Download PDF by Luis Torgo: Data mining with R : learning with case studies

By Luis Torgo

"The flexible services and big set of add-on applications make R an outstanding substitute to many latest and infrequently dear facts mining instruments. Exploring this sector from the viewpoint of a practitioner, information mining with R: studying with case reports makes use of functional examples to demonstrate the facility of R and information mining. Assuming no previous wisdom of R or information mining/statistical thoughts, the publication covers a

"This hands-on ebook makes use of useful examples to demonstrate the ability of R and information mining. Assuming no past wisdom of R or info mining/statistical ideas, it covers a various set of difficulties that pose various demanding situations by way of dimension, kind of info, pursuits of study, and analytical instruments. the most information mining tactics and methods are offered via unique, real-world case reviews. With those case experiences, the writer provides all priceless steps, code, and information. Mirroring the home made technique of the textual content, the assisting web site presents info units and R code"-- Read more...

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For these observations, R is unable to know the result of the comparison and thus the NAs. na(algae$NH4) & algae$NH4 > 19000,]. na() produces a vector of Boolean values (true or false). An element of this vector is true when NH4 is NA. This vector has as many elements as there are rows in the data frame algae. ’ is the logical negation operator. In summary, this alternative call would give us the rows of the data frame that have known values in NH4 and are greater than 19,000. Let us now explore a few examples of another type of data inspection.

The following would “organize” these ten numbers as a matrix: > m <- c(45, 23, 66, 77, 33, 44, 56, 12, 78, 23) > m [1] 45 23 66 77 33 44 56 12 78 23 > dim(m) <- c(2, 5) > m [1,] [2,] [,1] [,2] [,3] [,4] [,5] 45 66 33 56 78 23 77 44 12 23 Notice how the numbers were “spread” through a matrix with two rows and five columns (the dimension we have assigned to m using the dim() function). Actually, you could simply create the matrix using the simpler instruction: > m <- matrix(c(45, 23, 66, 77, 33, 44, 56, 12, 78, 23), 2, + 5) You may have noticed that the vector of numbers was spread in the matrix by columns; that is, first fill in the first column, then the second, and so on.

3 shows us that the variable oPO4 has a distribution of the observed values clearly concentrated on low values, thus with a positive skew. In most of the water samples, the value of oPO4 is low, but there are several observations with high values, and even with extremely high values. Sometimes when we encounter outliers, we are interested in inspecting the observations that have these “strange” values. We will show two ways of doing this. First, let us do it graphically. If we plot the values of variable NH4, we notice a very large value.

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