Generalized Pairs Plot in R

Your innovative idea may come from a detail exploration of your data. Most of the cases that data will contain both continuous and categorical variables. You will need to find explainable pattern from this. What if it can be displayed methodologically in a single diagram and you can do it with simple and short lines of coding?

Why R?

R is an open-sourced programming language for statistical computing and graphics supported by the R Foundation for Statistical Computing. It is easy to learn and comfortable to work with its widely used integrated development environment- RStudio.

Install Packages

First of all we need to install required packages with the below codes.

install.packages("tidyverse")
install.packages("GGally")
install.packages("ISLR")

The ‘tidyverse’ package is for data wrangling & data visualization. The widely used ‘ggplot2’ package is encapsulated in the ‘tidyverse’. ‘GGally’ reduces the complexity of combining geometric objects with transformed data by adding several functions to ‘ggplot2’. We will use _Mid-Atlantic Wage Dat_a for this exercise which contains wage data for a group of 3000 male workers in the Mid-Atlantic region of US. **‘ISLR’ **package is required to load this dataset.

Load Packages

The packages need to be installed once but every time you run your script you need to load the packages with the **library** function.

library(tidyverse)
library(GGally)
library(ISLR)

#analytics #data-science #innovation #visualization #r

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Generalized Pairs Plot in R
Marcus  Flatley

Marcus Flatley

1594399440

Getting Started with R Markdown — Guide and Cheatsheet

In this blog post, we’ll look at how to use R Markdown. By the end, you’ll have the skills you need to produce a document or presentation using R Mardown, from scratch!

We’ll show you how to convert the default R Markdown document into a useful reference guide of your own. We encourage you to follow along by building out your own R Markdown guide, but if you prefer to just read along, that works, too!

R Markdown is an open-source tool for producing reproducible reports in R. It enables you to keep all of your code, results, plots, and writing in one place. R Markdown is particularly useful when you are producing a document for an audience that is interested in the results from your analysis, but not your code.

R Markdown is powerful because it can be used for data analysis and data science, collaborating with others, and communicating results to decision makers. With R Markdown, you have the option to export your work to numerous formats including PDF, Microsoft Word, a slideshow, or an HTML document for use in a website.

r markdown tips, tricks, and shortcuts

Turn your data analysis into pretty documents with R Markdown.

We’ll use the RStudio integrated development environment (IDE) to produce our R Markdown reference guide. If you’d like to learn more about RStudio, check out our list of 23 awesome RStudio tips and tricks!

Here at Dataquest, we love using R Markdown for coding in R and authoring content. In fact, we wrote this blog post in R Markdown! Also, learners on the Dataquest platform use R Markdown for completing their R projects.

We included fully-reproducible code examples in this blog post. When you’ve mastered the content in this post, check out our other blog post on R Markdown tips, tricks, and shortcuts.

Okay, let’s get started with building our very own R Markdown reference document!

R Markdown Guide and Cheatsheet: Quick Navigation

1. Install R Markdown

R Markdown is a free, open source tool that is installed like any other R package. Use the following command to install R Markdown:

install.packages("rmarkdown")

Now that R Markdown is installed, open a new R Markdown file in RStudio by navigating to File > New File > R Markdown…. R Markdown files have the file extension “.Rmd”.

2. Default Output Format

When you open a new R Markdown file in RStudio, a pop-up window appears that prompts you to select output format to use for the document.

New Document

The default output format is HTML. With HTML, you can easily view it in a web browser.

We recommend selecting the default HTML setting for now — it can save you time! Why? Because compiling an HTML document is generally faster than generating a PDF or other format. When you near a finished product, you change the output to the format of your choosing and then make the final touches.

One final thing to note is that the title you give your document in the pop-up above is not the file name! Navigate to File > Save As.. to name, and save, the document.

#data science tutorials #beginner #r #r markdown #r tutorial #r tutorials #rstats #rstudio #tutorial #tutorials

August  Larson

August Larson

1624422360

R vs Python: What Should Beginners Learn?

Let go of any doubts or confusion, make the right choice and then focus and thrive as a data scientist.

I currently lead a research group with data scientists who use both R and Python. I have been in this field for over 14 years. I have witnessed the growth of both languages over the years and there is now a thriving community behind both.

I did not have a straightforward journey and learned many things the hard way. However, you can avoid making the mistakes I made and lead a more focussed, more rewarding journey and reach your goals quicker than others.

Before I dive in, let’s get something out of the way. R and Python are just tools to do the same thing. Data Science. Neither of the tools is inherently better than the other. Both the tools have been evolving over years (and will likely continue to do so).

Therefore, the short answer on whether you should learn Python or R is: it depends.

The longer answer, if you can spare a few minutes, will help you focus on what really matters and avoid the most common mistakes most enthusiastic beginners aspiring to become expert data scientists make.

#r-programming #python #perspective #r vs python: what should beginners learn? #r vs python #r

Generalized Pairs Plot in R

Your innovative idea may come from a detail exploration of your data. Most of the cases that data will contain both continuous and categorical variables. You will need to find explainable pattern from this. What if it can be displayed methodologically in a single diagram and you can do it with simple and short lines of coding?

Why R?

R is an open-sourced programming language for statistical computing and graphics supported by the R Foundation for Statistical Computing. It is easy to learn and comfortable to work with its widely used integrated development environment- RStudio.

Install Packages

First of all we need to install required packages with the below codes.

install.packages("tidyverse")
install.packages("GGally")
install.packages("ISLR")

The ‘tidyverse’ package is for data wrangling & data visualization. The widely used ‘ggplot2’ package is encapsulated in the ‘tidyverse’. ‘GGally’ reduces the complexity of combining geometric objects with transformed data by adding several functions to ‘ggplot2’. We will use _Mid-Atlantic Wage Dat_a for this exercise which contains wage data for a group of 3000 male workers in the Mid-Atlantic region of US. **‘ISLR’ **package is required to load this dataset.

Load Packages

The packages need to be installed once but every time you run your script you need to load the packages with the **library** function.

library(tidyverse)
library(GGally)
library(ISLR)

#analytics #data-science #innovation #visualization #r

Base Plotting in R

Using the base plotting system in R can seem overwhelming, but it doesn’t have to be.
Base plotting in R can be intimidating. It takes a canvas approach to plot construction, allowing you to paint layer after layer of detail onto your graphics. As a result, there is a seemingly endless number of functions and attributes to learn, but there’s no need to panic or jump straight to ggplot. This article highlights the versatility of the base plot() function while giving you some quick tricks to create beautiful plots.

#data-visualization #data-science #r #learning-to-code #plotting

akshay L

akshay L

1611665228

R Programming Course

In this R Tutorial For Beginners video, you will learn r programming language from scratch to advance concepts. This R training video also covers hands-on demo and interview questions. This R Programming Course is a must-watch video for everyone who wishes to learn the R language and make a career in the data science domain.

#r programming course #r programming course #r tutorial for beginners #learn r language