Code  Camp

Code Camp

1602032876

Learn React from Scratch: A Step-by-Step Guide

Learn the basics of React in this comprehensive course. You will learn about fundamentals, hooks, context API, react router, custom hooks, and more.

You will learn about fundamentals, hooks, context API, react router, custom hooks, and more. React is one of the most popular ways to build user interfaces using JavaScript.

React is one of the most popular ways to build user interfaces using JavaScript.

React projects video coming soon!

💻 Basic React Code: https://github.com/john-smilga/react-basics-2020
💻 Advanced React: https://github.com/john-smilga/react-advanced-2020

⭐️ Course Contents ⭐️
⌨️ (00:00) Intro
⌨️ (01:09) About React
⌨️ (06:51) Goals
⌨️ (08:50) Structure
⌨️ (09:46) Course Requirements
⌨️ (11:45) Dev Environment Setup
⌨️ (16:26) Text Editor Setup
⌨️ (18:20) Command Line Basics
⌨️ (25:00) NPM Basics
⌨️ (35:07) Create-React-App
⌨️ (37:05) Install Create-React-App
⌨️ (43:19) Folder Structure
⌨️ (52:25) Clean Boilerplate
⌨️ (54:00) First Component
⌨️ (1:04:13) Text Editor Setup
⌨️ (1:12:29) First Component In Detail
⌨️ (1:19:18) JSX Rules
⌨️ (1:28:55) Nested Components And Tools
⌨️ (1:34:50) Mini Book Project
⌨️ (1:42:59) CSS Basics
⌨️ (1:54:41) JSX Css
⌨️ (2:01:31) JSX Javascript
⌨️ (2:09:30) Props
⌨️ (2:25:41) Props Destructuring
⌨️ (2:29:44) Props - Children
⌨️ (2:36:13) Simple List
⌨️ (2:45:12) Proper List
⌨️ (2:52:49) Key Prop And Spread Operator
⌨️ (2:58:52) Event Basics
⌨️ (3:11:12) Import And Export Statements
⌨️ (3:23:25) Free Hosting
⌨️ (3:28:22) Advanced Intro
⌨️ (3:30:42) Get Starter Project
⌨️ (3:31:35) Install Starter
⌨️ (3:33:43) Starter Overview
⌨️ (3:39:00) UseState - Simple Use Case
⌨️ (3:48:44) UseState - Basics
⌨️ (4:00:47) General Rules Of Hooks
⌨️ (4:03:39) UseState - Array Example
⌨️ (4:17:27) UseState - Object Example
⌨️ (4:23:00) UseState - Multiple State Values
⌨️ (4:26:17) Simple Counter
⌨️ (4:32:10) Functional Update Form
⌨️ (4:44:55) Matching Project
⌨️ (4:47:27) useEffect - Basics
⌨️ (4:57:47) useEffect - Conditional
⌨️ (5:01:40) useEffect - Dependency List
⌨️ (5:07:13) useEffect - Cleanup Function
⌨️ (5:19:24) useEffect - Fetch Data
⌨️ (5:31:29) Multiple Returns
⌨️ (5:37:09) Multiple Returns - Fetching Data
⌨️ (5:52:07) Short - Circuit Evaluation
⌨️ (6:02:50) Ternary Operator
⌨️ (6:11:30) Show/Hide Component
⌨️ (6:20:15) Form Basics
⌨️ (6:28:53) Controlled Inputs
⌨️ (6:38:09) Add Item To The List
⌨️ (6:50:32) Multiple Inputs
⌨️ (7:06:29) useRef
⌨️ (7:16:20) useReducer - UseState Setup
⌨️ (7:29:52) useReducer - Refactor
⌨️ (7:37:15) useReducer - Add Item
⌨️ (7:53:14) useReducer - Remove Item
⌨️ (8:00:56) Prop Drilling -
⌨️ (8:14:36) Context API / - useContext
⌨️ (8:26:22) Custom Hooks - useFetch
⌨️ (8:38:09) PropTypes - Setup
⌨️ (8:47:48) PropTypes - Images
⌨️ (8:55:48) PropTypes - Default Values
⌨️ (9:00:41) React Router Intro
⌨️ (9:05:52) React Router - Basic Setup
⌨️ (9:15:50) React Router - Error And Switch Component
⌨️ (9:18:51) React Router - Links
⌨️ (9:23:22) React Router - URL Params And Placeholder
⌨️ (9:37:51) React Optimization Warning
⌨️ (9:41:21) React.memo
⌨️ (9:49:50) useCallback
⌨️ (9:58:00) useMemo
⌨️ (10:04:15) useCallback - Fetch Example

#react #javascript #web-development #programming #developer

What is GEEK

Buddha Community

Learn React from Scratch:  A Step-by-Step Guide
Autumn  Blick

Autumn Blick

1598839687

How native is React Native? | React Native vs Native App Development

If you are undertaking a mobile app development for your start-up or enterprise, you are likely wondering whether to use React Native. As a popular development framework, React Native helps you to develop near-native mobile apps. However, you are probably also wondering how close you can get to a native app by using React Native. How native is React Native?

In the article, we discuss the similarities between native mobile development and development using React Native. We also touch upon where they differ and how to bridge the gaps. Read on.

A brief introduction to React Native

Let’s briefly set the context first. We will briefly touch upon what React Native is and how it differs from earlier hybrid frameworks.

React Native is a popular JavaScript framework that Facebook has created. You can use this open-source framework to code natively rendering Android and iOS mobile apps. You can use it to develop web apps too.

Facebook has developed React Native based on React, its JavaScript library. The first release of React Native came in March 2015. At the time of writing this article, the latest stable release of React Native is 0.62.0, and it was released in March 2020.

Although relatively new, React Native has acquired a high degree of popularity. The “Stack Overflow Developer Survey 2019” report identifies it as the 8th most loved framework. Facebook, Walmart, and Bloomberg are some of the top companies that use React Native.

The popularity of React Native comes from its advantages. Some of its advantages are as follows:

  • Performance: It delivers optimal performance.
  • Cross-platform development: You can develop both Android and iOS apps with it. The reuse of code expedites development and reduces costs.
  • UI design: React Native enables you to design simple and responsive UI for your mobile app.
  • 3rd party plugins: This framework supports 3rd party plugins.
  • Developer community: A vibrant community of developers support React Native.

Why React Native is fundamentally different from earlier hybrid frameworks

Are you wondering whether React Native is just another of those hybrid frameworks like Ionic or Cordova? It’s not! React Native is fundamentally different from these earlier hybrid frameworks.

React Native is very close to native. Consider the following aspects as described on the React Native website:

  • Access to many native platforms features: The primitives of React Native render to native platform UI. This means that your React Native app will use many native platform APIs as native apps would do.
  • Near-native user experience: React Native provides several native components, and these are platform agnostic.
  • The ease of accessing native APIs: React Native uses a declarative UI paradigm. This enables React Native to interact easily with native platform APIs since React Native wraps existing native code.

Due to these factors, React Native offers many more advantages compared to those earlier hybrid frameworks. We now review them.

#android app #frontend #ios app #mobile app development #benefits of react native #is react native good for mobile app development #native vs #pros and cons of react native #react mobile development #react native development #react native experience #react native framework #react native ios vs android #react native pros and cons #react native vs android #react native vs native #react native vs native performance #react vs native #why react native #why use react native

Dylan  Iqbal

Dylan Iqbal

1561523460

Matplotlib Cheat Sheet: Plotting in Python

This Matplotlib cheat sheet introduces you to the basics that you need to plot your data with Python and includes code samples.

Data visualization and storytelling with your data are essential skills that every data scientist needs to communicate insights gained from analyses effectively to any audience out there. 

For most beginners, the first package that they use to get in touch with data visualization and storytelling is, naturally, Matplotlib: it is a Python 2D plotting library that enables users to make publication-quality figures. But, what might be even more convincing is the fact that other packages, such as Pandas, intend to build more plotting integration with Matplotlib as time goes on.

However, what might slow down beginners is the fact that this package is pretty extensive. There is so much that you can do with it and it might be hard to still keep a structure when you're learning how to work with Matplotlib.   

DataCamp has created a Matplotlib cheat sheet for those who might already know how to use the package to their advantage to make beautiful plots in Python, but that still want to keep a one-page reference handy. Of course, for those who don't know how to work with Matplotlib, this might be the extra push be convinced and to finally get started with data visualization in Python. 

You'll see that this cheat sheet presents you with the six basic steps that you can go through to make beautiful plots. 

Check out the infographic by clicking on the button below:

Python Matplotlib cheat sheet

With this handy reference, you'll familiarize yourself in no time with the basics of Matplotlib: you'll learn how you can prepare your data, create a new plot, use some basic plotting routines to your advantage, add customizations to your plots, and save, show and close the plots that you make.

What might have looked difficult before will definitely be more clear once you start using this cheat sheet! Use it in combination with the Matplotlib Gallery, the documentation.

Matplotlib 

Matplotlib is a Python 2D plotting library which produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms.

Prepare the Data 

1D Data 

>>> import numpy as np
>>> x = np.linspace(0, 10, 100)
>>> y = np.cos(x)
>>> z = np.sin(x)

2D Data or Images 

>>> data = 2 * np.random.random((10, 10))
>>> data2 = 3 * np.random.random((10, 10))
>>> Y, X = np.mgrid[-3:3:100j, -3:3:100j]
>>> U = 1 X** 2 + Y
>>> V = 1 + X Y**2
>>> from matplotlib.cbook import get_sample_data
>>> img = np.load(get_sample_data('axes_grid/bivariate_normal.npy'))

Create Plot

>>> import matplotlib.pyplot as plt

Figure 

>>> fig = plt.figure()
>>> fig2 = plt.figure(figsize=plt.figaspect(2.0))

Axes 

>>> fig.add_axes()
>>> ax1 = fig.add_subplot(221) #row-col-num
>>> ax3 = fig.add_subplot(212)
>>> fig3, axes = plt.subplots(nrows=2,ncols=2)
>>> fig4, axes2 = plt.subplots(ncols=3)

Save Plot 

>>> plt.savefig('foo.png') #Save figures
>>> plt.savefig('foo.png',  transparent=True) #Save transparent figures

Show Plot

>>> plt.show()

Plotting Routines 

1D Data 

>>> fig, ax = plt.subplots()
>>> lines = ax.plot(x,y) #Draw points with lines or markers connecting them
>>> ax.scatter(x,y) #Draw unconnected points, scaled or colored
>>> axes[0,0].bar([1,2,3],[3,4,5]) #Plot vertical rectangles (constant width)
>>> axes[1,0].barh([0.5,1,2.5],[0,1,2]) #Plot horiontal rectangles (constant height)
>>> axes[1,1].axhline(0.45) #Draw a horizontal line across axes
>>> axes[0,1].axvline(0.65) #Draw a vertical line across axes
>>> ax.fill(x,y,color='blue') #Draw filled polygons
>>> ax.fill_between(x,y,color='yellow') #Fill between y values and 0

2D Data 

>>> fig, ax = plt.subplots()
>>> im = ax.imshow(img, #Colormapped or RGB arrays
      cmap= 'gist_earth', 
      interpolation= 'nearest',
      vmin=-2,
      vmax=2)
>>> axes2[0].pcolor(data2) #Pseudocolor plot of 2D array
>>> axes2[0].pcolormesh(data) #Pseudocolor plot of 2D array
>>> CS = plt.contour(Y,X,U) #Plot contours
>>> axes2[2].contourf(data1) #Plot filled contours
>>> axes2[2]= ax.clabel(CS) #Label a contour plot

Vector Fields 

>>> axes[0,1].arrow(0,0,0.5,0.5) #Add an arrow to the axes
>>> axes[1,1].quiver(y,z) #Plot a 2D field of arrows
>>> axes[0,1].streamplot(X,Y,U,V) #Plot a 2D field of arrows

Data Distributions 

>>> ax1.hist(y) #Plot a histogram
>>> ax3.boxplot(y) #Make a box and whisker plot
>>> ax3.violinplot(z)  #Make a violin plot

Plot Anatomy & Workflow 

Plot Anatomy 

 y-axis      

                           x-axis 

Workflow 

The basic steps to creating plots with matplotlib are:

1 Prepare Data
2 Create Plot
3 Plot
4 Customized Plot
5 Save Plot
6 Show Plot

>>> import matplotlib.pyplot as plt
>>> x = [1,2,3,4]  #Step 1
>>> y = [10,20,25,30] 
>>> fig = plt.figure() #Step 2
>>> ax = fig.add_subplot(111) #Step 3
>>> ax.plot(x, y, color= 'lightblue', linewidth=3)  #Step 3, 4
>>> ax.scatter([2,4,6],
          [5,15,25],
          color= 'darkgreen',
          marker= '^' )
>>> ax.set_xlim(1, 6.5)
>>> plt.savefig('foo.png' ) #Step 5
>>> plt.show() #Step 6

Close and Clear 

>>> plt.cla()  #Clear an axis
>>> plt.clf(). #Clear the entire figure
>>> plt.close(). #Close a window

Plotting Customize Plot 

Colors, Color Bars & Color Maps 

>>> plt.plot(x, x, x, x**2, x, x** 3)
>>> ax.plot(x, y, alpha = 0.4)
>>> ax.plot(x, y, c= 'k')
>>> fig.colorbar(im, orientation= 'horizontal')
>>> im = ax.imshow(img,
            cmap= 'seismic' )

Markers 

>>> fig, ax = plt.subplots()
>>> ax.scatter(x,y,marker= ".")
>>> ax.plot(x,y,marker= "o")

Linestyles 

>>> plt.plot(x,y,linewidth=4.0)
>>> plt.plot(x,y,ls= 'solid') 
>>> plt.plot(x,y,ls= '--') 
>>> plt.plot(x,y,'--' ,x**2,y**2,'-.' ) 
>>> plt.setp(lines,color= 'r',linewidth=4.0)

Text & Annotations 

>>> ax.text(1,
           -2.1, 
           'Example Graph', 
            style= 'italic' )
>>> ax.annotate("Sine", 
xy=(8, 0),
xycoords= 'data', 
xytext=(10.5, 0),
textcoords= 'data', 
arrowprops=dict(arrowstyle= "->", 
connectionstyle="arc3"),)

Mathtext 

>>> plt.title(r '$sigma_i=15$', fontsize=20)

Limits, Legends and Layouts 

Limits & Autoscaling 

>>> ax.margins(x=0.0,y=0.1) #Add padding to a plot
>>> ax.axis('equal')  #Set the aspect ratio of the plot to 1
>>> ax.set(xlim=[0,10.5],ylim=[-1.5,1.5])  #Set limits for x-and y-axis
>>> ax.set_xlim(0,10.5) #Set limits for x-axis

Legends 

>>> ax.set(title= 'An Example Axes',  #Set a title and x-and y-axis labels
            ylabel= 'Y-Axis', 
            xlabel= 'X-Axis')
>>> ax.legend(loc= 'best')  #No overlapping plot elements

Ticks 

>>> ax.xaxis.set(ticks=range(1,5),  #Manually set x-ticks
             ticklabels=[3,100, 12,"foo" ])
>>> ax.tick_params(axis= 'y', #Make y-ticks longer and go in and out
             direction= 'inout', 
              length=10)

Subplot Spacing 

>>> fig3.subplots_adjust(wspace=0.5,   #Adjust the spacing between subplots
             hspace=0.3,
             left=0.125,
             right=0.9,
             top=0.9,
             bottom=0.1)
>>> fig.tight_layout() #Fit subplot(s) in to the figure area

Axis Spines 

>>> ax1.spines[ 'top'].set_visible(False) #Make the top axis line for a plot invisible
>>> ax1.spines['bottom' ].set_position(( 'outward',10))  #Move the bottom axis line outward

Have this Cheat Sheet at your fingertips

Original article source at https://www.datacamp.com

#matplotlib #cheatsheet #python

Garry Taylor

Garry Taylor

1653464648

Python Data Visualization: Bokeh Cheat Sheet

A handy cheat sheet for interactive plotting and statistical charts with Bokeh.

Bokeh distinguishes itself from other Python visualization libraries such as Matplotlib or Seaborn in the fact that it is an interactive visualization library that is ideal for anyone who would like to quickly and easily create interactive plots, dashboards, and data applications. 

Bokeh is also known for enabling high-performance visual presentation of large data sets in modern web browsers. 

For data scientists, Bokeh is the ideal tool to build statistical charts quickly and easily; But there are also other advantages, such as the various output options and the fact that you can embed your visualizations in applications. And let's not forget that the wide variety of visualization customization options makes this Python library an indispensable tool for your data science toolbox.

Now, DataCamp has created a Bokeh cheat sheet for those who have already taken the course and that still want a handy one-page reference or for those who need an extra push to get started.

In short, you'll see that this cheat sheet not only presents you with the five steps that you can go through to make beautiful plots but will also introduce you to the basics of statistical charts. 

Python Bokeh Cheat Sheet

In no time, this Bokeh cheat sheet will make you familiar with how you can prepare your data, create a new plot, add renderers for your data with custom visualizations, output your plot and save or show it. And the creation of basic statistical charts will hold no secrets for you any longer. 

Boost your Python data visualizations now with the help of Bokeh! :)


Plotting With Bokeh

The Python interactive visualization library Bokeh enables high-performance visual presentation of large datasets in modern web browsers.

Bokeh's mid-level general-purpose bokeh. plotting interface is centered around two main components: data and glyphs.

The basic steps to creating plots with the bokeh. plotting interface are:

  1. Prepare some data (Python lists, NumPy arrays, Pandas DataFrames and other sequences of values)
  2. Create a new plot
  3. Add renderers for your data, with visual customizations
  4. Specify where to generate the output
  5. Show or save the results
>>> from bokeh.plotting import figure
>>> from bokeh.io import output_file, show
>>> x = [1, 2, 3, 4, 5] #Step 1
>>> y = [6, 7, 2, 4, 5]
>>> p = figure(title="simple line example", #Step 2
x_axis_label='x',
y_axis_label='y')
>>> p.line(x, y, legend="Temp.", line_width=2) #Step 3
>>> output_file("lines.html") #Step 4
>>> show(p) #Step 5

1. Data 

Under the hood, your data is converted to Column Data Sources. You can also do this manually:

>>> import numpy as np
>>> import pandas as pd
>>> df = pd.OataFrame(np.array([[33.9,4,65, 'US'], [32.4, 4, 66, 'Asia'], [21.4, 4, 109, 'Europe']]),
                     columns= ['mpg', 'cyl',   'hp',   'origin'],
                      index=['Toyota', 'Fiat', 'Volvo'])


>>> from bokeh.models import ColumnOataSource
>>> cds_df = ColumnOataSource(df)

2. Plotting 

>>> from bokeh.plotting import figure
>>>p1= figure(plot_width=300, tools='pan,box_zoom')
>>> p2 = figure(plot_width=300, plot_height=300,
x_range=(0, 8), y_range=(0, 8))
>>> p3 = figure()

3. Renderers & Visual Customizations 

Glyphs 

Scatter Markers 
Bokeh Scatter Markers

>>> p1.circle(np.array([1,2,3]), np.array([3,2,1]), fill_color='white')
>>> p2.square(np.array([1.5,3.5,5.5]), [1,4,3],
color='blue', size=1)

Line Glyphs 

Bokeh Line Glyphs

>>> pl.line([1,2,3,4], [3,4,5,6], line_width=2)
>>> p2.multi_line(pd.DataFrame([[1,2,3],[5,6,7]]),
pd.DataFrame([[3,4,5],[3,2,1]]),
color="blue")

Customized Glyphs

Selection and Non-Selection Glyphs 

Selection Glyphs

>>> p = figure(tools='box_select')
>>> p. circle ('mpg', 'cyl', source=cds_df,
selection_color='red',
nonselection_alpha=0.1)

Hover Glyphs

Hover Glyphs

>>> from bokeh.models import HoverTool
>>>hover= HoverTool(tooltips=None, mode='vline')
>>> p3.add_tools(hover)

Color Mapping 

Bokeh Colormapping Glyphs

>>> from bokeh.models import CategoricalColorMapper
>>> color_mapper = CategoricalColorMapper(
             factors= ['US', 'Asia', 'Europe'],
             palette= ['blue', 'red', 'green'])
>>>  p3. circle ('mpg', 'cyl', source=cds_df,
            color=dict(field='origin',
                 transform=color_mapper), legend='Origin')

4. Output & Export 

Notebook

>>> from bokeh.io import output_notebook, show
>>> output_notebook()

HTML 

Standalone HTML 

>>> from bokeh.embed import file_html
>>> from bokeh.resources import CON
>>> html = file_html(p, CON, "my_plot")

>>> from  bokeh.io  import  output_file,  show
>>> output_file('my_bar_chart.html',  mode='cdn')

Components

>>> from bokeh.embed import components
>>> script, div= components(p)

PNG

>>> from bokeh.io import export_png
>>> export_png(p, filename="plot.png")

SVG 

>>> from bokeh.io import export_svgs
>>> p. output_backend = "svg"
>>> export_svgs(p,filename="plot.svg")

Legend Location 

Inside Plot Area 

>>> p.legend.location = 'bottom left'

Outside Plot Area 

>>> from bokeh.models import Legend
>>> r1 = p2.asterisk(np.array([1,2,3]), np.array([3,2,1])
>>> r2 = p2.line([1,2,3,4], [3,4,5,6])
>>> legend = Legend(items=[("One" ,[p1, r1]),("Two",[r2])], location=(0, -30))
>>> p.add_layout(legend, 'right')

Legend Background & Border 

>>> p.legend. border_line_color = "navy"
>>> p.legend.background_fill_color = "white"

Legend Orientation 

>>> p.legend.orientation = "horizontal"
>>> p.legend.orientation = "vertical"

Rows & Columns Layout

Rows

>>> from bokeh.layouts import row
>>>layout= row(p1,p2,p3)

Columns

>>> from bokeh.layouts import columns
>>>layout= column(p1,p2,p3)

Nesting Rows & Columns 

>>>layout= row(column(p1,p2), p3)

Grid Layout 

>>> from bokeh.layouts import gridplot
>>> rowl = [p1,p2]
>>> row2 = [p3]
>>> layout = gridplot([[p1, p2],[p3]])

Tabbed Layout 

>>> from bokeh.models.widgets import Panel, Tabs
>>> tab1 = Panel(child=p1, title="tab1")
>>> tab2 = Panel(child=p2, title="tab2")
>>> layout = Tabs(tabs=[tab1, tab2])

Linked Plots

Linked Axes 

Linked Axes
>>> p2.x_range = p1.x_range
>>> p2.y_range = p1.y_range

Linked Brushing 

>>> p4 = figure(plot_width = 100, tools='box_select,lasso_select')
>>> p4.circle('mpg', 'cyl' , source=cds_df)
>>> p5 = figure(plot_width = 200, tools='box_select,lasso_select')
>>> p5.circle('mpg', 'hp', source=cds df)
>>>layout= row(p4,p5)

5. Show or Save Your Plots  

>>> show(p1)
>>> show(layout)
>>> save(p1)

Have this Cheat Sheet at your fingertips

Original article source at https://www.datacamp.com

#python #datavisualization #bokeh #cheatsheet

Mathew Rini

1615544450

How to Select and Hire the Best React JS and React Native Developers?

Since March 2020 reached 556 million monthly downloads have increased, It shows that React JS has been steadily growing. React.js also provides a desirable amount of pliancy and efficiency for developing innovative solutions with interactive user interfaces. It’s no surprise that an increasing number of businesses are adopting this technology. How do you select and recruit React.js developers who will propel your project forward? How much does a React developer make? We’ll bring you here all the details you need.

What is React.js?

Facebook built and maintains React.js, an open-source JavaScript library for designing development tools. React.js is used to create single-page applications (SPAs) that can be used in conjunction with React Native to develop native cross-platform apps.

React vs React Native

  • React Native is a platform that uses a collection of mobile-specific components provided by the React kit, while React.js is a JavaScript-based library.
  • React.js and React Native have similar syntax and workflows, but their implementation is quite different.
  • React Native is designed to create native mobile apps that are distinct from those created in Objective-C or Java. React, on the other hand, can be used to develop web apps, hybrid and mobile & desktop applications.
  • React Native, in essence, takes the same conceptual UI cornerstones as standard iOS and Android apps and assembles them using React.js syntax to create a rich mobile experience.

What is the Average React Developer Salary?

In the United States, the average React developer salary is $94,205 a year, or $30-$48 per hour, This is one of the highest among JavaScript developers. The starting salary for junior React.js developers is $60,510 per year, rising to $112,480 for senior roles.

* React.js Developer Salary by Country

  • United States- $120,000
  • Canada - $110,000
  • United Kingdom - $71,820
  • The Netherlands $49,095
  • Spain - $35,423.00
  • France - $44,284
  • Ukraine - $28,990
  • India - $9,843
  • Sweden - $55,173
  • Singapore - $43,801

In context of software developer wage rates, the United States continues to lead. In high-tech cities like San Francisco and New York, average React developer salaries will hit $98K and $114per year, overall.

However, the need for React.js and React Native developer is outpacing local labour markets. As a result, many businesses have difficulty locating and recruiting them locally.

It’s no surprise that for US and European companies looking for professional and budget engineers, offshore regions like India are becoming especially interesting. This area has a large number of app development companies, a good rate with quality, and a good pool of React.js front-end developers.

As per Linkedin, the country’s IT industry employs over a million React specialists. Furthermore, for the same or less money than hiring a React.js programmer locally, you may recruit someone with much expertise and a broader technical stack.

How to Hire React.js Developers?

  • Conduct thorough candidate research, including portfolios and areas of expertise.
  • Before you sit down with your interviewing panel, do some homework.
  • Examine the final outcome and hire the ideal candidate.

Why is React.js Popular?

React is a very strong framework. React.js makes use of a powerful synchronization method known as Virtual DOM, which compares the current page architecture to the expected page architecture and updates the appropriate components as long as the user input.

React is scalable. it utilises a single language, For server-client side, and mobile platform.

React is steady.React.js is completely adaptable, which means it seldom, if ever, updates the user interface. This enables legacy projects to be updated to the most new edition of React.js without having to change the codebase or make a few small changes.

React is adaptable. It can be conveniently paired with various state administrators (e.g., Redux, Flux, Alt or Reflux) and can be used to implement a number of architectural patterns.

Is there a market for React.js programmers?
The need for React.js developers is rising at an unparalleled rate. React.js is currently used by over one million websites around the world. React is used by Fortune 400+ businesses and popular companies such as Facebook, Twitter, Glassdoor and Cloudflare.

Final thoughts:

As you’ve seen, locating and Hire React js Developer and Hire React Native developer is a difficult challenge. You will have less challenges selecting the correct fit for your projects if you identify growing offshore locations (e.g. India) and take into consideration the details above.

If you want to make this process easier, You can visit our website for more, or else to write a email, we’ll help you to finding top rated React.js and React Native developers easier and with strives to create this operation

#hire-react-js-developer #hire-react-native-developer #react #react-native #react-js #hire-react-js-programmer

Franz  Becker

Franz Becker

1651604400

React Starter Kit: Build Web Apps with React, Relay and GraphQL.

React Starter Kit — "isomorphic" web app boilerplate   

React Starter Kit is an opinionated boilerplate for web development built on top of Node.js, Express, GraphQL and React, containing modern web development tools such as Webpack, Babel and Browsersync. Helping you to stay productive following the best practices. A solid starting point for both professionals and newcomers to the industry.

See getting started guide, demo, docs, roadmap  |  Join #react-starter-kit chat room on Gitter  |  Visit our sponsors:

 

Hiring

Getting Started

Customization

The master branch of React Starter Kit doesn't include a Flux implementation or any other advanced integrations. Nevertheless, we have some integrations available to you in feature branches that you can use either as a reference or merge into your project:

You can see status of most reasonable merge combination as PRs labeled as TRACKING

If you think that any of these features should be on master, or vice versa, some features should removed from the master branch, please let us know. We love your feedback!

Comparison

 

React Starter Kit

React Static Boilerplate

ASP.NET Core Starter Kit

App typeIsomorphic (universal)Single-page applicationSingle-page application
Frontend
LanguageJavaScript (ES2015+, JSX)JavaScript (ES2015+, JSX)JavaScript (ES2015+, JSX)
LibrariesReact, History, Universal RouterReact, History, ReduxReact, History, Redux
RoutesImperative (functional)DeclarativeDeclarative, cross-stack
Backend
LanguageJavaScript (ES2015+, JSX)n/aC#, F#
LibrariesNode.js, Express, Sequelize,
GraphQL
n/aASP.NET Core, EF Core,
ASP.NET Identity
SSRYesn/an/a
Data APIGraphQLn/aWeb API

Backers

♥ React Starter Kit? Help us keep it alive by donating funds to cover project expenses via OpenCollective or Bountysource!

lehneres Tarkan Anlar Morten Olsen Adam David Ernst Zane Hitchcox  

How to Contribute

Anyone and everyone is welcome to contribute to this project. The best way to start is by checking our open issues, submit a new issue or feature request, participate in discussions, upvote or downvote the issues you like or dislike, send pull requests.

Learn More

Related Projects

  • GraphQL Starter Kit — Boilerplate for building data APIs with Node.js, JavaScript (via Babel) and GraphQL
  • Membership Database — SQL schema boilerplate for user accounts, profiles, roles, and auth claims
  • Babel Starter Kit — Boilerplate for authoring JavaScript/React.js libraries

Support

License

Copyright © 2014-present Kriasoft, LLC. This source code is licensed under the MIT license found in the LICENSE.txt file. The documentation to the project is licensed under the CC BY-SA 4.0 license.


Author: kriasoft
Source Code: https://github.com/kriasoft/react-starter-kit
License: MIT License

#graphql #react