Alverta Hammes

Alverta Hammes

1630084020

Tutorial to VR Eye Tracking Works

Eye-tracking uses virtual reality technology and involves wearing a high-quality VR headset with built-in eye tracking. Watch our video to learn more.

▶ TIME CODES:
00:00 Intro
00:37 What is VR eye-tracking?
02:00 Why employ eye-tracking In VR?
04:28 How close is eye-tracking to mind reading?
04:59 First VR headset with eye-tracking
​05:40 Contact Jelvix

#developer 

What is GEEK

Buddha Community

Tutorial to VR Eye Tracking Works
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

Autumn Blick

Autumn Blick

1596584126

R Tutorial: Better Blog Post Analysis with googleAnalyticsR

In my previous role as a marketing data analyst for a blogging company, one of my most important tasks was to track how blog posts performed.

On the surface, it’s a fairly straightforward goal. With Google Analytics, you can quickly get just about any metric you need for your blog posts, for any date range.

But when it comes to comparing blog post performance, things get a bit trickier.

For example, let’s say we want to compare the performance of the blog posts we published on the Dataquest blog in June (using the month of June as our date range).

But wait… two blog posts with more than 1,000 pageviews were published earlier in the month, And the two with fewer than 500 pageviews were published at the end of the month. That’s hardly a fair comparison!

My first solution to this problem was to look up each post individually, so that I could make an even comparison of how each post performed in their first day, first week, first month, etc.

However, that required a lot of manual copy-and-paste work, which was extremely tedious if I wanted to compare more than a few posts, date ranges, or metrics at a time.

But then, I learned R, and realized that there was a much better way.

In this post, we’ll walk through how it’s done, so you can do my better blog post analysis for yourself!

What we’ll need

To complete this tutorial, you’ll need basic knowledge of R syntax and the tidyverse, and access to a Google Analytics account.

Not yet familiar with the basics of R? We can help with that! Our interactive online courses teach you R from scratch, with no prior programming experience required. Sign up and start today!

You’ll also need the dyplr, lubridate, and stringr packages installed — which, as a reminder, you can do with the install.packages() command.

Finally, you will need a CSV of the blog posts you want to analyze. Here’s what’s in my dataset:

post_url: the page path of the blog post

post_date: the date the post was published (formatted m/d/yy)

category: the blog category the post was published in (optional)

title: the title of the blog post (optional)

Depending on your content management system, there may be a way for you to automate gathering this data — but that’s out of the scope of this tutorial!

For this tutorial, we’ll use a manually-gathered dataset of the past ten Dataquest blog posts.

#data science tutorials #promote #r #r tutorial #r tutorials #rstats #tutorial #tutorials

Beth Cooper

Beth Cooper

1659694200

Easy Activity Tracking for Models, Similar to Github's Public Activity

PublicActivity

public_activity provides easy activity tracking for your ActiveRecord, Mongoid 3 and MongoMapper models in Rails 3 and 4.

Simply put: it can record what happens in your application and gives you the ability to present those recorded activities to users - in a similar way to how GitHub does it.

!! WARNING: README for unreleased version below. !!

You probably don't want to read the docs for this unreleased version 2.0.

For the stable 1.5.X readme see: https://github.com/chaps-io/public_activity/blob/1-5-stable/README.md

About

Here is a simple example showing what this gem is about:

Example usage

Tutorials

Screencast

Ryan Bates made a great screencast describing how to integrate Public Activity.

Tutorial

A great step-by-step guide on implementing activity feeds using public_activity by Ilya Bodrov.

Online demo

You can see an actual application using this gem here: http://public-activity-example.herokuapp.com/feed

The source code of the demo is hosted here: https://github.com/pokonski/activity_blog

Setup

Gem installation

You can install public_activity as you would any other gem:

gem install public_activity

or in your Gemfile:

gem 'public_activity'

Database setup

By default public_activity uses Active Record. If you want to use Mongoid or MongoMapper as your backend, create an initializer file in your Rails application with the corresponding code inside:

For Mongoid:

# config/initializers/public_activity.rb
PublicActivity.configure do |config|
  config.orm = :mongoid
end

For MongoMapper:

# config/initializers/public_activity.rb
PublicActivity.configure do |config|
  config.orm = :mongo_mapper
end

(ActiveRecord only) Create migration for activities and migrate the database (in your Rails project):

rails g public_activity:migration
rake db:migrate

Model configuration

Include PublicActivity::Model and add tracked to the model you want to keep track of:

For ActiveRecord:

class Article < ActiveRecord::Base
  include PublicActivity::Model
  tracked
end

For Mongoid:

class Article
  include Mongoid::Document
  include PublicActivity::Model
  tracked
end

For MongoMapper:

class Article
  include MongoMapper::Document
  include PublicActivity::Model
  tracked
end

And now, by default create/update/destroy activities are recorded in activities table. This is all you need to start recording activities for basic CRUD actions.

Optional: If you don't need #tracked but still want the comfort of #create_activity, you can include only the lightweight Common module instead of Model.

Custom activities

You can trigger custom activities by setting all your required parameters and triggering create_activity on the tracked model, like this:

@article.create_activity key: 'article.commented_on', owner: current_user

See this entry http://rubydoc.info/gems/public_activity/PublicActivity/Common:create_activity for more details.

Displaying activities

To display them you simply query the PublicActivity::Activity model:

# notifications_controller.rb
def index
  @activities = PublicActivity::Activity.all
end

And in your views:

<%= render_activities(@activities) %>

Note: render_activities is an alias for render_activity and does the same.

Layouts

You can also pass options to both activity#render and #render_activity methods, which are passed deeper to the internally used render_partial method. A useful example would be to render activities wrapped in layout, which shares common elements of an activity, like a timestamp, owner's avatar etc:

<%= render_activities(@activities, layout: :activity) %>

The activity will be wrapped with the app/views/layouts/_activity.html.erb layout, in the above example.

Important: please note that layouts for activities are also partials. Hence the _ prefix.

Locals

Sometimes, it's desirable to pass additional local variables to partials. It can be done this way:

<%= render_activity(@activity, locals: {friends: current_user.friends}) %>

Note: Before 1.4.0, one could pass variables directly to the options hash for #render_activity and access it from activity parameters. This functionality is retained in 1.4.0 and later, but the :locals method is preferred, since it prevents bugs from shadowing variables from activity parameters in the database.

Activity views

public_activity looks for views in app/views/public_activity.

For example, if you have an activity with :key set to "activity.user.changed_avatar", the gem will look for a partial in app/views/public_activity/user/_changed_avatar.html.(|erb|haml|slim|something_else).

Hint: the "activity." prefix in :key is completely optional and kept for backwards compatibility, you can skip it in new projects.

If you would like to fallback to a partial, you can utilize the fallback parameter to specify the path of a partial to use when one is missing:

<%= render_activity(@activity, fallback: 'default') %>

When used in this manner, if a partial with the specified :key cannot be located it will use the partial defined in the fallback instead. In the example above this would resolve to public_activity/_default.html.(|erb|haml|slim|something_else).

If a view file does not exist then ActionView::MisingTemplate will be raised. If you wish to fallback to the old behaviour and use an i18n based translation in this situation you can specify a :fallback parameter of text to fallback to this mechanism like such:

<%= render_activity(@activity, fallback: :text) %>

i18n

Translations are used by the #text method, to which you can pass additional options in form of a hash. #render method uses translations when view templates have not been provided. You can render pure i18n strings by passing {display: :i18n} to #render_activity or #render.

Translations should be put in your locale .yml files. To render pure strings from I18n Example structure:

activity:
  article:
    create: 'Article has been created'
    update: 'Someone has edited the article'
    destroy: 'Some user removed an article!'

This structure is valid for activities with keys "activity.article.create" or "article.create". As mentioned before, "activity." part of the key is optional.

Testing

For RSpec you can first disable public_activity and add require helper methods in the rails_helper.rb with:

#rails_helper.rb
require 'public_activity/testing'

PublicActivity.enabled = false

In your specs you can then blockwise decide whether to turn public_activity on or off.

# file_spec.rb
PublicActivity.with_tracking do
  # your test code goes here
end

PublicActivity.without_tracking do
  # your test code goes here
end

Documentation

For more documentation go here

Common examples

Set the Activity's owner to current_user by default

You can set up a default value for :owner by doing this:

  1. Include PublicActivity::StoreController in your ApplicationController like this:
class ApplicationController < ActionController::Base
  include PublicActivity::StoreController
end
  1. Use Proc in :owner attribute for tracked class method in your desired model. For example:
class Article < ActiveRecord::Base
  tracked owner: Proc.new{ |controller, model| controller.current_user }
end

Note: current_user applies to Devise, if you are using a different authentication gem or your own code, change the current_user to a method you use.

Disable tracking for a class or globally

If you need to disable tracking temporarily, for example in tests or db/seeds.rb then you can use PublicActivity.enabled= attribute like below:

# Disable p_a globally
PublicActivity.enabled = false

# Perform some operations that would normally be tracked by p_a:
Article.create(title: 'New article')

# Switch it back on
PublicActivity.enabled = true

You can also disable public_activity for a specific class:

# Disable p_a for Article class
Article.public_activity_off

# p_a will not do anything here:
@article = Article.create(title: 'New article')

# But will be enabled for other classes:
# (creation of the comment will be recorded if you are tracking the Comment class)
@article.comments.create(body: 'some comment!')

# Enable it again for Article:
Article.public_activity_on

Create custom activities

Besides standard, automatic activities created on CRUD actions on your model (deactivatable), you can post your own activities that can be triggered without modifying the tracked model. There are a few ways to do this, as PublicActivity gives three tiers of options to be set.

Instant options

Because every activity needs a key (otherwise: NoKeyProvided is raised), the shortest and minimal way to post an activity is:

@user.create_activity :mood_changed
# the key of the action will be user.mood_changed
@user.create_activity action: :mood_changed # this is exactly the same as above

Besides assigning your key (which is obvious from the code), it will take global options from User class (given in #tracked method during class definition) and overwrite them with instance options (set on @user by #activity method). You can read more about options and how PublicActivity inherits them for you here.

Note the action parameter builds the key like this: "#{model_name}.#{action}". You can read further on options for #create_activity here.

To provide more options, you can do:

@user.create_activity action: 'poke', parameters: {reason: 'bored'}, recipient: @friend, owner: current_user

In this example, we have provided all the things we could for a standard Activity.

Use custom fields on Activity

Besides the few fields that every Activity has (key, owner, recipient, trackable, parameters), you can also set custom fields. This could be very beneficial, as parameters are a serialized hash, which cannot be queried easily from the database. That being said, use custom fields when you know that you will set them very often and search by them (don't forget database indexes :) ).

Set owner and recipient based on associations

class Comment < ActiveRecord::Base
  include PublicActivity::Model
  tracked owner: :commenter, recipient: :commentee

  belongs_to :commenter, :class_name => "User"
  belongs_to :commentee, :class_name => "User"
end

Resolve parameters from a Symbol or Proc

class Post < ActiveRecord::Base
  include PublicActivity::Model
  tracked only: [:update], parameters: :tracked_values
  
  def tracked_values
   {}.tap do |hash|
     hash[:tags] = tags if tags_changed?
   end
  end
end

Setup

Skip this step if you are using ActiveRecord in Rails 4 or Mongoid

The first step is similar in every ORM available (except mongoid):

PublicActivity::Activity.class_eval do
  attr_accessible :custom_field
end

place this code under config/initializers/public_activity.rb, you have to create it first.

To be able to assign to that field, we need to move it to the mass assignment sanitizer's whitelist.

Migration

If you're using ActiveRecord, you will also need to provide a migration to add the actual field to the Activity. Taken from our tests:

class AddCustomFieldToActivities < ActiveRecord::Migration
  def change
    change_table :activities do |t|
      t.string :custom_field
    end
  end
end

Assigning custom fields

Assigning is done by the same methods that you use for normal parameters: #tracked, #create_activity. You can just pass the name of your custom variable and assign its value. Even better, you can pass it to #tracked to tell us how to harvest your data for custom fields so we can do that for you.

class Article < ActiveRecord::Base
  include PublicActivity::Model
  tracked custom_field: proc {|controller, model| controller.some_helper }
end

Help

If you need help with using public_activity please visit our discussion group and ask a question there:

https://groups.google.com/forum/?fromgroups#!forum/public-activity

Please do not ask general questions in the Github Issues.

Author: public-activity
Source code: https://github.com/public-activity/public_activity
License: MIT license

#ruby  #ruby-on-rails