Inhalt: Shiny allows R data science teams to build interactive data-driven web apps without needing to learn HTML, CSS, or JavaScript. It is a powerful and versatile tool that is often used for R&D, data analysis, and even external marketing purposes. If you have a good understanding of the R language and know how to separate client-side code from server-side, you are ready to dive into this course and build a Shiny app. Martin John Hadley covers organizing single and split-file apps, managing data tables, using APIs to get data into an app, adding data controls, deploying an app, and more. Umfang: 02:50:15.00
Inhalt: Analyzing big data is great, but not if you can't share your results. In this course, Martin Hadley shows how to create interactive presentations of large data sets with R, RStudio, and Shiny, an R-based tool for producing interactive, web-ready data visualizations. Learn why these tools are important to data scientists, how to configure and install them, and how to use them to make your findings more clear and engaging. Discover the different types of presentations you can make right out of the box with R Markdown templates (built right into RStudio) and how to customize the templates with CSS. Find out how to register for RPubs to deploy RStudio presentations for sharing, and then go beyond the basics with Shiny-adding interactivity and creating embeddable dashboards without the need for HTML or JavaScript. This is an exciting course for analysts who want to increase the relevance and visibility of their work. Make sure to watch the knowledge checks at the end of each chapter to test your new skills. Umfang: 01:53:06.00
Inhalt: The pinnacle of a data science project is often the presentation of your findings. Data science is, after all, about deriving insights from data and then sharing those insights with others. This course shows how to design high-quality reports and presentations, including interactive web experiences and printable PDFs, using R Markdown and RStudio, the authoring framework designed specifically for data science. Instructor Martin John Hadley starts with an overview of the PDF and HTML reporting options. He then shows how to include R code in your documents and add visual interest with slides, charts, images, and tables. Plus, learn how to customize the styles in your document, override markdown, and insert prebuilt data visualizations with htmlwidgets. Last but not least, Martin explains how to publish your R Markdown documents for sharing and distribution. Umfang: 02:40:52.00
Inhalt: R is an incredibly powerful and widely used programming language for statistical analysis and data science. The "tidyverse" collects some of the most versatile R packages: ggplot2, dplyr, tidyr, readr, purrr, and tibble. The packages work in harmony to clean, process, model, and visualize data. This course introduces the core concepts of the tidyverse as compared to the traditional base R. It focuses on the novice user and those unfamiliar with the pipe (%>%) operator. After covering these R basics, instructor Martin Hadley progresses to importing and filtering data from Excel, CSV, and SPSS files, and summarizing and tabulating data in the tidyverse. Then learn how to identify if data is too wide or long and convert it if necessary, and conduct nonstandard evaluation. By the end of the course, you should be able to integrate the tidyverse into your R workflow and leverage a variety of new tools for importing, filtering, visualizing, and modeling research and statistical data. Umfang: 03:44:25.00
Inhalt: Using the R language almost exclusively, htmlwidgets allow you to create the same interactive maps, charts, and graphs you see on popular data journalism sites and in BI dashboards. You can connect R to popular JavaScript libraries-such as Plotly and Leaflet-with htmlwidget packages. The interactive visualizations you create can be used in R Markdown reports and presentations, and even integrated into rich, responsive Shiny applications. This course introduces you to the fundamental skills needed to add htmlwidgets to your R workflow. Start by learning to manage packages and structure data for visualizations with the tidyverse and the pipe operator. Then there is an important question: Which library should you choose? The course introduces five popular options: Leaflet, Plotly, Highcharter, visNetwork, and DataTables (DT). Instructor Martin Hadley shows how to use these libraries to create scattergeo, choropleth, and geolines maps; stacked bar charts, scatter charts, bubble charts, and heat maps; treemaps and time series charts; interactive networks and graphs; and responsive, interactive data tables. Plus, learn how to customize your visualizations with legends and tooltips, and extract click information for Shiny apps. Umfang: 05:25:39.00
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