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

Sum of Columns

The colSums function is an optimised function for calculating the sums of columns in an array. The example here also illustrates how to format output to make large numbers much easier to read, by including commas. The times can be obtained using the system.time function.

> A <- matrix(runif(10000000, 0, 100), ncol=10)
> colSums(A)			   # Optimised: 0.1 seconds
> rep(1, nrow(A)) %*% A		   # Slower: 0.3 seconds
> apply(A, 2, sum)		   # Slowest: 0.7 seconds
> format(colSums(A), big.mark=",")

 [1] "49,966,626" "49,968,075" "49,977,689" "50,010,843" "50,038,271"
 [6] "49,936,119" "50,027,467" "49,985,741" "50,065,027" "49,985,044"

> colSums(A)

 [1] 49966626 49968075 49977689 50010843 50038271 49936119 50027467 49985741
 [9] 50065027 49985044

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