Integrate Admin Template in ReactJS is today’s leading topic. We are using React v16, and also we need to download the template from this GitHub repo: https://github.com/almasaeed2010/AdminLTE/releases
As per requirement, first, we need to download the HTML template.
As per its original documentation, we need to install globally create-react-app globally.
npm install -g create-react-app create-react-app admin-app
It will make the boilerplate with the development server to run the necessary ReactJS application.
#reactjs #github #react v16
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DashboardKit is a Bootstrap 5 based Admin Template that comes with 170+ ready to use conceptual pages. We have made DashboardKit a really powerful backend template with having tons of UI components, form elements, charts, tables along with 7+ admin panels.
• Developer centric : Even a novice developer can customize the DashboardKit. Our code is readable and well optimized for any level of developer.
• Modern design : User experience is the major important part in DashboardKit. We’ve designed each page with conceptual user interface.
• Performance first : Code is handwritten from scratch & doing that we’ve achieved the best performance in DashboardKit.
• Well documented : Detailed documentation guide contains- Quick setup, Integrate into an existing project, How to use components, references, and changelog.
• Responsive : DashboardKit’s design renders fast & perfect on desktop, tablet & mobile without any lagging issue.
• Browsers Compatiblity : DashboardKit is well tested in Chrome, Firefox, Safari(macOS) , Edge & Internet Explorer(>=IE11).
• Regular updates : Any bug fixes, new features, improvements in DashboardKit will always free.
• Support : You can submit us the issues or any new feature request via our email support dashboardkit[at]gmail[dot]com.
• Bootstrap : Most popular design framework for developing responsive and mobile-first websites.
• Sass : Sass is a preprocessor scripting language that is interpreted or compiled into CSS.
• npm/Gulp : Node Package Manager with Gulp build system for the fast development.
DashboardKit free Bootstrap 5 admin template includes the basic layouts with below listed pages. For more features, please checkout pro version.
• Analytics Dashboard
• All Basic Bootstrap components
• 250+ Feather Icons
• Form Elements
• Bootstrap Basic Table
• Apex charts
• Google Map
• Login/Registration Pages
• Sample Page
We Solved your pain points?
You have seen lots of other admin templates but Is it really convenient for you? Has it resolve all your below pain points? We’re closely working on dashboard making since 2013 & we know the real pain points of our customer base.
• Hassle to start : Quickstart guide for start with DashboardKit. We also cover how to implement DashboardKit into your existing project with minimal effort. Guide for Quickstart
• Hard code structure : DashboardKit has a fully structured code with a well-commented guide that helps to ease your development.
• Components useability : You can easily access & use the DashboardKit’s all components using one-click code copy/paste mechanism. It will surely save your time.
• Responsive issues : Proper bug-free responsive design is a key factor for any project. DashboardKit is well optimized for that.
• Messy documentation : Simple easy to understand Documentation covers all aspects of setup, components guide, reference links, changelog.
Upgrated to Pro DashboardKit
For more Pages, UI components, forms variants & access to 7+ admin panels check out the Premium version of DashboardKit Bootstrap 5 Admin Template.
DashboardKit documentation helps you in all aspects related to setup, how to use components, plugins references & changelog. Please refer this link.
For support please contact us on dashboardkit[at].gmail[dot]com.
#dashboardkit #bootstrap 5 admin template #bootstrap admin template #admin dashboard #admin template #bootstrap
Welcome to my blog , hey everyone in this article you learn how to customize the Django app and view in the article you will know how to register and unregister models from the admin view how to add filtering how to add a custom input field, and a button that triggers an action on all objects and even how to change the look of your app and page using the Django suit package let’s get started.
#django #create super user django #customize django admin dashboard #django admin #django admin custom field display #django admin customization #django admin full customization #django admin interface #django admin register all models #django customization
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.
[tidyverse](https://www.dataquest.io/blog/tutorial-getting-started-with-r-and-rstudio/#tve-jump-173bb264c2b)Packages into Memory
#data science tutorials #beginner #r tutorial #r tutorials #rstats #tutorial #tutorials
What will we do?
#reactjs #springboot #integration #tutorial
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:
Common symptoms of messy data include data that contain:
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:
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
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.
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:
Once the package is installed, load it to memory:
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…
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