns of credit?
names(credit)## [1] "checking_balance" "months_loan_duration" "credit_history"
## [4] "purpose" "amount" "savings_balance"
## [7] "employment_duration" "percent_of_income" "years_at_residence"
## [10] "age" "other_credit" "housing"
## [13] "existing_loans_count" "job" "dependents"
## [16] "phone" "default"Exercise 4 Add a new column to the dataframe called amount.eur that converts the loan amount in DM to euros. (1 DM is about .5 EUR)
amount.dm <- 0.5
credit$amount.eur <- credit$amount / amount.dm
head(credit$amount.eur)## [1] 2338 11902 4192 15764 9740 18110head(credit)## checking_balance months_loan_duration credit_history
## 1 < 0 DM 6 critical
## 2 1 - 200 DM 48 good
## 3 unknown 12 critical
## 4 < 0 DM 42 good
## 5 < 0 DM 24 poor
## 6 unknown 36 good
## purpose amount savings_balance employment_duration
## 1 furniture/appliances 1169 unknown > 7 years
## 2 furniture/appliances 5951 < 100 DM 1 - 4 years
## 3 education 2096 < 100 DM 4 - 7 years
## 4 furniture/appliances 7882 < 100 DM 4 - 7 years
## 5 car 4870 < 100 DM 1 - 4 years
## 6 education 9055 unknown 1 - 4 years
## percent_of_income years_at_residence age other_credit housing
## 1 4 4 67 none own
## 2 2 2 22 none own
## 3 2 3 49 none own
## 4 2 4 45 none other
## 5 3 4 53 none other
## 6 2 4 35 none other
## existing_loans_count job dependents phone default amount.eur
## 1 2 skilled 1 yes no 2338
## 2 1 skilled 1 no yes 11902
## 3 1 unskilled 2 no no 4192
## 4 1 skilled 2 no no 15764
## 5 2 skilled 2 no yes 9740
## 6 1 unskilled 2 yes no 18110Exercise 5 What is the median, mean, and standard deviation of the loan amounts in EUR?
sd(credit$amount.eur)## [1] 5645.474mean(credit$amount.eur)## [1] 6542.516median(credit$amount.eur)## [1] 4639Exercise 6 What was the most common purpose for getting a loan? What was the least common reason?
unique(credit$purpose)## [1] "furniture/appliances" "education" "car"
## [4] "business" "renovations"purpose.sort <- table(credit$purpose)
purpose.sort##
## business car education
## 97 349 59
## furniture/appliances renovations
## 473 22sort(purpose.sort, decrease = F)##
## renovations education business
## 22 59 97
## car furniture/appliances
## 349 473Exercise 7 What percent of people got the loan for either education or a car?
c<- credit$purpose == "car" | credit$purpose == "education"
c## [1] FALSE FALSE TRUE FALSE TRUE TRUE FALSE TRUE FALSE TRUE TRUE
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## [991] TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE TRUEsum(c)## [1] 408mean(c)## [1] 0.408Exercise 8 What percentage of the people had a good credit history?
head(credit)## checking_balance months_loan_duration credit_history
## 1 < 0 DM 6 critical
## 2 1 - 200 DM 48 good
## 3 unknown 12 critical
## 4 < 0 DM 42 good
## 5 < 0 DM 24 poor
## 6 unknown 36 good
## purpose amount savings_balance employment_duration
## 1 furniture/appliances 1169 unknown > 7 years
## 2 furniture/appliances 5951 < 100 DM 1 - 4 years
## 3 education 2096 < 100 DM 4 - 7 years
## 4 furniture/appliances 7882 < 100 DM 4 - 7 years
## 5 car 4870 < 100 DM 1 - 4 years
## 6 education 9055 unknown 1 - 4 years
## percent_of_income years_at_residence age other_credit housing
## 1 4 4 67 none own
## 2 2 2 22 none own
## 3 2 3 49 none own
## 4 2 4 45 none other
## 5 3 4 53 none other
## 6 2 4 35 none other
## existing_loans_count job dependents phone default amount.eur
## 1 2 skilled 1 yes no 2338
## 2 1 skilled 1 no yes 11902
## 3 1 unskilled 2 no no 4192
## 4 1 skilled 2 no no 15764
## 5 2 skilled 2 no yes 9740
## 6 1 unskilled 2 yes no 18110history.g <- credit$credit_history == "good"
mean(history.g)## [1] 0.53Exercise 9 Of those people with a critical credit history, what was the median loan amount in EUR? What about for people with a good credit history?
median(subset(x = credit, subset = credit_history == "critical")$amount.eur)## [1] 4362median(subset(x = credit, subset = credit_history == "good")$amount.eur)## [1] 4435Exercise 10 Was there a relationship between a borrower’s age and their loan amount in EUR? Test this in two ways: once using a correlation, and once by comparing the average loan amount of people whose age is above the median age to those whose age is less than the median age.
cor(credit$age, credit$amount.eur)## [1] 0.03271642median.age <- median(credit$age)
median.age## [1] 33above <- mean(subset(x = credit, subset = credit$age > median.age)$amount.eur)
above## [1] 6708.62under <- mean(subset(x = credit, subset = credit$age < median.age)$amount.eur)
under## [1] 6459.685compare <- above == under
compare## [1] FALSEExercise 11 Did people with a savings balance of less than 100 DM have a different default rate than those with a savings balance greater than 500 DM?
head(credit)## checking_balance months_loan_duration credit_history
## 1 < 0 DM 6 critical
## 2 1 - 200 DM 48 good
## 3 unknown 12 critical
## 4 < 0 DM 42 good
## 5 < 0 DM 24 poor
## 6 unknown 36 good
## purpose amount savings_balance employment_duration
## 1 furniture/appliances 1169 unknown > 7 years
## 2 furniture/appliances 5951 < 100 DM 1 - 4 years
## 3 education 2096 < 100 DM 4 - 7 years
## 4 furniture/appliances 7882 < 100 DM 4 - 7 years
## 5 car 4870 < 100 DM 1 - 4 years
## 6 education 9055 unknown 1 - 4 years
## percent_of_income years_at_residence age other_credit housing
## 1 4 4 67 none own
## 2 2 2 22 none own
## 3 2 3 49 none own
## 4 2 4 45 none other
## 5 3 4 53 none other
## 6 2 4 35 none other
## existing_loans_count job dependents phone default amount.eur
## 1 2 skilled 1 yes no 2338
## 2 1 skilled 1 no yes 11902
## 3 1 unskilled 2 no no 4192
## 4 1 skilled 2 no no 15764
## 5 2 skilled 2 no yes 9740
## 6 1 unskilled 2 yes no 18110less.100 <- subset(x = credit,
subset = credit$savings_balance == "< 100 DM")$default == "yes"
less.100## [1] TRUE FALSE FALSE TRUE FALSE TRUE TRUE TRUE FALSE TRUE FALSE
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## [595] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUEmean.less.100 <- mean(less.100)
mean.less.100## [1] 0.3598673unique(credit$savings_balance)## [1] "unknown" "< 100 DM" "500 - 1000 DM" "> 1000 DM"
## [5] "100 - 500 DM"over.500 <- subset(x = credit, subset = credit$savings_balance == "500-1000 DM" | credit$savings_balance == "> 1000 DM")$default == "yes"
mean.over.500 <- mean(over.500)
mean.over.500## [1] 0.125mean.over.500 == mean.less.100## [1] FALSE