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1. Textual 7 Lightweight Irc Client V7 1 4e
2. Textual 7 Lightweight Irc Client V7 1 4.5

Last Updated on July 30, 2020 by

IRC7's chat network supports both the MSN Chat and IRC protocol. This means that scripts and other third party applications that you use to use during the days MSN Chat was still available for use will work with our network. Bring back the good memories from the old MSN Chat days on the IRC7 network.

Textual 7 is the world’s most popular application for interacting with Internet Relay Chat (IRC) chatrooms on OS X. First appearing in 2010, Textual has since evolved and matured into the top IRC client for OS X; relied on and trusted by thousands of people.

Beautiful Interface
• Textual includes two elegantly designed dark and light variants of the user interface which have been refined all the way down to the very last pixel.

Powerful
• Textual supports very powerful modern technologies such as native IPv6, the latest IRCv3 specifications, client-side certificate authentication, and much, much more in an easy to navigate, clutter free environment.

Textual 7 Lightweight Irc Client V7 1 4e

Privacy is valued
• Textual protects your privacy by leveraging widely accepted, proven technologies such as Off-the-Record Messaging (OTR) to ensure that the only people reading your conversations are those that you intend to.

Customizable
• Textual takes customization to the next level with styles created using well documented, open web standards in addition to addons built using a multitude of programming languages such as AppleScript, Objective-C, PHP, Python, Perl, Ruby, Shell, and Swift.

Textual also includes…

• Address Book for tracking when friends are online & offline
• Auto-completion for nicknames, commands, and channels
• Bold, color, italic, and underline text formatting
• Built in support for converting text emoticons to emojis
• Growl and native Notification Center support
• Inline media embedding (Show images inline with chat)
• Inline nickname colorization
• Per-channel notification controls
• Powerful ZNC integration for power users
• Powerful filters that can perform commands in response to input
• Powerful, customizable, rule-based ignore matching
• Quick navigation using fuzzy searching
• Rich array of keyboard shortcuts
• SOCKS4, SOCKS5, and HTTP proxy support (with or without SSL)
• Simple Authentication and Security Layer (SASL)
• Support for sending self-signed certificates for authentication
• Support for validating and specifying trust for untrusted certificates
• Viewing multiple channels at the same time
• iCloud support for syncing connections and preferences

…and much, much more!

See https://www.codeux.com/ for more information

What’s New
Version 7.1.6:
• Fixed hardened runtime blocking the loading of custom extensions and inline media modules.

Information

In-App Purchases

• Free Trial
• Upgrade from v6 (free)
• Upgrade from v6 $7.99
• Standard Edition $12.99

• Details:
  

2019-07-30

Elasticsearch is a distributed NoSQL document store search-engine and column-oriented database, whose fast (near real-time) reads and powerful aggregation engine make it an excellent choice as an ‘analytics database’ for R&D, production-use or both. Installation is simple, it ships with sensible default settings that allow it to work effectively out-of-the-box, and all interaction is made via a set of intuitive and extremely well documentedRESTful APIs. I’ve been using it for two years now and I am evangelical.

The elasticsearchr package implements a simple Domain-Specific Language (DSL) for indexing, deleting, querying, sorting and aggregating data in Elasticsearch, from within R. The main purpose of this package is to remove the labour involved with assembling HTTP requests to Elasticsearch’s REST APIs and processing the responses. Instead, users of this package need only send and receive data frames to Elasticsearch resources. Users needing richer functionality are encouraged to investigate the excellent elastic package from the good people at rOpenSci.

Textual 7 Lightweight Irc Client V7 1 4.5

This package is available on CRAN or from this GitHub repository. To install the latest development version from GitHub, make sure that you have the devtools package installed (this comes bundled with RStudio), and then execute the following on the R command line:

Installing Elasticsearch

Elasticsearch can be downloaded here, where the instructions for installing and starting it can also be found. OS X users (such as myself) can also make use of Homebrew to install it with the command,

And then start it by executing $ elasticsearch from within any Terminal window. Successful installation and start-up can be checked by navigating any web browser to http://localhost:9200, where the following message should greet you (give or take the cluster name that changes with every restart),

Elasticsearch 101

If you followed the installation steps above, you have just installed a single Elasticsearch ‘node’. When not testing on your laptop, Elasticsearch usually comes in clusters of nodes (usually there are at least 3). The easiest easy way to get access to a managed Elasticsearch cluster is by using the Elastic Cloud managed service provided by Elastic (note that Amazon Web Services offer something similar too). For the rest of this brief tutorial I will assuming you’re running a single node on your laptop (a great way of working with data that is too big for memory).

In Elasticsearch a ‘row’ of data is stored as a ‘document’. A document is a JSON object - for example, the first row of R’s iris dataset,

would be represented as follows using JSON,

Documents are classified into ‘types’ and stored in an ‘index’. In a crude analogy with traditional SQL databases that is often used, we would associate an index with a database instance and the document types as tables within that database. In practice this example is not accurate - it is better to think of all documents as residing in a single - possibly sparse - table (defined by the index), where the document types represent non-unique sub-sets of columns in the table. This is especially so as fields that occur in multiple document types (within the same index), must have the same exists in document type customer as well as in document type address, then 'name' will need to be a string in both. Note, that ‘types’ are being slowly phased-out and in Elasticsearch v7.x there will only be indices.

Each document is considered a ‘resource’ that has a Uniform Resource Locator (URL) associated with it. Elasticsearch URLs all have the following format: http://your_cluster:9200/your_index/your_doc_type/your_doc_id. For example, the above iris document could be living at http://localhost:9200/iris/data/1 - you could even point a web browser to this location and investigate the document’s contents.

Although Elasticsearch - like most NoSQL databases - is often referred to as being ‘schema free’, as we have already see this is not entirely correct. What is true, however, is that the schema - or ‘mapping’ as it’s called in Elasticsearch - does not need to be declared up-front (although you certainly can do this). Elasticsearch is more than capable of guessing the types of fields based on new data indexed for the first time.

For more information on any of these basic concepts take a look here

elasticsearchr: a Quick Start

elasticsearchr is a lightweight client - by this I mean that it only aims to do ‘just enough’ work to make using Elasticsearch with R easy and intuitive. You will still need to read the Elasticsearch documentation to understand how to compose queries and aggregations. What follows is a quick summary of what is possible.

Elasticsearch Data Resources

Elasticsearch resources, as defined by the URLs described above, are defined as elastic objects in elasticsearchr. For example,

Refers to documents of type ‘data’ in the ‘iris’ index located on an Elasticsearch node on my laptop. Note that: - it is possible to leave the document type empty if you need to refer to all documents in an index; and, - elastic objects can be defined even if the underling resources have yet to be brought into existence.

Indexing New Data

To index (insert) data from a data frame, use the %index% operator as follows:

In this example, the iris dataset is indexed into the ‘iris’ index and given a document type called ‘data’. Note that I have not provided any document ids here. To explicitly specify document ids there must be a column in the data frame that is labelled id, from which the document ids will be taken.

Deleting Data

Documents can be deleted in three different ways using the %delete% operator. Firstly, an entire index (including the mapping information) can be erased by referencing just the index in the resource - e.g.,

Alternatively, documents can be deleted on a type-by-type basis leaving the index and it’s mappings untouched, by referencing both the index and the document type as the resource - e.g.,

Finally, specific documents can be deleted by referencing their ids directly - e.g.,

Queries

Any type of query that Elasticsearch makes available can be defined in a query object using the native Elasticsearch JSON syntax - e.g. to match every document we could use the match_all query,

To execute this query we use the %search% operator on the appropriate resource - e.g.,

Selecting a Subset of Fields to Return

Sometimes only subset of all the available fields need to be returned, so it is much more efficient for Elasticsearch only to return the required data as opposed to all of it. This can be achieved as follows,

The selected fields are defined using Elasticsearch’s source filtering API.

Sorting Query Results

Query results can be sorted on multiple fields by defining a sort object using the same Elasticsearch JSON syntax - e.g. to sort by sepal_width in ascending order the required sort object would be defined as,

This is then added to a query object whose results we want sorted and executed using the %search% operator as before - e.g.,

Aggregations

Similarly, any type of aggregation that Elasticsearch makes available can be defined in an aggs object - e.g. to compute the average sepal_width per-species of flower we would specify the following aggregation,

(Elasticsearch 5.x and 6.x users please note that when using the out-of-the-box mappings the above aggregation requires that 'field': 'species' be changed to 'field': 'species.keyword' - see here for more information as to why)

This aggregation is also executed via the %search% operator on the appropriate resource - e.g.,

Queries and aggregations can be combined such that the aggregations are computed on the results of the query. For example, to execute the combination of the above query and aggregation, we would execute,

where the combination yields,

For comprehensive coverage of all query and aggregations types please refer to the rather excellent official documentation (newcomers to Elasticsearch are advised to start with the ‘Query String’ query).

Mappings

We have also included the ability to create an empty index with a custom mapping, using the %create% operator - e.g.,

Where in this instance mapping_default_simple() is a default mapping that I have shipped with elasticsearchr. It switches-off the text analyser for all fields of type ‘string’ (i.e. switches off free text search), allows all text search to work with case-insensitive lower-case terms, and maps any field with the name ‘timestamp’ to type ‘date’, so long as it has the appropriate string or long format.

Cluster and Index Information

We have also added the ability to retrieve basic information from the cluster, using the %info% operator. For example, to retrieve a list of all avaliable indices in the cluster,

Or to list all of the available fields in an index,

Acknowledgements

A big thank you to Hadley Wickham and Jeroen Ooms, the authors of the httr and jsonlite packages that elasticsearchr leans upon heavily. And, to the other contributors and supporters - your efforts are greatly appreciated!