The The R Primer logo Primer
Detailed list of content of The R Primer.
  1. Importing data
    1. Read data from a text file
    2. Read data from a simple XML file
    3. Read data from an XML file
    4. Read data from an SQL database using ODBC
    5. Reading spreadsheets
    6. Read data from a CSV file
    7. Read data from an Excel spreadsheet
    8. Read data from an Excel spreadsheet under Windows
    9. Read data from a LibreOffice or+\thinmuskip {.1667em} OpenOffice Calc spreadsheet
    10. Read data from the clipboard
    11. Importing data from other statistical software programs
    12. Import a SAS dataset
    13. Import an SPSS dataset
    14. Import a Stata dataset
    15. Import a Systat dataset
    16. Exporting data
    17. Export data to a text file
    18. Export a data frame to a CSV file
    19. Export a data frame to a spreadsheet
    20. Export a data frame to an Excel spreadsheet under Windows
    21. Export a data frame to a SAS dataset
    22. Export a data frame to an SPSS dataset
    23. Export a data frame to a Stata dataset
    24. Export a data frame to XML
  2. Manipulating data
    1. Using mathematical functions and operations
    2. Working with common functions
    3. Working with dates
    4. Working with character vectors
    5. Find the value of x corresponding to the maximum or minimum of y
    6. Check if elements in one object are present in another object
    7. Transpose a matrix (or data frame)
    8. Impute values using last observation carried forward
    9. Convert comma as decimal mark to period
    10. Working with data frames
    11. Select a subset of a dataset
    12. Select the complete cases of a dataset
    13. Delete a variable from a data frame
    14. Join datasets
    15. Merge datasets
    16. Stack the columns of a data frame together
    17. Reshape a data frame from wide to long format or vice versa
    18. Create a table of counts
    19. Convert a table of counts to a data frame
    20. Convert a data frame to a vector
    21. Factors
    22. Convert a factor to numeric
    23. Add a new level to an existing factor
    24. Combine the levels of a factor
    25. Remove unused levels of a factor
    26. Cut a numeric vector into a factor
    27. Transforming variables
    28. Sort data
    29. Transform a variable
    30. Apply a function multiple times to parts of a data frame or array
    31. Use a Box-Cox transformation to make non-normally distributed data approximately normal
    32. Calculate the area under a curve
  3. Statistical analyses
    1. Descriptive statistics
    2. Create descriptive tables
    3. Linear models
    4. Fit a linear regression model
    5. Fit a multiple linear regression model
    6. Fit a polynomial regression model
    7. Fit a one-way analysis of variance
    8. Fit a two-way analysis of variance
    9. Fit a linear normal model
    10. Generalized linear models
    11. Fit a logistic regression model
    12. Fit a multinomial logistic regression model
    13. Fit a Poisson regression model
    14. Fit an ordinal logistic regression model
    15. Methods for analysis of repeated measurements
    16. Fit a linear mixed-effects model
    17. Fit a linear mixed-effects model with serial correlation
    18. Fit a generalized linear mixed model
    19. Fit a generalized estimating equation model
    20. Decompose a time series into a trend, seasonal, and residual components
    21. Analyze time series using an ARMA model
    22. Specific methods
    23. Compare populations using t test
    24. Fit a nonlinear model
    25. Fit a Tobit regression model
    26. Model validation
    27. Test for normality of a single sample
    28. Test for variance homogeneity across groups
    29. Validate a linear or generalized linear model
    30. Contingency tables
    31. Analysis of two-dimensional contingency tables
    32. Analyze contingency tables using log-linear models
    33. Agreement
    34. Create a Bland-Altman plot of agreement to compare two quantitative methods
    35. Determine agreement among several methods of a quantitative measurement
    36. Calculate Cohen's kappa
    37. Multivariate methods
    38. Fit a multivariate regression model
    39. Cluster observations
    40. Use principal component analysis to reduce data dimensionality
    41. Fit a principal component regression model
    42. Classify observations using linear discriminant analysis
    43. Use partial least squares regression for prediction
    44. Resampling statistics and bootstrapping
    45. Non-parametric bootstrap analysis
    46. Use cross-validation to estimate the performance of a model or algorithm
    47. Calculate power or sample size for simple designs
    48. Robust statistics
    49. Correct p-values for multiple testing
    50. Non-parametric methods
    51. Use Wilcoxon's signed rank test to test a sample median
    52. Use Mann-Whitney's test to compare two groups
    53. Compare groups using Kruskal-Wallis' test
    54. Compare groups using Friedman's test for a two-way block design
    55. Survival analysis
    56. Fit a Kaplan-Meier survival curve to event history data
    57. Fit a Cox regression model (proportional hazards model)
    58. Fit a Cox regression model (proportional hazards model) with time-varying covariates
  4. Graphics
    1. Including Greek letters and equations in graphs
    2. Set colors in R graphics
    3. Set color palettes in R graphics
    4. High-level plots
    5. Create a scatter plot
    6. Create a histogram
    7. Make a boxplot
    8. Create a bar plot
    9. Create a bar plot with error bars
    10. Create a plot with estimates and confidence intervals
    11. Create a pyramid plot
    12. Plot multiple series
    13. Make a 2D surface plot
    14. Make a 3D surface plot
    15. Plot a 3D scatter plot
    16. Create a heat map plot
    17. Plot a correlation matrix
    18. Make a quantile-quantile plot
    19. Graphical model validation for linear models
    20. More advanced graphics
    21. Create a broken axis to indicate discontinuity
    22. Create a plot with two y-axes
    23. Rotate axis labels
    24. Multiple plots
    25. Add a legend to a plot
    26. Add a table to a plot
    27. Label points in a scatter plot
    28. Identify points in a scatter plot
    29. Visualize points, shapes, and surfaces in 3D and interact with them in real-time
    30. Working with graphics
    31. Exporting graphics
    32. Produce graphics output in LaTeX-ready format
    33. Embed fonts in postscript or pdf graphics
  5. R
    1. Getting information
    2. Getting help
    3. Finding R source code for a function
    4. R packages
    5. Installing R packages
    6. Update installed R packages
    7. List the installed packages
    8. List the content of a package
    9. List or view vignettes
    10. Install a package from BioConductor
    11. Permanently change the default directory where R installs packages
    12. Automatically load a package when R starts
    13. The R workspace
    14. Managing the workspace
    15. Changing the current working directory
    16. Saving and loading workspaces
    17. Saving and loading histories
    18. Interact with the file system
    19. Locate and choose files interactively
    20. Interact with the operating system