Generating R datasets

Here, we show you how to generate an R dataset called iris.RData by using the R save() function:

path<-"http://archive.ics.uci.edu/ml/machine-learning-databases/" 
dataSet<-"iris/bezdekIris.data" 
a<-paste(path,dataSet,sep='') 
.iris<-read.csv(a,header=F) 
colnames(.iris)<-c("sepalLength","sepalWidth","petalLength","petalWidth","Class") 
save(iris,file="c:/temp/iris.RData") 

To upload the function, we use the load() function:

>load("c:/temp/iris.RData") 
> head(.iris) 
  sepalLength sepalWidth petalLength petalWidth       Class 
1         5.1        3.5         1.4        0.2 Iris-setosa 
2         4.9        3.0         1.4        0.2 Iris-setosa 
3         4.7        3.2         1.3        0.2 Iris-setosa 
4         4.6        3.1         1.5        0.2 Iris-setosa 
5         5.0        3.6         1.4        0.2 Iris-setosa 
6         5.4        3.9         1.7        0.4 Iris-setosa 

Note that the extension of .RData is not critical. The second way to save R data is to apply an R function called saveRDS(), shown in the code here:

inFile<-"http://canisius.edu/~yany/data/ff3monthly.csv" 
ff3monthly<-read.csv(inFile,skip=3)
saveRDS(ff3monthly,file="c:/temp/ff3monthly.rds") 

The corresponding function to load the dataset is called readRDS(). Another important property when using the rds dataset is that we can assign another more convenient name, shown in the code that follows. In this case, we call it abc instead of ff3monthly:

>abc<-readRDS("c:/temp/ff3monthly.rds") 
>head(abc,3) 
        DATE MKT_RF     SMB     HML     RF 
1 1926-07-01 0.0296 -0.0230 -0.0287 0.0022 
2 1926-08-01 0.0264 -0.0140  0.0419 0.0025 
3 1926-09-01 0.0036 -0.0132  0.0001 0.0023 
>head(ff3monthly,3) 
        DATE MKT_RF     SMB     HML     RF 
1 1926-07-01 0.0296 -0.0230 -0.0287 0.0022 
2 1926-08-01 0.0264 -0.0140  0.0419 0.0025 
3 1926-09-01 0.0036 -0.0132  0.0001 0.0023