By Mike Allerhand (auth.)
This short presents a roadmap for the R language and programming atmosphere with signposts to extra assets and documentation.
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Extra info for A Tiny Handbook of R
This lead to two kinds of functions: ‘‘high-level’’ functions that create new graphs with axes, labels and titles, and ‘‘low-level’’ functions that annotate plots, adding points, lines, and text. Create a new graph using a high-level function, and use successive low-level functions to add further graphics if necessary. plot 49 Stem-and-leaf plot Box-and-whisker plot Quantile-quantile plot Bar plot Dot plot Profile plot of group means Distribution plots: [ x¼iris$Petal:Length [ histðxÞ [ plotðdensityðxÞÞ [ stemðxÞ [ qqnormðxÞ [ boxplotðiris½3:4Þ Scatterplots: [ pairsðiris½1:4Þ [ plotðPetal:Length $Petal:Width; irisÞ # plotðy $xÞ is equivalent to plotðx; yÞ [ coplotðPetal:Length $Petal:Width j Species; irisÞ # Conditioning plot Count data in contingency tables: [ x¼HairEyeColor½; ; 2 [ addmarginsðxÞ # 2-way contingency table [ barplotðx; beside¼T; legend¼TÞ [ dotchartðxÞ [ dotchartðprop:tableðxÞÃ100Þ [ mosaicplotðxÞ Cell means12: x¼withðwarpbreaks; tapplyðbreaks; listðwool; tensionÞ; meanÞÞ # Cell means [ barplotðx; beside¼T; legend¼TÞ [ withðwarpbreaks; interaction:plotðwool; tension; breaksÞÞ [ withðwarpbreaks; interaction:plotðtension; wool; breaksÞÞ 12 See also function plotmeans in package gplots for cell means with error bars, and function intxplot in package HH for an interaction plot with error bars.
Lowest=TRUE groups right-most values within the highest interval). For example: 31 An ordered factor does not refer to the order of the level labels, but simply marks the factor as ‘‘ordered’’ so it can be handled appropriately by functions where the distinction between nominal and ordinal data is relevant. For example the contrast coding used in linear modelling functions has different defaults for unordered and ordered factors. Unordered factors get comparisons between group means. Ordered factors get trend analysis.
Cases is provided for testing for NA along data frame rows. Other functions provide arguments to accommodate NA. 4 Handling Missing Values Cases with missing values can be dropped altogether: [ dat ¼ dat½complete:casesðdatÞ; # Drop cases ðrowsÞ with any NA It is possible to impute40 values that are missing. Descriptive and modelling functions have default methods for handling NA values and generally provide arguments to override the default behaviour and specify how NA is handled. rm (‘‘NA remove’’) that can be set to TRUE to instruct the function to omit all NA from the calculation.