AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |
Back to Blog
![]() Since the document is based on code, future changes are easy to implement and the document is reproducible by others.Ĭreating a report is a separate, time-consuming step. The same document can be published to multiple formats (e.g. HTML, PDF, Word). Rich text is added throughout the analytic process to describe the motivation and the conclusions for each chunk of the code.Ĭomments are added to the script, and a report that describes the entire analysis is drafted separately after the script is completed.Īll output is embedded in a single document and collocated with the narrative and code chunk to which it belongs.Įach individual output is sent to file and is collected later into a report. The following table summarizes the differences between notebooks and scripts. The dual format gives you a single file that can be viewed in a browser or opened in the RStudio IDE. The HTML output of R Notebooks is a dual-file format that contains both the HTML and the R Markdown source code. When you save the notebook, the same cache is rendered inside a document. When you execute a code chunk in an R Notebook, the output is cached and rendered inside the IDE. For example, R Markdown documents give you many options when selecting graphics, templates, and formats for your output. These are not necessarily inherent differences, but differences of emphasis. R Notebooks have some features that are not found in traditional notebooks. They can be used to create elegantly formatted output in multiple document types (e.g. HTML, PDF, and Word). That means they are written in plain text and work well with version control. R Notebooks are based on R Markdown documents. Number 2: R Notebooks have great features You can find my code and about 50 plots under the project directory (I hope you have permissions).Hold onto your hats while I batch execute this entire script!.I have a thousand lines of code and you get to read my amazing comments!.Here is the thought process for doing data science with scripts: Sharing your results in a report requires a separate, time consuming process. Your output may or may not be captured at all. You add comments to the code, but the comments tend to be terse or nonexistent. If you do data science with R scripts, on the other hand, you develop your code as a single script. I am going to share all chunks of code with you in a single, reproducible document.I am going to execute this chunk of code and show you the output.I have a chunk of code that I want to tell you about. ![]() Here is the thought process for doing data science with notebooks: When you are done, you have an elegant report that can be shared with others. You add narrative and output around the code chunk, which puts it into context and makes it reproducible. With notebooks, you break your script into manageable code chunks. Doing science with physical notebooks is an idea that is centuries old.Įlectronic notebooks follow the same pattern as physical notebooks, but apply the pattern to code. The process had a nice flow which helped me improve my thinking. When I conducted an experiment, I drew sketches and wrote down my results. In my high school science class I used a laboratory notebook that contained all my experiments. If scripting is for writing software, then notebooks are for doing data science. Number 3: Notebooks are for doing science ![]()
0 Comments
Read More
Leave a Reply. |