How to Build Ethereum Dapp - Full Tutorial for Beginners

We are going to build 5 Ethereum Dapps. For each of them, we will:

  • Develop smart contracts
  • Test smart contracts
  • Build a web frontend to interact with the smart contract

The course covers:

  • The architecture of Dapps
  • Solidity, the programming language for Ethereum smart contract
  • Remix, the online IDE for Solidity
  • Truffle, the framework for Solidity smart contracts
  • Ganache, the local development Blockchain
  • Web3, the JS library to integrate a Dapp frontend
  • Metamask, the Ethereum wallet used by most Dapps (browser extension)
  • Deployment to public testnet (Ropsten) and mainnet, using Infura API

Requirements:

  • NodeJS v10
  • Works on both Windows, Linux and Mac

💻 Code: https://github.com/jklepatch/eattheblocks/tree/master/dapp-30

#blockchain #node-js

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How to Build Ethereum Dapp - Full Tutorial for Beginners
Mark Anderson

Mark Anderson

1616681478

DApps Development Platform kickstart your business development

We at Blockchain App Factory provide a DApps Development platform for users to connect developers directly with no middleman interaction to handle program and data changes. The DApp focuses on backend programming to run as a decentralized peer-to-peer (P2P) network. The DApp benefits from Zero downtime, secured privacy, complete data integrity, and resistance to censorship.

#dapps platform development #dapps platform development company #ethereum dapp development companies #ethereum dapps platform development company #dapps development company #how to build dapps

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

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

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

Sival Alethea

Sival Alethea

1624305600

C++ Tutorial for Beginners - Full Course. DO NOT MISS!!!

This course will give you a full introduction into all of the core concepts in C++.
⭐️ Contents ⭐
⌨️ (0:00:00) Introduction
⌨️ (0:01:38) Windows Installation
⌨️ (0:04:54) Mac Installation
⌨️ (0:08:44) Setup & Hello World
⌨️ (0:12:29) Drawing a Shape
⌨️ (0:19:55) Variables
⌨️ (0:31:43) Data Types
⌨️ (0:39:15) Working With Strings
⌨️ (0:49:00) Working With Numbers
⌨️ (0:59:41) Getting User Input
⌨️ (1:05:32) Building a Calculator
⌨️ (1:09:28) Building a Mad Libs
⌨️ (1:13:45) Arrays
⌨️ (1:20:03) Functions
⌨️ (1:29:47) Return Statement
⌨️ (1:35:22) If Statements
⌨️ (1:47:15) If Statements (con’t)
⌨️ (1:55:58) Building a Better Calculator
⌨️ (2:02:20) Switch Statements
⌨️ (2:10:47) While Loops
⌨️ (2:18:53) Building a Guessing Game
⌨️ (2:29:18) For Loops
⌨️ (2:38:32) Exponent Function
⌨️ (2:45:21) 2d Arrays & Nested Loops
⌨️ (2:54:55) Comments
⌨️ (2:59:11) Pointers
⌨️ (3:13:26) Classes & Objects
⌨️ (3:25:40) Constructor Functions
⌨️ (3:34:41) Object Functions
⌨️ (3:41:43) Getters & Setters
⌨️ (3:54:04) Inheritance

Course developed by Mike Dane. Check out his YouTube channel for more great programming
📺 The video in this post was made by freeCodeCamp.org
The origin of the article: https://www.youtube.com/watch?v=vLnPwxZdW4Y&list=PLWKjhJtqVAblfum5WiQblKPwIbqYXkDoC&index=10
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