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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-visualization-in-r-with-live-project/
课程评论：没有评论
课程名称：R语言数据可视化 概述：欢迎参加Uplatz提供的R语言数据可视化课程。数据可视化是量化分析的一种方法，使我们能够探索数据并传达我们的发现。在数据科学中，分析数据仅是成功的一半；有效地传达数据和结果以分享知识、促进决策同样至关重要。数据可视化是一种强大的工具，可以帮助各类统计知识背景的人理解复杂的模式和发现。R编程语言不仅专为统计计算而设计，还特别注重图形呈现。除了基本的绘图功能，R还通过附加代码包提供了更强大的功能。ggplot2包由Hadley Wickham开发，是tidyverse的一部分，它被认为是最佳的绘图工具之一，结合了高度的自定义和清晰、美观的图形，且对程序员的要求较低。因此，ggplot2是一个旨在使数据管理、分析和可视化更易于使用的包集合，支持不同类型的数据的绘图。此课程将深入探讨如何使用R创建基于定量数据的有效图形，帮助您更好地理解数据并增强您的研究成果。 课程目标： - 理解数据可视化的目的及其有效沟通方式。 - 快速介绍基础R图形。 - 了解R中不同的图表类型。 - 探索R中的数据可视化工具。 - 选择适合您数据的图表类型。 - 添加图表细节，掌握ggplot2包。 - 实施ggplot2中的“图形语法”，如尺度、坐标系统、位置调整和分面等。 - 创建复杂可视化并探索变量之间的关系。 - 从零开始设计和实施可视化。 - 理解设计原则的科学。 - 区分用于发表的解释性图形与用于数据探索的图形。 - 掌握高级图形自定义技术。 课程大纲： 1. R中的数据可视化 - 数据可视化的定义。 - 选择正确的图表类型。 - 数据可视化的重要性及其好处。 - 数据可视化应用：散点图、直方图、条形图、堆叠条形图、箱形图、热图、折线图、密度图和饼图等R程序。 2. ggplot2包的数据可视化 - 了解ggplot2。 - 使用ggplot2绘图。 - 逐步构建图表。 - 单变量分布与条形图，ggplot2的注释功能，坐标轴操作，密度图。 3. R中的更多数据可视化工具 - 文本挖掘和词云。 - 雷达图、华夫图、面积图、相关图。 4. 数据可视化项目 - “可视化Covid-19”综合项目——从零开始创建。 通过学习R语言的数据可视化，您不仅可以获得技术技能，还有能力通过视觉吸引力和有意义的图表和仪表板有效沟通见解。主要收益包括： - 强大的可视化库：R提供丰富的库如ggplot2、plotly和Shiny，轻松创建复杂和互动的可视化。 - 高度可定制性：R允许对图表的每个细节进行微调。 - 数据分析的整合：R使数据操作和可视化无缝集成。 - 支持复杂数据：R在处理大型数据集和复杂统计分析方面表现出色。 - 可重现性：利用R创建的可视化，可以通过脚本轻松记录和再现。 - 互动性：R的互动功能使用户动态探索数据可视化。 - 广泛行业应用：R在金融、医疗和学术等领域被广泛采用，掌握它可以开启各种分析和数据驱动的角色。 总之，这门课程将帮助您掌握R语言的数据可视化技巧，为您的职业发展打开新的大门。
A warm welcome to the Data Visualization with R course by Uplatz.Data Visualization refers to quantitative analysis that allows us to explore data and communicate our findings. In data science, analyzing your data is only half the battle - communicating your data and results to share knowledge and facilitate decision making is also essential. Data visualization is a powerful tool that can allow people across all ranges of statistical know-how to understand complex patterns and findings.Not only the R programming language was specifically designed for statistical computing, it was also developed with a focus on graphics. As well as the standard plotting functions available in base R, additional functionality is also available through add-on packages of code. The ggplot2 package was developed by Hadley Wickham as part of the tidyverse (a collection of packages designed with data science in mind) and is considered one of the best tools for plotting graphs. It combines high levels of customization with clean and visually pleasing graphics, often with minimal effort put in on the part of the programmer. Thus ggplot2 is basically a set of packages that aim to make data management, analysis and visualization more user-friendly. It contains a wide range of options to customize plots and can be used for all types of data.Uplatz provides this in-depth training on Data Visualization using R. This Data Visualization with R course helps you to effectively create figures based on your quantitative data. If you want to understand your data better and add impact to your publications, Data Visualization in R is the right course for you. Understand the art of visual communication and practical implementation of data visualization. This is done using the R statistical programming environment with your own data.Course ObjectivesThe purpose of data visualization and how to use it to communicate effectivelyA quick introduction to base R graphicsDifferent plot types in RData Visualization tools in RWhich is the most appropriate plot type for your dataAdding details to plotsggplot2 packageImplementing the "Grammar of Graphics" in ggplot2, such as scales, coordinate systems, position adjustments, and facetingCreating complex visualizations and investigating the correlations between variablesDesigning and implementing a visualization from scratchHow much is too muchThe science of perception and apply design principlesTo distinguish between explanatory graphics for publication and for data explorationAdvanced plot customization and beyondData Visualization with R - Course Curriculum1. Data Visualization in RWhat is data visualization?Selecting right chart typeImportance of data visualization & its benefitsApplications of DATA VisualizationR Programs for Scatterplot, Histogram, Bar & Stacked bar chart, boxplot, heatmap, line chart, density plot, pie chart2. Data Visualization with ggplot2 packageWhat is ggplot2Plotting with ggplot2Building your plots iterativelyUnivariate distributions & bar plotAnnotation with ggplot2Axis manipulationDensity plot3. More Data Visualization tools in RText mining and word cloudRadar chartWaffle chartArea ChartCorrelogram4. Project on Data Visualization"Visualizing Covid-19" comprehensive project - create from scratchBy learning data visualization with R, you gain not only technical skills but also the ability to communicate insights effectively through visually appealing and meaningful charts and dashboards. Some of the key benefits include:Powerful Visualization Libraries: R provides a rich set of libraries like ggplot2, plotly, and Shiny that allow you to create complex and interactive visualizations with ease.Customizability: R allows for highly customizable and detailed visualizations. You can fine-tune every aspect of your charts, from colors and shapes to annotations and themes.Integration with Data Analysis: As a data analysis language, R seamlessly integrates data manipulation and visualization. This reduces the need for switching between different tools or languages, streamlining the workflow.Support for Complex Data: R excels at handling large datasets, complex statistical analysis, and visualizing results in meaningful ways, making it ideal for scientific research, finance, and business analytics.Reproducibility: Visualizations created with R can be easily documented and reproduced through scripts, enabling better transparency and repeatability in analysis.Interactivity: R's interactive features (via packages like plotly and Shiny) allow users to explore data visualizations dynamically, which is valuable for exploring trends and gaining deeper insights.Wide Industry Adoption: R is widely used in fields like finance, healthcare, academia, and research. Mastering it can open doors to various analytical and data-driven roles.Collaboration: R integrates well with other tools like Tableau, Power BI, and databases, making it easier to collaborate in teams working on larger projects.
