George Koelpin

George Koelpin

1597974660

A Ghost Demo: How to Go Headless with Ghost CMS [Tutorial]

_n a rush? Skip to technical tutorial or _live demo

As a kid, didn’t you love listening to ghost stories while sitting around a campfire? Some were lame, like Casper; some were kinda’ cool, like Bloody Mary; and some were just downright weird, like Shirime (yeah… I’ll let you look that one up yourself).

All these stories had one thing in common: they connected the listeners, even if for just a brief period of time.

But today, I’m going to use my Ghost demo to teach you how to connect with your readers for a long time. And to do that, we won’t be focused on stories about ghosts (don’t worry, those will be in there too) because we’ll actually be using the ghost itself.

Ghost CMS, that is.

Plus, in keeping with the spooky vibe of this post, let’s make that a Ghost gone headless. More specifically, in this article I am going to:

But first things first, let’s get caught up on exactly what Ghost CMS is.

A brief history of Ghost CMS…

floating-ghosts

Ghost is a free, open-source platform designed with one thing in mind: minimalistic content publishing. In other words, Ghost is made for bloggers. Period. In fact, it was the brain-child of a former WordPress employee, John O’Nolan, so it’s no surprise that blogging stays fixed at this platform’s core.

O’Nolan worked with fellow coder Hannah Wolfe to create Ghost back in 2013 after getting frustrated with WordPress (and it is just me or have we been hearing that a lot in the last five years?). This probably doesn’t surprise you as Ghost is commonly touted as a sleeker WP alternative.

The overall goal was to create a platform that was simple, lean, and modern. Something with all the good parts of WordPress without being over-bloated. But the best description of Ghost comes from O’Nolan’s own words: it’s “just a blogging website.”

And it’s directly in that simplicity where Ghost’s force lies.

#go

What is GEEK

Buddha Community

A Ghost Demo: How to Go Headless with Ghost CMS [Tutorial]
Fannie Zemlak

Fannie Zemlak

1599854400

What's new in the go 1.15

Go announced Go 1.15 version on 11 Aug 2020. Highlighted updates and features include Substantial improvements to the Go linker, Improved allocation for small objects at high core counts, X.509 CommonName deprecation, GOPROXY supports skipping proxies that return errors, New embedded tzdata package, Several Core Library improvements and more.

As Go promise for maintaining backward compatibility. After upgrading to the latest Go 1.15 version, almost all existing Golang applications or programs continue to compile and run as older Golang version.

#go #golang #go 1.15 #go features #go improvement #go package #go new features

George Koelpin

George Koelpin

1597974660

A Ghost Demo: How to Go Headless with Ghost CMS [Tutorial]

_n a rush? Skip to technical tutorial or _live demo

As a kid, didn’t you love listening to ghost stories while sitting around a campfire? Some were lame, like Casper; some were kinda’ cool, like Bloody Mary; and some were just downright weird, like Shirime (yeah… I’ll let you look that one up yourself).

All these stories had one thing in common: they connected the listeners, even if for just a brief period of time.

But today, I’m going to use my Ghost demo to teach you how to connect with your readers for a long time. And to do that, we won’t be focused on stories about ghosts (don’t worry, those will be in there too) because we’ll actually be using the ghost itself.

Ghost CMS, that is.

Plus, in keeping with the spooky vibe of this post, let’s make that a Ghost gone headless. More specifically, in this article I am going to:

But first things first, let’s get caught up on exactly what Ghost CMS is.

A brief history of Ghost CMS…

floating-ghosts

Ghost is a free, open-source platform designed with one thing in mind: minimalistic content publishing. In other words, Ghost is made for bloggers. Period. In fact, it was the brain-child of a former WordPress employee, John O’Nolan, so it’s no surprise that blogging stays fixed at this platform’s core.

O’Nolan worked with fellow coder Hannah Wolfe to create Ghost back in 2013 after getting frustrated with WordPress (and it is just me or have we been hearing that a lot in the last five years?). This probably doesn’t surprise you as Ghost is commonly touted as a sleeker WP alternative.

The overall goal was to create a platform that was simple, lean, and modern. Something with all the good parts of WordPress without being over-bloated. But the best description of Ghost comes from O’Nolan’s own words: it’s “just a blogging website.”

And it’s directly in that simplicity where Ghost’s force lies.

#go

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

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