A script is simply a text file containing a set of commands and comments. The script can be saved and used later to re-execute the saved commands. The script can also be edited so you can execute a modified version of the commands.
You can open a new empty script by clicking the New File icon in the upper left of the main RStudio toolbar. This icon looks like a white square with a white plus sign in a green circle. Clicking the icon opens the New File Menu. Click the R Script menu option and the script editor will open with an empty script. Your R Studio should look similar to below (image source: R for Data Science)
home alone 2 script pdf
R Markdown documents take script files to a new level by allowing you to mix R commands with explanatory text. Think of an R Markdown document as an R script on steroids. Your R Markdown source document is compiled into an output report evaluating the R commands in the source document to produce easily reproducible results in an aesthetically pleasing form. It combines code, results from the code, and narrative text explaining the results to produce beautiful documents and academic reports.
These documents will provide us an easy-to-read document to grade; more importantly, you will get to practice (1) writing scripts, (2) keeping track of the analyses you run, and (3) organizing your output in a reader-friendly manner. When you submit these documents on Canvas, do not combine them into a zipped compressed folder. They should be two separate files.
To be clear, R is a programming language. RStudio is an application. R Markdown is a markup syntax to convert R script and text into a pdf or html document. It allows for presentation-ready documents that show commands and results in a seamless flow. When you write R code and embed it in presentation documents created using R Markdown, you are forced to explicitly state the steps you took to do your research.
Once again, treat the R Markdown file as a self-contained, stand alone script. This is an important concept to understand because many students get tripped up on it when first starting out.
The only time that the script might be dealing with that particular layer is when the MXD and layer variables are first all defined. (Each of these layers -- Annexations, Customers, Pole -- are in SDE #1.)
I rebuilt the MXD from scratch with the same layers, and saw the exact same issue. Add layer from SDE #2, the script is much slower. Take layer from SDE #2 out of the map, the script goes back to its original speed.
I asked the network/database guy if he knew what was going on, and he'd never seen anything like it. How can a layer that isn't involved in an analysis have any impact on performance, let alone slow it down 10x?
Statisticians creating epidemiological analyses in R may need to make an analysis available to users who are unfamiliar with the language. A solution is to provide a user-friendly web application for the R script. Values for variables and data files for processing are submitted by the user on a web form. The web application then runs the R script on a server, out of sight of the user, and returns the results of the analysis to the user's web page.
Rwui (R Web User Interface) [8] is a web application that creates web applications for running R scripts. Code for the web application is generated automatically so that a fully functional web application for an R script can be implemented in a matter of minutes. So statisticians who are unfamiliar with web programming can easily create web applications for running R scripts. 2ff7e9595c
Comments