Namani Karthik

1615797720

Selenium Tutorial for Absolute Beginners

In this tutorial we will learn about the selenium framework its advantages and disadvantages and its components

👨🏼‍💻Contents👨🏼‍💻
00:00:01​ How To Become An Automation Test Engineer | Test Automation Engineer Roles & Skills Requirements
00:15:18​ Automation Testing Life Cycle | Challenges of Manual Testing | Regression Testing Vs Retesting
00:44:58​ Software Automation Testing Basics | How does Automated Testing Tool Work?
00:59:32​ 1 - What is Selenium? Introduction to Selenium | Selenium Basics
01:23:57​ 2 - Selenium Installation On Windows | Selenium WebDriver Setup
01:42:47​ 3 - How to Write and Run a Test Case in Selenium
02:15:56​ 4 - Selenium Locators | Locators In Selenium WebDriver With Examples
03:06:11​ 5 - How to Write and Run a Test Case in Selenium

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Selenium Tutorial for Absolute Beginners
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

Ilene Jerde

Ilene Jerde

1596801420

Selenium Tool Suite - Components and Features

Selenium is an automation testing tool; it is primarily used to test websites and web applications; it is an open-source tool. With the help of Selenium, test cases can run directly in web browsers, just like a person operating the web browsers. It supports many web browsers such as Opera, Safari, Chrome, Firefox, IE, etc. There are several different sub tools to support different automation test approaches. In this article, we will learn about selenium tool suite, its components and features. So let’s start!!!

#selenium tutorials #selenium grid #selenium ide #selenium rc #selenium tool suite #selenium webdriver

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

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

Destiny Volkman

Destiny Volkman

1597503600

WebDriverIO Tutorial: Handling Alerts and Overlay In Selenium

You’d hardly find a website these days without alerts and pop-ups! The alert boxes warn you whenever you perform a wrong action or to enter details to access a website. These alert boxes stop you from performing any other browser functions till the alert is resolved. This is why it becomes important that you handle them in your Selenium test automation scripts.

In this WebDriverIO tutorial on alert handling in Selenium, I’ll show you how to handle alerts and pop-ups as well as overlay modal in WebDriverIO. I will also cover the different types of alerts you will face during automation and what are the key points you need to follow for alert handling in Selenium using WebDriverIO.

Types Of Alerts In WebDriverIO

Alerts and pop-ups are pretty common in any website development, and while performing Selenium test automation you have to handle them as well. These alerts or rather javascript alerts, are pop up that takes your attention away from the current browser and forces you to read them. You won’t be able to perform any further browser action if you don’t know how to handle the alerts, this stands true for both manual and automation.

It is important to note that you can’t identify alerts using devtools or by XPath. Also, since they can’t be handled as a window, this is why it gets a bit tricky to handle them, but don’t worry, you’d find more about this in the latter section of this WebDriverIo tutorial.

There are three types of alerts that you’d need to handle in WebDriverIO.

  1. Alert pop up
  2. Confirmation Alert
  3. Prompt pop up

Alert Pop Ups

The alert pop up or alert() method displays an alert box with just a message and ‘OK’ button. This alert used to inform the user about some information. There is only one button ‘OK’ displayed with the text of information. Here, the user has only one option to press the OK button. Below is the example of alert pop up.

JavaScript Alerts

Confirmation Alert

The confirmation alert is the second type of alert with a message, where it gives the user the option to press OK or Cancel. Here is the example of a confirmation alert.

Confirmation Alert

Prompt Pop-Up

The prompt pop up is the last alert where this used to let the user give input for the website. Here, the user can give input and press the OK button or press Cancel to avoid giving input. Below is the example of the prompt pop up.

Apart from these in-built javascript alerts, there is also one more pop up which is known as modal. The main difference between an alert and modal is that alert can not go off without requested actions e.g, OK, or Cancel. In the modal, it is made using the < div > tag by giving special CSS code. This modal can go off by clicking somewhere other than the modal.

Overlay Modal

This modal is built using the client-side framework e.g bootstrap, ReactJS. A developer can be used to display some information, pop up, and form. There is no special

Here is an example of Overlay Modal:

Modal title

Now, that you are familiar with a different kind of alert and modal available in javascript. I’ll show you more about alert handling in Selenium in this WebDriverIO tutorial.

#tutorial #performance #selenium #selenium automation #selenium automated testing #automation selenium #webdriver io