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5 Super SEO Blogger Tools

In this post, I have explained top blogging tools that need to be considered by every blogger. These tools help you write better SEO friendly blog posts.



1). Headline Analyzer The best tool is the EMV Headline Analyzer. When you enter the headline it analyzes it and gives you EMV ranking. When you get '50' and above it performs better SEO.

2). Headline Length Checker The usual headline length is 50 to 60 characters. Beyond that, the headline will get truncated and looks ugly for search engine users. The tool SERP Snippet Optimization Tool useful to know how it appears in the search results.

3). Free Submission to Search Engines The tool Ping-O-Matic is a nice free submission tool. After your blog post, you can submit your feed to Ping-O-Matic. It submits to search engines freely.

4). Spell and Grammar Check Another free tool is Grammarly, this tool checks your spelling and grammar mistakes. So that you can avoid small mistakes.

5). Keyword AnalyzerWordstream Keyword analyzer i…

R language five useful real functions

In Data Science R language plays a crucial role. In the R language, there are five top functions present. These functions I have explained in this post.
#5-key-points-in-r

1. Storing Values

  • Stores a value to variable. The value can be same or mixed data type.
  • It is available /* */ to give comments for your scripts inside
  • Char, Double, Boolean and Decimal are more frequently used data types

2. Reading data from files

  • Large data objects will usually be read as values from external files rather than entered during an R session at the keyboard. 
  • R input facilities are simple and their requirements are fairly strict and even rather inflexible. There is a clear presumption by the designers of R that you will be able to modify your input files using other tools, such as file editors or Perl1 to fit in with the requirements of R. Generally this is very simple.
  • If variables are to be held mainly in data frames, as we strongly suggest they should be, an entire data frame can be read directly with the read.table() function. 
  • There is also a more primitive input function, scan(), that can be called directly. For more details on importing data into R and also exporting data, see the R Data Import/Export manual.

3. Accessing builtin datasets

  • Around 100 datasets are supplied with R (in package datasets), and others are available in packages (including the recommended packages supplied with R). To see the list of datasets currently available use data().
  • All the datasets supplied with R are available directly by name. However, many packages still use the obsolete convention in which data was also used to load datasets into R, for example data(infert) and this can still be used with the standard packages (as in this example). 
  • In most cases this will load an R object of the same name. However, in a few cases it loads several objects, so see the on-line help for the object to see what to expect.

4. Grouped expressions

  • R is an expression language in the sense that its only command type is a function or expression which returns a result. Even an assignment is an expression whose result is the value assigned,and it may be used wherever any expression may be used; in particular multiple assignments are possible. 
  • Commands may be grouped together in braces, {expr_1; ...; expr_m}, in which case the value of the group is the result of the last expression in the group evaluated. Since such a group is also an expression it may, for example, be itself included in parentheses and used a part of an even larger expression, and so on

5. Writing your own functions

  • R language allows the user to create objects of mode function. These are true R functions that are stored in a special internal form and may be used in further expressions and so on. In the process, the language gains enormously in power,convenience and elegance, and learning to write useful functions is one of the main ways to make your use of R comfortable and productive. 
  • It should be emphasized that most of the functions supplied as part of the R system, such as mean(), var(), postscript() and so on, are themselves written in R and thus do not differ materially from user written functions.

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