Download Automated Data Collection with R: A Practical Guide to Web by Simon Munzert, Christian Rubba, Dominic Nyhuis, Peter Meiner PDF

By Simon Munzert, Christian Rubba, Dominic Nyhuis, Peter Meiner

A arms on consultant to net scraping and textual content mining for either novices and skilled clients of R Introduces basic recommendations of the most structure of the net and databases and covers HTTP, HTML, XML, JSON, SQL.

Provides uncomplicated strategies to question net files and information units (XPath and commonplace expressions). an in depth set of workouts are awarded to lead the reader via every one approach.

Explores either supervised and unsupervised thoughts in addition to complex options reminiscent of information scraping and textual content administration. Case reviews are featured all through besides examples for every procedure provided. R code and ideas to routines featured within the booklet are supplied on a aiding site.

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Extra info for Automated Data Collection with R: A Practical Guide to Web Scraping and Text Mining

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The authentication process is limited to user verification and identification. Processing user access control is known as authorization. Database authorization means managing a user’s role and privileges to schema, tables, and columns. Cassandra’s internal authentication is an SSL-encryption mechanism that we’ll look at in the form of practical recipes.

The random partitioner would evenly distribute data in a cluster. Data distribution would rely on assigned initial_token value or num_tokens for assigning rows to each node. MD5 hashes are 16 bytes and are used to represent hexadecimal digits. Each node is assigned a data range that is represented by the token value. On receiving read/write request, with random partitioner selected as the partitioning strategy hash value for each row key gets generated and assigned to the node responsible for serving that read/write request.

A conditional DDL allows a user to validate whether a questioned keyspace, column family, or index is present or not. Let’s look at a few examples of how this works. Keyspaces • Create the keyspace twitter if it doesn’t exist: create keyspace if not exists twitter with replication = {'class':'SimpleStrategy', 'replication_factor' : 3}; • Drop the keyspace twitter if it exists: drop keyspace if exists twitter; Tables • Create a table users if it doesn’t exist: create table if not exists users(user_id text,followers set, tweet_date timestamp, tweet_body text, first_name text, PRIMARY KEY(user_id,tweet_date, first_name)); • Drop the table users if it exists: drop table if exists users; Indexes • Create an index over column first_name on the users table if it doesn’t exist: create index if not exists users_first_name_idx on users(first_name); • Drop the index users_first_name_idx if it exists: drop index if exists users_first_name_idx; 59 Chapter 3 ■ Indexes and Composite Columns Summary With this chapter we have discussed data modeling and indexing concepts and their use in Cassandra.

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