BackgroundThe visual sequence logo has been a hot area in the development of bioinformatics tools. ggseqlogo written in R language has been the most popular API since it was published. With the popularity of artificia...
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BackgroundThe visual sequence logo has been a hot area in the development of bioinformatics tools. ggseqlogo written in R language has been the most popular API since it was published. With the popularity of artificial intelligence and deep learning, Python is currently the most popular programming language. The programming language used by bioinformaticians began to shift to Python. Providing APIs in Python that are similar to those in R can reduce the learning cost of relearning a programming language. And compared to ggplot2 in R, drawing framework is not as easy to use in Python. The appearance of plotnine (ggplot2 in Python version) makes it possible to unify the programming methods of bioinformatics visualization tools between R and ***, we introduce plotnineSeqSuite, a new plotnine-based Python package provides a ggseqlogo-like API for programmatic drawing of sequence logos, sequence alignment diagrams and sequence histograms. To be more precise, it supports custom letters, color themes, and fonts. Moreover, the class for drawing layers is based on object-oriented design so that users can easily encapsulate and extend *** is the first ggplot2-style package to implement visualization of sequence -related graphs in Python. It enhances the uniformity of programmatic plotting between R and Python. Compared with tools appeared already, the categories supported by plotnineSeqSuite are much more complete. The source code of plotnineSeqSuite can be obtained on GitHub (https://***/caotianze/plotnineseqsuite) and PyPI (https://***/project/plotnineseqsuite), and the documentation homepage is freely available on GitHub at (https://***/plotnineseqsuite/).
Being able to visualize data in consistent high-quality ways is a useful skill for HCI researchers and practitioners. In this course, attendees will learn how to produce high quality plots and visualizations using the...
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ISBN:
(纸本)9781450359719
Being able to visualize data in consistent high-quality ways is a useful skill for HCI researchers and practitioners. In this course, attendees will learn how to produce high quality plots and visualizations using the ggplot2 library for the R statistical computing language. There are no prerequisites and attendees will leave with scripts to get them started as well as foundational knowledge of free open-source tools that they can build on to produce complex, even interactive, visualizations.
Being able to visualise data in consistent, high-quality ways is a useful skill for HCI researchers and practitioners. In this course, attendees will learn how to produce high quality plots and visualisations using th...
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ISBN:
(纸本)9781450391566
Being able to visualise data in consistent, high-quality ways is a useful skill for HCI researchers and practitioners. In this course, attendees will learn how to produce high quality plots and visualisations using the ggplot2 library for the R statistical computing language. There are no prerequisites and attendees will leave with scripts to get them started as well as foundational knowledge of free open-source tools that they can build on to produce complex, even interactive, visualisations. Course information materials can be found at https://***/chi22-course.
In this book review, we offer chapter-by-chapter review and general comments of Hadley Wickham's (2016) ggplot2: Elegant Graphics for Data Analysis. Two examples of 2-way interaction plots were included to highlig...
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In this book review, we offer chapter-by-chapter review and general comments of Hadley Wickham's (2016) ggplot2: Elegant Graphics for Data Analysis. Two examples of 2-way interaction plots were included to highlight the flexibility and power of the ggplot2 package in R.
In psychology and human neuroscience, the practice of creating multiple subplots and combining them into a composite plot has become common because the nature of research has become more multifaceted and sophisticated...
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In psychology and human neuroscience, the practice of creating multiple subplots and combining them into a composite plot has become common because the nature of research has become more multifaceted and sophisticated. In the last decade, the number of methods and tools for data visualization has surged. For example, R, a programming language, has become widely used in part because of ggplot2, a free, open-source, and intuitive plotting library. However, despite its strength and ubiquity, it has some built-in restrictions that are most noticeable when one creates a composite plot, which currently involves a complex and repetitive process with steps that go against the principles of open science out of necessity. To address this issue, I introduce smplot2, an open-source R package that integrates ggplot2's declarative syntax and a programmatic approach to plotting. The package aims to enable users to create customizable composite plots by linearizing the process of complex visualization.
Exponential growth rate in the data generation in diverse fields revolutionized the way the analytics tools and machine learning algorithms applied in the Big Data. Considering the pace at which data is generated and ...
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ISBN:
(纸本)9781538681138
Exponential growth rate in the data generation in diverse fields revolutionized the way the analytics tools and machine learning algorithms applied in the Big Data. Considering the pace at which data is generated and the variety of data, identifying the right data at the right time and the relationship is crucial to make decisions. Identifying the data type and the relationship between the parameters is challenging as the size of the real time data is massive and dynamic in nature. Data Visualization as part of Big Data Exploratory analysis helps to identify the relationship and to understand the characteristics of big data in an effective way. In other words, data visualization is the graphical representation of data, it links the data availability and data analysis, organizes and presents important findings from the data. As plenty of visualization packages and tools are available, choosing the right tool is very important. gglot2 is one among the statistical graphics tool for data visualization as the working model is entirely based on grammar of graphics. The unique feature of ggplot is the layered approach, as the each component is highly interdependent on the other and so it helps in the step by step analysis of the data. With this feature available ggplot2 was used in the Real time Public Distribution System data to identify the relationship between various parameters and to understand the behavior pattern.
Information about climate changes is required at global, regional and basin levels for a variety of purposes, including the study of impact of the greenhouse gases. The analyses mentioned in this research relate to th...
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ISBN:
(纸本)9781509032570
Information about climate changes is required at global, regional and basin levels for a variety of purposes, including the study of impact of the greenhouse gases. The analyses mentioned in this research relate to the observation of trends in the temperatures of the Indian states. The research begins with the exposition of the ongoing analysis methodologies prevalent in exploratory analysis and prediction modeling on temperature data. It further develops into the proposed work, where the analysis of means of the average temperatures observed across the Indian states from 1800-2013 is summarized, which in turn is found to reveal confounding results. The proposed work concludes with further focused analysis of geographically similar states, namely the states lying on the Indo-Gangetic plains, which reveal encouraging results, thereby showing an occurrence of a trend. The research concludes with the propounding of the future scope, which includes modeling for predicting the average temperatures which can be attained over the next few decades, which in turn would be significant for the observation of the corollaries of global warming in India.
As we, all know data is ubiquitous;it is found everywhere, as it is not wrong to say that data is new oil. As data is increasing exponentially so is the necessity to test this data. In this paper, we discussed how SVM...
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ISBN:
(纸本)9781665414517
As we, all know data is ubiquitous;it is found everywhere, as it is not wrong to say that data is new oil. As data is increasing exponentially so is the necessity to test this data. In this paper, we discussed how SVM works and have used diabetes database and implemented SVM in R on this dataset to predict if the person has diabetes or no. SVM (support vector machine) uses support vectors to create a convenient plain, which can distinguish between two classes with high accuracy. Diabetes have many symptoms and is caused by many things in this paper we have selected all possible variables that can attribute to a person having diabetes and split the database into two datasets to train and test the algorithm. SVM has two main parameters, which can he changed to improve accuracy, C parameter and k-fold. We tried different values for both to find the optimum values for my dataset, which can improve algorithm accuracy. We were able to implement the algorithm for my dataset with the help of R language, which is high level functional language having plethora of libraries which can be used to carry out SVM algorithm and to create mathematical graphs,such as ggplot2, plot, lattice.
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