![]() ![]() title: "Penguins Report" author: "David" date: "" output: word_document - ``` library(tidyverse) ``` You’ll learn how to work with YAML metadata, R code chunks, and Markdown-formatted text, create in-line R code that can change the report’s text dynamically, and run the document’s code in various ways. In this chapter, we’ll break down the pieces of an R Markdown document, then talk about some potential pitfalls and best practices. If you need to recreate that January customer satisfaction report in February, you can rerun your code to produce a new document with the newest data, and to fix an error in your analysis, you can simply adjust your code. When you use a single tool, your workflow becomes way more efficient. R Markdown combines data analysis, data visualization, and other R code with narrative text to create a document that can be exported to many formats, including Word, PDF, and HTML, to share with non-R users. For example, you might realize you forgot to include a couple of surveys in your original analysis or catch a mistake. This multi-tool process might work for one-time project, but let’s be honest: Few projects are really one-time. ![]() But what happens when, the next month, new surveys roll in, and you have to redo your report? Yup, back through steps one through five. Many people use this workflow for data analysis. Write your report in Word, pasting in your charts from Excel along the way.Export summaries of your data as Excel spreadsheets.Use SPSS to clean and analyze your data.Download your data from Google Sheets and import it into a statistical analysis tool like SPSS.Now you’re ready to analyze the data and write up your results. Imagine this: You’ve collected surveys about customer satisfaction with your new product. ![]()
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