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ggplot2 is an open-source data visualization package for the statistical programming language R.Created by Hadley Wickham in 2005, ggplot2 is an implementation of Leland Wilkinson's Grammar of Graphics—a general scheme for data visualization which breaks up graphs into semantic components such as scales and layers. ggplot2 can serve as a replacement for the base graphics in R and contains a ...
Heiberger R. and Neuwirth E.: R Through Excel, Springer Verlag 2009. Neuwirth, E.: R meets the Workplace - Embedding R into Excel and making it more accessible. Paper presented at the UseR 2008, Dortmund. Narasimhan, B.: Disseminating Statistical Methodology and Results via R and Excel: Two Examples. Paper presented at the Interface 2007 ...
Figure 2. Box-plot with whiskers from minimum to maximum Figure 3. Same box-plot with whiskers drawn within the 1.5 IQR value. A boxplot is a standardized way of displaying the dataset based on the five-number summary: the minimum, the maximum, the sample median, and the first and third quartiles.
Box plot of five West Coast cities rainfall data. The box plot above is not entirely clear. It is hard to distinguish the cities that are somewhat similar (average or mean is not statistically different) vs. the ones that are dissimilar (average or mean is statistically different). Now, let's view the same boxplot using the CLD methodology.
In statistical graphics, the functional boxplot is an informative exploratory tool that has been proposed for visualizing functional data. [1] [2] Analogous to the classical boxplot, the descriptive statistics of a functional boxplot are: the envelope of the 50% central region, the median curve and the maximum non-outlying envelope.
Boxplot (with an interquartile range) and a probability density function (pdf) of a Normal N(0,σ 2) Population. In descriptive statistics, the interquartile range (IQR) is a measure of statistical dispersion, which is the spread of the data. [1] The IQR may also be called the midspread, middle 50%, fourth spread, or H‑spread.
Therefore, the group of patients with large stones using treatment A (group 3) does worse than the group with small stones, even if the latter used the inferior treatment B (group 2). Based on these effects, the paradoxical result is seen to arise because the effect of the size of the stones overwhelms the benefits of the better treatment (A).
The users may have feedback, which results in additional analysis. As such, much of the analytical cycle is iterative. [13] When determining how to communicate the results, the analyst may consider implementing a variety of data visualization techniques to help communicate the message more clearly and efficiently to the audience. [45]