OpenShot Video Editor Tutorial - Designed for Beginners

OpenShot Video Editor is one of the best FREE video editors. Not only is OpenShot free but it is extremely easy to use. This video tutorial will show you how to use the OpenShot Video Editor from start to finish. Learn OpenShot from all the mini-lessons listed below.

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OpenShot Video Editor Tutorial - Designed for Beginners
Jeromy  Lowe

Jeromy Lowe


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

Willie  Beier

Willie Beier


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

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:


Once the package is installed, load it to memory:


Now that tidyverse is loaded into memory, take a “glimpse” of the Brooklyn dataset:

## Observations: 20,185
## Variables: 21
## $ BOROUGH <chr> "3", "3", "3", "3", "3", "3", "…
## $ `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

Landscapes Website Design | Nature Landscapes Website Designer

Most landscapers think of their website as an online brochure. In reality of consumers have admitted to judging a company’s credibility based on their web design, making your website a virtual sales rep capable of generating massive amounts of leads and sales. If your website isn’t actively increasing leads and new landscaping contracts, it may be time for a redesign.

DataIT Solutions specializes in landscape website designing that are not only beautiful but also rank well in search engine results and convert your visitors into customers. We’ve specialized in the landscaping industry for over 10 years, and we look at your business from an owner’s perspective.

Why use our Landscapes for your landscape design?

  • Superior experience
  • Friendly personal service
  • Choice of design layout
  • Budget sensitive designs
  • Impartial product choice and advice
  • Planting and lighting designs

Want to talk about your website?
If you are a gardener or have a gardening company please do not hesitate to contact us for a quote.
Need help with your website?
Get in touch

#nature landscapes website design #landscapes website design #website design #website designing #website designer #designer

The  NineHertz

The NineHertz


How to Hire Video Game Developers For Video Game Development Company?

The gaming industry has taken a boom in the last few years. If we talk about numbers, according to NewZoo, the worth of the video gaming industry was $159.3 Billion in 2020. Video games are not just something for fun now, players and users expect much more from video game developers. Creating such products that just do not satisfy the player’s needs and exceed their expectations is what video game development company are thriving for.

Though kickstarting a new game-making studio is not an easy task. This business requires a team with a huge passion to create games and earn money from these video games. The idea of the approach is to create such unique games that will reach millions of people in the world and gain popularity. This growth demands more professionals in this field.

This just can not be obtained by finding someone with a good CV, the whole process includes a deep dig down to grab the right talent. Read on to learn more about Mobile game developers and the process of hiring video game developers.

Read Complete Blog Here -

#Video Game Development

#Video Game developers

#Video game development studio

#Video game development services