1621652194

# R Programming For Beginners-Full Course | Learn R in 3 Hours| R Language Tutorial

Learn R Programming for Beginners in our 3-hour video releasing today! R is a programming language software environment used for statistical computing and graphics. Owing to its computational power, the R programming language is extremely favoured by statisticians and data analysts and data miners for the purpose of carrying out data analysis and developing statistical software. These factors have made R to be one of the most favoured programming languages with data science, which is exactly why so many people want to learn it today.

Great Learning brings you this tutorial on R Programming for Beginners to help you understand everything you need to know about this topic and getting started on the journey to learn about it well. This video starts with an introduction to R and its installation, followed by understanding the concepts of variables, data types and operators, etc. in R. Following this, we will understand data manipulation with dplyr and data visualization with ggplot2. Finally, we learn about neural networks! This video teaches R programming and its key functions and concepts with a case study, demonstrations & examples to help you get started on the right foot.

• 00:00:00 Introduction
• 00:01:10 Agenda
• 00:01:37 Installing R and R-studio
• 00:03:09 Variables, Datatypes and Operators
• 00:16:48 Vector in R
• 00:24:49 List, Matrix and Array in R
• 00:38:47 Inbuilt Functions in R
• 00:43:35 Flow Control Statements in R
• 00:54:12 Data Manipulation with dplyr
• 01:07:50 Data Visualization with ggplot2
• 01:27:16 Pokemon Case Study
• 02:03:31 Intro to Neural Networks
• 02:31:54 Demo: Neural Networks using R

#python #data-analysis #r #r-programming #programming

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

1621652194

## R Programming For Beginners-Full Course | Learn R in 3 Hours| R Language Tutorial

Learn R Programming for Beginners in our 3-hour video releasing today! R is a programming language software environment used for statistical computing and graphics. Owing to its computational power, the R programming language is extremely favoured by statisticians and data analysts and data miners for the purpose of carrying out data analysis and developing statistical software. These factors have made R to be one of the most favoured programming languages with data science, which is exactly why so many people want to learn it today.

Great Learning brings you this tutorial on R Programming for Beginners to help you understand everything you need to know about this topic and getting started on the journey to learn about it well. This video starts with an introduction to R and its installation, followed by understanding the concepts of variables, data types and operators, etc. in R. Following this, we will understand data manipulation with dplyr and data visualization with ggplot2. Finally, we learn about neural networks! This video teaches R programming and its key functions and concepts with a case study, demonstrations & examples to help you get started on the right foot.

• 00:00:00 Introduction
• 00:01:10 Agenda
• 00:01:37 Installing R and R-studio
• 00:03:09 Variables, Datatypes and Operators
• 00:16:48 Vector in R
• 00:24:49 List, Matrix and Array in R
• 00:38:47 Inbuilt Functions in R
• 00:43:35 Flow Control Statements in R
• 00:54:12 Data Manipulation with dplyr
• 01:07:50 Data Visualization with ggplot2
• 01:27:16 Pokemon Case Study
• 02:03:31 Intro to Neural Networks
• 02:31:54 Demo: Neural Networks using R

#python #data-analysis #r #r-programming #programming

1599097440

## Data Visualization in R with ggplot2: A Beginner Tutorial

A famous general is thought to have said, “A good sketch is better than a long speech.” That advice may have come from the battlefield, but it’s applicable in lots of other areas — including data science. “Sketching” out our data by visualizing it using ggplot2 in R is more impactful than simply describing the trends we find.

This is why we visualize data. We visualize data because it’s easier to learn from something that we can see rather than read. And thankfully for data analysts and data scientists who use R, there’s a tidyverse package called ggplot2 that makes data visualization a snap!

In this blog post, we’ll learn how to take some data and produce a visualization using R. To work through it, it’s best if you already have an understanding of R programming syntax, but you don’t need to be an expert or have any prior experience working with ggplot2

#data science tutorials #beginner #ggplot2 #r #r tutorial #r tutorials #rstats #tutorial #tutorials

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.

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!

## 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.

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

1596513720

## 1. Characteristics of Clean Data and Messy Data

What exactly is clean data? Clean data is accurate, complete, and in a format that is ready to analyze. Characteristics of clean data include data that are:

• Free of duplicate rows/values
• Error-free (e.g. free of misspellings)
• Relevant (e.g. free of special characters)
• The appropriate data type for analysis
• Free of outliers (or only contain outliers have been identified/understood), and
• Follows a “tidy data” structure

Common symptoms of messy data include data that contain:

• Special characters (e.g. commas in numeric values)
• Numeric values stored as text/character data types
• Duplicate rows
• Misspellings
• Inaccuracies
• White space
• Missing data
• Zeros instead of null values

## 2. Motivation

In this blog post, we will work with five property-sales datasets that are publicly available on the New York City Department of Finance Rolling Sales Data website. We encourage you to download the datasets and follow along! Each file contains one year of real estate sales data for one of New York City’s five boroughs. We will work with the following Microsoft Excel files:

• rollingsales_bronx.xls
• rollingsales_brooklyn.xls
• rollingsales_manhattan.xls
• rollingsales_queens.xls
• rollingsales_statenisland.xls

As we work through this blog post, imagine that you are helping a friend launch their home-inspection business in New York City. You offer to help them by analyzing the data to better understand the real-estate market. But you realize that before you can analyze the data in R, you will need to diagnose and clean it first. And before you can diagnose the data, you will need to load it into R!

Benefits of using tidyverse tools are often evident in the data-loading process. In many cases, the tidyverse package `readxl` will clean some data for you as Microsoft Excel data is loaded into R. If you are working with CSV data, the tidyverse `readr` package function `read_csv()` is the function to use (we’ll cover that later).

Let’s look at an example. Here’s how the Excel file for the Brooklyn borough looks:

The Brooklyn Excel file

Now let’s load the Brooklyn dataset into R from an Excel file. We’ll use the `readxl`package. We specify the function argument `skip = 4` because the row that we want to use as the header (i.e. column names) is actually row 5. We can ignore the first four rows entirely and load the data into R beginning at row 5. Here’s the code:

``````library(readxl) # Load Excel files
brooklyn <- read_excel("rollingsales_brooklyn.xls", skip = 4)
``````

Note we saved this dataset with the variable name `brooklyn` for future use.

## 4. View the Data with tidyr::glimpse()

The tidyverse offers a user-friendly way to view this data with the `glimpse()` function that is part of the `tibble` package. To use this package, we will need to load it for use in our current session. But rather than loading this package alone, we can load many of the tidyverse packages at one time. If you do not have the tidyverse collection of packages, install it on your machine using the following command in your R or R Studio session:

``````install.packages("tidyverse")
``````

Once the package is installed, load it to memory:

``````library(tidyverse)
``````

Now that `tidyverse` is loaded into memory, take a “glimpse” of the Brooklyn dataset:

``````glimpse(brooklyn)
## Observations: 20,185
## Variables: 21
## \$ BOROUGH <chr> "3", "3", "3", "3", "3", "3", "…
## \$ NEIGHBORHOOD <chr> "BATH BEACH", "BATH BEACH", "BA…
## \$ `BUILDING CLASS CATEGORY` <chr> "01 ONE FAMILY DWELLINGS", "01 …
## \$ `TAX CLASS AT PRESENT` <chr> "1", "1", "1", "1", "1", "1", "…
## \$ BLOCK <dbl> 6359, 6360, 6364, 6367, 6371, 6…
## \$ LOT <dbl> 70, 48, 74, 24, 19, 32, 65, 20,…
## \$ `EASE-MENT` <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## \$ `BUILDING CLASS AT PRESENT` <chr> "S1", "A5", "A5", "A9", "A9", "…
## \$ ADDRESS <chr> "8684 15TH AVENUE", "14 BAY 10T…
## \$ `APARTMENT NUMBER` <chr> NA, NA, NA, NA, NA, NA, NA, NA,…
## \$ `ZIP CODE` <dbl> 11228, 11228, 11214, 11214, 112…
## \$ `RESIDENTIAL UNITS` <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1…
## \$ `COMMERCIAL UNITS` <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## \$ `TOTAL UNITS` <dbl> 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1…
## \$ `LAND SQUARE FEET` <dbl> 1933, 2513, 2492, 1571, 2320, 3…
## \$ `GROSS SQUARE FEET` <dbl> 4080, 1428, 972, 1456, 1566, 22…
## \$ `YEAR BUILT` <dbl> 1930, 1930, 1950, 1935, 1930, 1…
## \$ `TAX CLASS AT TIME OF SALE` <chr> "1", "1", "1", "1", "1", "1", "…
## \$ `BUILDING CLASS AT TIME OF SALE` <chr> "S1", "A5", "A5", "A9", "A9", "…
## \$ `SALE PRICE` <dbl> 1300000, 849000, 0, 830000, 0, …
## \$ `SALE DATE` <dttm> 2020-04-28, 2020-03-18, 2019-0…
``````

The `glimpse()` function provides a user-friendly way to view the column names and data types for all columns, or variables, in the data frame. With this function, we are also able to view the first few observations in the data frame. This data frame has 20,185 observations, or property sales records. And there are 21 variables, or columns.

#data science tutorials #beginner #r #r tutorial #r tutorials #rstats #tidyverse #tutorial #tutorials