| Description |
1 online resource (xvi, 250 pages) : illustrations |
| Series |
The expert's voice in R |
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Expert's voice in R
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| Contents |
At a Glance; Introduction; Chapter 1: R Fundamentals; Downloading and Installing R; Getting Orientated; The R Console and Command Prompt; Functions; Objects; Simple Objects; Vectors; Data Frames; The Data Editor; Workspaces; Error Messages; Script Files; Summary; Chapter 2: Working with Data Files; Entering Data Directly; Importing Plain Text Files; CSV and Tab-Delimited Files; DIF Files; Other Plain Text Files; Importing Excel Files; Importing Files from Other Software; Using Relative File Paths; Exporting Datasets; Summary; Chapter 3: Preparing and Manipulating Your Data; Variables |
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Rearranging and Removing VariablesRenaming Variables; Variable Classes; Calculating New Numeric Variables; Dividing a Continuous Variable into Categories; Working with Factor Variables; Manipulating Character Variables; Concatenating Character Strings; Extracting a Substring; Searching a Character Variable; Working with Dates and Times; Adding and Removing Observations; Adding New Observations; Removing Specific Observations; Removing Duplicate Observations; Selecting a Subset of the Data; Selecting a Subset According to Selection Criteria; Selecting a Random Sample from a Dataset |
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Sorting a DatasetSummary; Chapter 4: Combining and Restructuring Datasets; Appending Rows; Appending Columns; Merging Datasets by Common Variables; Stacking and Unstacking a Dataset; Stacking Data; Unstacking Data; Reshaping a Dataset; Summary; Chapter 5: Summary Statistics for Continuous Variables; Univariate Statistics; Statistics by Group; Measures of Association; Covariance; Pearson's Correlation Coefficient; Spearman's Rank Correlation Coefficient; Hypothesis Test of Correlation; Comparing a Sample with a Specified Distribution; Shapiro-Wilk Test; Kolmogorov-Smirnov Test |
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Confidence Intervals and Prediction IntervalsSummary; Chapter 6: Tabular Data; Frequency Tables; Creating Tables; Displaying Tables; Creating Tables from Count Data; Creating a Table Directly; Chi-Square Goodness-of-Fit Test; Tests of Association Between Categorical Variables; Chi-Square Test of Association; Fisher's Exact Test; Proportions Test; Summary; Chapter 7: Probability Distributions; Probability Distributions in R; Probability Density Functions and Probability Mass Functions; Finding Probabilities; Finding Quantiles; Generating Random Numbers; Summary; Chapter 8: Creating Plots |
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Simple PlotsHistograms; Normal Probability Plots; Stem-and-Leaf Plots; Bar Charts; Pie Charts; Scatter Plots; Scatterplot Matrices; Box Plots; Plotting a Function; Exporting and Saving Plots; Summary; Chapter 9: Customizing Your Plots; Titles and Labels; Axes; Colors; Plotting Symbols; Plotting Lines; Shaded Areas; Adding Items to Plots; Adding Straight Lines; Adding a Mathematical Function Curve; Adding Labels and Text; Adding a Grid; Adding Arrows; Overlaying Plots; Adding a Legend; Multiple Plots in the Plotting Area; Changing the Default Plot Settings; Summary |
| Summary |
R is a popular and growing open source statistical analysis and graphics environment as well as a programming language and platform. You'll be able to navigate the R system, enter and import data, manipulate datasets, calculate summary statistics, create statistical plots and customize their appearance, perform hypothesis tests such as the t-tests and analyses of variance, and build regression models. Examples are built around actual datasets to simulate real-world solutions, and programming basics are explained to assist those who do not have a development background. No prior knowledge of R or of programming is assumed, though you should have some experience with statistics. What you'll learn: How to apply statistical concepts using R and some R programming; How to work with data files, prepare and manipulate data, and combine and restructure datasets; How to summarize continuous and categorical variables; What is a probability distribution; How to create and customize plots; How to do hypothesis testing; How to build and use regression and linear models. -- Edited summary from book |
| Notes |
English |
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Online resource; title from PDF title page (SpringerLink, viewed July 11, 2014) |
| Subject |
Statistics -- Data processing.
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R (Computer program language)
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MATHEMATICS -- Applied.
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MATHEMATICS -- Probability & Statistics -- General.
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R (Computer program language)
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Statistics -- Data processing
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| Form |
Electronic book
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| ISBN |
9781484201398 |
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1484201396 |
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