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SQL Query: 3 Methods for Calculating Cumulative SUM

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SQL provides various constructs for calculating cumulative sums, offering flexibility and efficiency in data analysis. In this article, we explore three distinct SQL queries that facilitate the computation of cumulative sums. Each query leverages different SQL constructs to achieve the desired outcome, catering to diverse analytical needs and preferences. Using Window Functions (e.g., PostgreSQL, SQL Server, Oracle) SELECT id, value, SUM(value) OVER (ORDER BY id) AS cumulative_sum  FROM your_table; This query uses the SUM() window function with the OVER clause to calculate the cumulative sum of the value column ordered by the id column. Using Subqueries (e.g., MySQL, SQLite): SELECT t1.id, t1.value, SUM(t2.value) AS cumulative_sum FROM your_table t1 JOIN your_table t2 ON t1.id >= t2.id GROUP BY t1.id, t1.value ORDER BY t1.id; This query uses a self-join to calculate the cumulative sum. It joins the table with itself, matching rows where the id in the first table is greater than or

R Language: Data types and structures

To make the best of the R language, you'll need a strong understanding of the basic data types and data structures and how to operate on those. Very Important to understand because these are the things you will manipulate on a day-to-day basis in R. Everything in R is an object.

R language
The basic data types 
  • logical (e.g., TRUE, FALSE)
  • integer (e.g,, 2L, as.integer(3))
  • numeric (real or decimal) (e.g, 2, 2.0, pi)
  • complex (e.g, 1 + 0i, 1 + 4i)
  • character (e.g, "a", "swc")
The basic data structures in R
  • vector
  • list
  • matrix
  • data frame
  • factors
  • tables
Vector in R
A vector is the most common and basic data structure in R and is pretty much the workhorse of R. 
Vectors can be of two types:
  • atomic vectors
  • lists

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