DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining Data Mining Data Mining with Rattle Introduction Data Transform Explore A Model Building Framework Unsupervised Modelling Two Class Models Multi Class Models Regression Models Text Mining Evaluation and Deployment Moving into R Troubleshooting R for the Data Miner R Data Graphics in R Understanding Data Preparing Data Building Models Evaluating Models Algorithms Apriori Bagging Bayes Classifier Boosting Cluster Analysis Conditional Trees Hierarchical Clustering K-Means K-Nearest Neighbours Linear Models Logistic Regression Neural Networks Support Vector Machines Text Mining Open Products AlphaMiner Borgelt Data Mining Suite KNime R Rattle Weka Closed Products C4.5 Clementine Equbits Foresight GhostMiner InductionEngine ODM Enterprise Miner Statistica Data Miner TreeNet Virtual Predict Appendicies Glossary Bibliography Index

Clipboard Data

Suppose you are reviewing a small sample of data on the screen in any application (e.g., browsing a web site with some sample data). You want to load the data into R. This can be easily accomplished by selecting or highlighting the data with the mouse and telling R to read from the clipboard.

As an example, visit one of the UCI Machine Learning Repository datasets, such as: http://www.ics.uci.edu/~mlearn/databases/autos/imports-85.data. Highlight the first few rows of the data and then run the following read.table function with the file function identifying the clipboard to be read from:

> autos <- read.csv(file("clipboard"), header=FALSE)
> autos

  V1 V2          V3  V4  V5  V6          V7  V8    V9  V10   V11  V12  V13  V14
1  3  ? alfa-romero gas std two convertible rwd front 88.6 168.8 64.1 48.8 2548
2  3  ? alfa-romero gas std two convertible rwd front 88.6 168.8 64.1 48.8 2548
3  1  ? alfa-romero gas std two   hatchback rwd front 94.5 171.2 65.5 52.4 2823

   V15  V16 V17  V18  V19  V20 V21 V22  V23 V24 V25   V26
1 dohc four 130 mpfi 3.47 2.68   9 111 5000  21  27 13495
2 dohc four 130 mpfi 3.47 2.68   9 111 5000  21  27 16500
3 ohcv  six 152 mpfi 2.68 3.47   9 154 5000  19  26 16500

You can also use scan, for example, to read data from the clipboard into a vector or list:

> x <- scan("clipboard", what="")
Read 7 items
> x
[1] "Age"   "Gender"  "Salary"  "Home" "Vehicle"  "Address"  "Married"

To try this out yourself, select the list of strings and run the scan function.

You can also write to the clipboard:

> write.table(ds, "clipboard", sep="\t", row.names=FALSE)

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