![]() ![]() approach a graph from a visual perspective, thinking about how each component of the data is represented on the final plot.create custom themes that capture in-house or journal style requirements and that can easily be applied to multiple plots.save any ggplot2 plot (or part thereof) for later modification or reuse.add customizable smoothers that use powerful modeling capabilities of R, such as loess, linear models, generalized additive models, and robust regression.superimpose multiple layers (points, lines, maps, tiles, box plots) from different data sources with automatically adjusted common scales.produce handsome, publication-quality plots with automatic legends created from the plot specification.ggplot2 is a data visualization package for R that helps users create data graphics, including those that are multi-layered, with ease. This new edition to the classic book by ggplot2 creator Hadley Wickham highlights compatibility with knitr and RStudio. These results provide new insight regarding the way in which individual music preferences are built. We propose that this pattern is consistent with a preferential attachment model, according to which the probability of listening to a given artist at a given time is proportional to the frequency to which the artist was listened to in the past. The scaling parameter of the distribution varies considerably among users, Providing a metric that characterizes the way in which different people explore music. With the use of this data set, we are able to confirm that the number of songs reproduced per artist follows a truncated power-law distribution. We obtained the history of songs listened to by 50 different users of the online database system Last.fm, spanning on average five years of activity. In this study, we aimed to analyze music listening behaviors using personal records of music listening activity. However, previous efforts seldom focused on people’s deliberate choices of music in everyday life. Music preferences have long been studied owing to their importance in the fields of psychology and sociology. The methodology suggested in this article should provide students of ‘hit song science’ and the likes with a more rigorous approach to appraising commercial success, as well as a comprehensive background as to its origin and relevance to popular music studies. However, a modest ‘star power’ effect may have represented a small but vital edge for the oligopoly of multinational recording companies. Our results indicate that, while records that have sold well will keep on selling, the same might not be true for recording artists. ![]() ![]() We make the case for the pivotal role of the market share approach in the music industry and demonstrate its efficacy as a ‘success measure’ methodology by providing a descriptive summary with regard to ‘connotations’ of cumulative advantage based on 50 years of Billboard Hot 100® history. Starting from the theories of Rosen and Adler, this article concentrates mainly on the phenomenon referred to as cumulative advantage, as one of the leading candidate mechanisms to explain the formation of the ‘power law-like’ distributions found in, e.g. The existence of highly skewed success distributions in the music industry has been repeatedly demonstrated by scholars, but there still is no agreement about how these shapes relate to concepts like ‘talent’, ‘reputation’ and ‘quality’. The common power law exponent of two is seen to emerge as a consequence of the tendency for musical activity to be spread evenly across the log-success bands. This implies that musical success is a multiplicative quality, and suggests that musical markets operate to strike a balance between familiarity (socio-cultural importance) and novelty (individual importance). These models can be seen as manifestations of a more fundamental process resulting from the law of maximum entropy, subject to a constraint on the average value of the logarithm of the success measure. An examination of these models' transience characteristics suggests parallels with some historical music examples, giving clues to the ways that success and obscurity might emerge in practice and the extent to which success might be influenced by inherent musical quality. It presents several simple models which can produce power law distributions. It starts from the observation that the patterns of success, across many historical music datasets, follow a similar mathematical relationship known as a power law, often with an exponent approximately equal to two. This paper investigates the processes leading to musical fame or obscurity, whether for composers, performers, or works themselves. ![]()
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