David Nano

David Nano

1559545528

Svelte 3 Reaction & QuickStart Tutorial

First, we learn some basic concepts in **Svelte **3 and how to build reactive UI components with JavaScript. Then we build a fullstack realtime todo list with RxFire and Svelte…

Svelte.js - The Complete Guide

☞ http://learnstartup.net/p/6L2KpOKp8

Thanks for reading :heart: If you liked this post, share it with all of your programming buddies! Follow me on Facebook | Twitter

Learn More

☞ Svelte.js - The Complete Guide

☞ The Complete JavaScript Course 2019: Build Real Projects!

☞ Advanced CSS and Sass: Flexbox, Grid, Animations and More!

☞ CSS - The Complete Guide (incl. Flexbox, Grid & Sass)

☞ CSS Bootcamp - Master CSS (Including CSS Grid / Flexbox)

☞ Build Responsive Real World Websites with HTML5 and CSS3

☞ Become a JavaScript developer - Learn (React, Node,Angular)

☞ JavaScript: Understanding the Weird Parts

☞ Vue JS 2 - The Complete Guide (incl. Vue Router & Vuex)

☞ The Full JavaScript & ES6 Tutorial - (including ES7 & React)

☞ JavaScript - Step By Step Guide For Beginners

☞ The Web Developer Bootcamp

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Willie Beier

Willie Beier

1596728880

Tutorial: Getting Started with R and RStudio

In this tutorial we’ll learn how to begin programming with R using RStudio. We’ll install R, and RStudio RStudio, an extremely popular development environment for R. We’ll learn the key RStudio features in order to start programming in R on our own.

If you already know how to use RStudio and want to learn some tips, tricks, and shortcuts, check out this Dataquest blog post.

Table of Contents

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

Rowland Gutkowski

Rowland Gutkowski

1596513720

Tutorial: Loading and Cleaning Data with R and the tidyverse

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!

3. Load Data into R with readxl

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 readxlpackage. 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

Jeromy Lowe

Jeromy Lowe

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

Autumn Blick

Autumn Blick

1596584126

R Tutorial: Better Blog Post Analysis with googleAnalyticsR

In my previous role as a marketing data analyst for a blogging company, one of my most important tasks was to track how blog posts performed.

On the surface, it’s a fairly straightforward goal. With Google Analytics, you can quickly get just about any metric you need for your blog posts, for any date range.

But when it comes to comparing blog post performance, things get a bit trickier.

For example, let’s say we want to compare the performance of the blog posts we published on the Dataquest blog in June (using the month of June as our date range).

But wait… two blog posts with more than 1,000 pageviews were published earlier in the month, And the two with fewer than 500 pageviews were published at the end of the month. That’s hardly a fair comparison!

My first solution to this problem was to look up each post individually, so that I could make an even comparison of how each post performed in their first day, first week, first month, etc.

However, that required a lot of manual copy-and-paste work, which was extremely tedious if I wanted to compare more than a few posts, date ranges, or metrics at a time.

But then, I learned R, and realized that there was a much better way.

In this post, we’ll walk through how it’s done, so you can do my better blog post analysis for yourself!

What we’ll need

To complete this tutorial, you’ll need basic knowledge of R syntax and the tidyverse, and access to a Google Analytics account.

Not yet familiar with the basics of R? We can help with that! Our interactive online courses teach you R from scratch, with no prior programming experience required. Sign up and start today!

You’ll also need the dyplr, lubridate, and stringr packages installed — which, as a reminder, you can do with the install.packages() command.

Finally, you will need a CSV of the blog posts you want to analyze. Here’s what’s in my dataset:

post_url: the page path of the blog post

post_date: the date the post was published (formatted m/d/yy)

category: the blog category the post was published in (optional)

title: the title of the blog post (optional)

Depending on your content management system, there may be a way for you to automate gathering this data — but that’s out of the scope of this tutorial!

For this tutorial, we’ll use a manually-gathered dataset of the past ten Dataquest blog posts.

#data science tutorials #promote #r #r tutorial #r tutorials #rstats #tutorial #tutorials

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