From aefa47ee05e326108ce003bd8b6db209f71ab5d2 Mon Sep 17 00:00:00 2001 From: Edithabich Date: Tue, 7 May 2024 16:00:16 +0300 Subject: [PATCH 1/2] SEO-182838-PA-Title-too-short-changes --- predictive-analytics/Getting-Started-with-R.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/predictive-analytics/Getting-Started-with-R.md b/predictive-analytics/Getting-Started-with-R.md index 848fca8..7a17221 100644 --- a/predictive-analytics/Getting-Started-with-R.md +++ b/predictive-analytics/Getting-Started-with-R.md @@ -7,7 +7,7 @@ control: Essential Predictive Analytics documentation: ug --- -# Getting Started with R +# Getting Started with R in Predictive Analytics R is a free, open-source statistical programming language. Its utility and popularity show the same explosive growth that characterizes the increasing availability and variety of data. And while the command-line interface of R can be intimidating at first, the strengths of this approach, such as increased ability to share and reproduce analysis, soon become apparent. The following guidelines serve as an introduction to R for those who are intrigued by its possibilities. Chapter 1 lays out the steps for installing R and a companion product, RStudio, for working with variables and data sets, and for discovering the power of the third-party packages that supplement R’s functionality. From 650d1ba0c57d5fb3c44d4e8eb1b76d7ef98ffab3 Mon Sep 17 00:00:00 2001 From: Edithabich Date: Wed, 8 May 2024 09:21:50 +0300 Subject: [PATCH 2/2] fixed ci issue --- predictive-analytics/Getting-Started-with-R.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/predictive-analytics/Getting-Started-with-R.md b/predictive-analytics/Getting-Started-with-R.md index 7a17221..e6607da 100644 --- a/predictive-analytics/Getting-Started-with-R.md +++ b/predictive-analytics/Getting-Started-with-R.md @@ -280,7 +280,7 @@ If you would like to delete a package, use remove.packages(), like this: remove. ## R’s datasets package -The built-in package "datasets" makes it easy to experiment with R's procedures using real data. Although this package is part of R's base installation, you must load it. You can either select it in the Packages tab or enter library("datasets") or require("datasets"). You can see a list of the available data sets by typing data() or by going to the [R Datasets](http://stat.ethz.ch/R-manual/R-devel/library/datasets/html/00Index.html)[ ](http://stat.ethz.ch/R-manual/R-devel/library/datasets/html/00Index.html)[Package](http://stat.ethz.ch/R-manual/R-devel/library/datasets/html/00Index.html)[ ](http://stat.ethz.ch/R-manual/R-devel/library/datasets/html/00Index.html)list. +The built-in package "datasets" makes it easy to experiment with R's procedures using real data. Although this package is part of R's base installation, you must load it. You can either select it in the Packages tab or enter library("datasets") or require("datasets"). You can see a list of the available data sets by typing data() or by going to the [R Datasets](https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/00Index.html)[ ](https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/00Index.html)[Package](https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/00Index.html)[ ](https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/00Index.html)list. For more information on a particular data set, you can search R Help by typing “?” and the name of the dataset with no space: ?airmiles. You can also see the contents of the dataset by entering its name: airmiles. To see the structure of the dataset, use str(), like this: str(airmiles). This shows you what kind of data set it is, how many observations and variables it has, and the first few values.