Easy VueJS Drag & Drop tutorial

Are you ready to learn how to make VueJS Drag and Drop components? Too bad it’s time to learn. So let’s do it. In this video we learn about drag and drop event listeners to create a Trello like drag and drop system in VUE!

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10 Best Vue Drag and Drop Component For Your App

Vue Drag and drop is a feature of many interactive web apps. It provides an intuitive way for users to manipulate their data. Adding drag and drop feature is easy to add to Vue.js apps.

Here are 10 vue drop components that contribute to the flexibility of your vue application.

1. Vue.Draggable

Vue component (Vue.js 2.0) or directive (Vue.js 1.0) allowing drag-and-drop and synchronization with view model array.

Based on and offering all features of Sortable.js

Demo: https://sortablejs.github.io/Vue.Draggable/#/simple

Download: https://github.com/SortableJS/Vue.Draggable/archive/master.zip

2. realtime-kanban-vue

Real-time kanban board built with Vue.js and powered by Hamoni Sync.

Demo: https://dev.to/pmbanugo/real-time-kanban-board-with-vuejs-and-hamoni-sync-52kg

Download: https://github.com/pmbanugo/realtime-kanban-vue/archive/master.zip

3. vue-nestable

Drag & drop hierarchical list made as a vue component.

Goals

• A simple vue component to create a draggable list to customizable items
• Reorder items by dragging them above an other item
• Intuitively nest items by dragging right
• Fully customizable, ships with no css
• Everything is configurable: item identifier, max nesting level, threshold for nesting

Demo: https://rhwilr.github.io/vue-nestable/

Download: https://github.com/rhwilr/vue-nestable/archive/master.zip

4. VueDraggable

VueJS directive for drag and drop.

Native HTML5 drag and drop implementation made for VueJS.

Demo: https://vivify-ideas.github.io/vue-draggable/

Download: https://github.com/Vivify-Ideas/vue-draggable/archive/master.zip

5. vue-grid-layout

vue-grid-layout is a grid layout system, like Gridster, for Vue.js. Heavily inspired in React-Grid-Layout

Demo: https://jbaysolutions.github.io/vue-grid-layout/examples/01-basic.html

Download: https://github.com/jbaysolutions/vue-grid-layout/archive/master.zip

6. vue-drag-tree

It’s a tree components(Vue2.x) that allow you to drag and drop the node to exchange their data .

Feature

• Double click on an node to turn it into a folder
• Drag and Drop the tree node, even between two different levels
• Controls whether a particular node can be dragged and whether the node can be plugged into other nodes
• Append/Remove Node in any level (#TODO)

Demo: https://vigilant-curran-d6fec6.netlify.com/#/

Download: https://github.com/shuiRong/vue-drag-tree/archive/master.zip

7. VueDragDrop

A Simple Drag & Drop example created in Vue.js.

Demo: https://seregpie.github.io/VueDragDrop/

Download: https://github.com/SeregPie/VueDragDrop/archive/master.zip

8. Vue-drag-resize

Vue Component for resize and drag elements.

Demo: http://kirillmurashov.com/vue-drag-resize/

Download: https://github.com/kirillmurashov/vue-drag-resize/archive/master.zip

9. vue-smooth-dnd

A fast and lightweight drag&drop, sortable library for Vue.js with many configuration options covering many d&d scenarios.

This library consists wrapper Vue.js components over smooth-dnd library.

Show, don’t tell !

Demo: https://kutlugsahin.github.io/vue-smooth-dnd/#/cards

Download: https://github.com/kutlugsahin/vue-smooth-dnd/archive/master.zip

10. vue-dragula

Drag and drop so simple it hurts

Demo: http://astray-git.github.io/vue-dragula/

Download: https://github.com/Astray-git/vue-dragula/archive/master.zip

#vue #vue-drag #vue-drop #drag-and-drop #vue-drag-and-drop

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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

• 1. Install R
• 2. Install RStudio
• 3. First Look at RStudio
• 4. The Console
• 5. The Global Environment
• 6. Install the [tidyverse](https://www.dataquest.io/blog/tutorial-getting-started-with-r-and-rstudio/#tve-jump-173bb26184b) Packages
• 7. Load the [tidyverse](https://www.dataquest.io/blog/tutorial-getting-started-with-r-and-rstudio/#tve-jump-173bb264c2b) Packages into Memory
• 8. Identify Loaded Packages
• 9. Get Help on a Package
• 10. Get Help on a Function
• 11. RStudio Projects
• 12. Save Your “Real” Work. Delete the Rest.
• 13. R Scripts
• 14. Run Code
• 15. Access Built-in Datasets
• 16. Style
• 17. Reproducible Reports with R Markdown
• 18. Use RStudio Cloud
• 19. Get Your Hands Dirty!
• Additional Resources
• Bonus: Cheatsheets

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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.

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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