1、用R語言讀取檔案中的資料

1.1、用R語言讀取結構化資料

以University of California Irvine Machine Learning Repository (http://archive.ics.uci.edu/ml/)的car資料為例：

uciCar <- read.table(
'http://www.win-vector.com/dfiles/car.data.csv',
sep=',',
header=T
)

完成讀取資料以後，我們需要對資料進行初步的觀察分析。需要用到一下幾個命令：

檢視資料的型別class()：

class(uciCar)

summary(uciCar)

有的資料並沒有很好的結構化，例如：http://mng.bz/mZbu 的資料

A11 6 A34 A43 1169 A65 A75 4 A93 A101 4 ...
A12 48 A32 A43 5951 A61 A73 2 A92 A101 2 ...
A14 12 A34 A46 20

d <- read.table(paste('http://archive.ics.uci.edu/ml/',
'machine-learning-databases/statlog/german/german.data',sep=''),
stringsAsFactors=F,header=F)
print(d[1:3,])
 

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')
d$Good.Loan <- as.factor(ifelse(d$Good.Loan==1,'GoodLoan','BadLoan'))
print(d[1:3,])
 

mapping <- list(
'A40'='car (new)',
'A41'='car (used)',
'A42'='furniture/equipment',
'A43'='radio/television',
'A44'='domestic appliances',
...
)

我們用迴圈把資料進行替換：

for(i in 1:(dim(d))[2]) {
    if(class(d[,i])=='character') {
        d[,i] <- as.factor(as.character(mapping[d[,i]]))
    }
}

print(d[1:3,'Purpose'])

https://github.com/WinVector/zmPDSwR/tree/master/Statlog/