New PDF release: Mastering Scientific Computing with R

By Paul Gerrard, Radia M. Johnson

Employ specialist quantitative how to solution clinical questions with a strong open resource information research environment

About This Book

  • Perform publication-quality technology utilizing R
  • Use a few of R's strongest and least identified positive aspects to resolve complicated clinical computing problems
  • Learn tips to create visible illustrations of clinical results

Who This e-book Is For

If you need to easy methods to quantitatively resolution clinical questions for useful reasons utilizing the strong R language and the open resource R software surroundings, this ebook is perfect for you. it's perfect for scientists who comprehend medical options, be aware of a bit R, and wish so as to commence using R so one can resolution empirical medical questions. a few R publicity is beneficial, yet now not compulsory.

In Detail

With this e-book, you are going to study not only approximately R, yet tips on how to use R to reply to conceptual, clinical, and experimental questions.

Beginning with an summary of primary R strategies, you will find out how R can be utilized to accomplish the main generally wanted clinical facts research projects: checking out for statistically major changes among teams and version relationships in facts. you are going to delve into linear algebra and matrix operations with an emphasis now not at the R syntax, yet on how those operations can be utilized to handle universal computational or analytical wishes. This e-book additionally covers the appliance of matrix operations for the aim of discovering constitution in high-dimensional facts utilizing the crucial part, exploratory issue, and confirmatory issue research as well as structural equation modeling. additionally, you will grasp tools for simulation and find out about a complicated analytical method.

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Extra resources for Mastering Scientific Computing with R

Sample text

Basic plots and the ggplot2 package This section will review how to make basic plots using the built-in R functions and the ggplot2 package to plot graphics. Basic plots in R include histograms and scatterplots. To plot a histogram, we use the hist() function: > x <- c(5, 7, 12, 15, 35, 9, 5, 17, 24, 27, 16, 32) > hist(x) [ 33 ] Programming with R The output is shown in the following plot: You can plot mathematical formulas with the plot() function as follows: > x <- seq(2, 25, by=1) > y <- x^2 +3 > plot(x, y) The output is shown in the following plot: [ 34 ] Chapter 1 You can graph a univariate mathematical function on an interval using the curve() function with the from and to arguments to set the left and right endpoints, respectively.

This property comes in very handy when writing for loops as we will see later in this chapter in the Flow control section. Let's take a look at the length function: > length(coordinates) [1] 32 The length() and names() functions have attributes with higher-dimensional generalizations. The length() function generalizes to nrow() and ncol() for matrices, and dim() for arrays. Similarly, names() can be generalized to rownames(), colnames() for matrices, and dimnames() for multidimensional arrays. Note that dimnames() takes a list of character vectors corresponding to the names of each dimension of the array.

Na() functions. na() function will return TRUE if NA is present. Because we want to return TRUE when there is no NA present instead of when an NA is present, we use the ! na()) command.

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