Aurelio Yost

Aurelio Yost

1639482121

Cypress Tutorial: How to install Cypress Plugin - Part III

Learn how to install a Cypress Plugin and set up environment variables.

This video is a part of our Cypress Testing Tutorial series. In this video, Chris DeSilva (@desilvadev) explains how to install and use your first Cypress plugin - Cypress Testing library that allows you to use ‘’FIND ALL’’ and ‘’FIND BY’’ commands supported by the Cypress test automation framework. The video also walks you through the benefits of the React Testing library along with demonstrating the use of JSON files for creating and setting environment variables. 🪐

By the end of this video, you will learn 🎫 -: 
🔹 What is Cypress Automation Tool?
🔹 What are Cypress plugins?
🔹 How to use Cypress JSON file?
🔹 How do I install Cypress plugin?
🔹 What is Cypress Testing Library?
🔹 How to use “FIND BY” and “FIND ALL” Cypress commands?

Ｖɪᴅᴇᴏ Ｃʜᴀᴘᴛᴇʀꜱ 👀 
➤ 00:00 Introduction
➤ 00:20 Learning Goals
➤ 01:15 How to navigate the directories of Cypress plugin
➤ 04:05 Installation of Cypress Testing Library plugin
➤ 08:50 Setting up the base URL
➤ 12:16 Setting up Environment Variables Syntax
➤ 16:30 Recap
➤ 18:34 Conclusion of Session

#cypress  #testing #json 

What is GEEK

Buddha Community

Cypress Tutorial: How to install Cypress Plugin - Part III
Aurelio Yost

Aurelio Yost

1639482121

Cypress Tutorial: How to install Cypress Plugin - Part III

Learn how to install a Cypress Plugin and set up environment variables.

This video is a part of our Cypress Testing Tutorial series. In this video, Chris DeSilva (@desilvadev) explains how to install and use your first Cypress plugin - Cypress Testing library that allows you to use ‘’FIND ALL’’ and ‘’FIND BY’’ commands supported by the Cypress test automation framework. The video also walks you through the benefits of the React Testing library along with demonstrating the use of JSON files for creating and setting environment variables. 🪐

By the end of this video, you will learn 🎫 -: 
🔹 What is Cypress Automation Tool?
🔹 What are Cypress plugins?
🔹 How to use Cypress JSON file?
🔹 How do I install Cypress plugin?
🔹 What is Cypress Testing Library?
🔹 How to use “FIND BY” and “FIND ALL” Cypress commands?

Ｖɪᴅᴇᴏ Ｃʜᴀᴘᴛᴇʀꜱ 👀 
➤ 00:00 Introduction
➤ 00:20 Learning Goals
➤ 01:15 How to navigate the directories of Cypress plugin
➤ 04:05 Installation of Cypress Testing Library plugin
➤ 08:50 Setting up the base URL
➤ 12:16 Setting up Environment Variables Syntax
➤ 16:30 Recap
➤ 18:34 Conclusion of Session

#cypress  #testing #json 

Aliyah Satterfield

Aliyah Satterfield

1591643580

Adobe XD plugin for Flutter with CodePen Tutorial

Recently Adobe XD releases a new version of the plugin that you can use to export designs directly into flutter widgets or screens. Yes, you read it right, now you can make and export your favorite design in Adobe XD and export all the design in the widget form or as a full-screen design, this can save you a lot of time required in designing.

What we will do?
I will make a simple design of a dialogue box with a card design with text over it as shown below. After you complete this exercise you can experiment with the UI. You can make your own components or import UI kits available with the Adobe XD.

#developers #flutter #adobe xd design export to flutter #adobe xd flutter code #adobe xd flutter code generator - plugin #adobe xd flutter plugin #adobe xd flutter plugin tutorial #adobe xd plugins #adobe xd to flutter #adobe xd tutorial #codepen for flutter.

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

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

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