In summary, data analysis, like experimentation, must be considered as an open-ended, highly interactive, iterative process, whose actual steps are selected segments of a stubbily branching, tree-like pattern of possible actions.
Data analysis and statistics: an expository overview J. W. Tukey and M. B. Wilk (1966)
…exploratory data analysis is an attitude, a state of flexibility, a willingness to look for those things that we believe are not there, as well as for those we believe might be there. Except for its emphasis on graphs, its tools are secondary to its purpose.
J. W. Tukey in a comment to E. Parzen (1979)
Many biologists are first exposed to the R language by following a cookbook-type approach to conduct a statistical analysis like a t-test or an analysis of variance (ANOVA). Although R excels at these and more complicated statistical tasks, R’s real power is as a data programming language you can use to explore and understand data in an open-ended, highly interactive, iterative way. Learning R as a data programming language will give you the freedom to experiment and problem solve during data analysis—exactly what we need as bioinformaticians. In particular, we’ll focus on the subset of the R language that allows you to conduct exploratory data analysis (EDA). Note, however, that EDA is only one aspect of the R language—R also includes state-of-the-art statistical and machine learning methods.
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