Importing an XML Schema into Postgres to Automatically Create a Table and Populate it with an XML File As the name suggests, PostgreSQL is a powerful object-relational database system that can store, manage, and manipulate data in various formats. However, sometimes we need to import data from external sources, such as XML files, into our database for further processing or analysis. In this article, we will explore how to import an XML schema into Postgres, automatically create a table based on the schema, and populate it with an XML file.

options(digits.secs=1) myformat.POSIXct <- function(x, digits=0) { x2 <- round(unclass(x), digits) attributes(x2) <- attributes(x) x <- as.POSIXlt(x2) x$sec <- round(x$sec, digits) format.POSIXlt(x, paste("%Y-%m-%d %H:%M:%OS",digits,sep="")) } t1 <- as.POSIXct('2011-10-11 07:49:36.3') format(t1) myformat.POSIXct(t1,1) t2 <- as.POSIXct('2011-10-11 23:59:59.999') format(t2) myformat.POSIXct(t2,0) myformat.POSIXct(t2,1)

Converting String with PM and AM to Timestamp in BigQuery In this article, we will explore how to convert a string field with PM and AM values to a timestamp in BigQuery. We will delve into the world of date and time formats, parsing, and conversion. Understanding the Problem The problem at hand involves converting a string field that contains dates in a Unix timestamp format, but with PM and AM suffixes.

Creating Unique Values and Relating Columns to These in SQL Server 2017 As a newbie to SQL Server, it’s great that you’re finding the database management system extremely useful. However, when it comes to rearranging your SQL structure, things can get tricky. In this article, we’ll explore how to create unique values from a column and relate columns to these new values. Understanding Unique Values In SQL Server, a unique value is a value that appears only once in a table or set of data.

Counting Unique Values Per Month in R In this article, we will explore how to count the number of unique values per month for a given dataset. This can be particularly useful when working with data that contains date fields and you want to group your data by month. Preparation To begin, let’s assume we have a dataset with dead bird records from field observers. The dataset looks like this:

Understanding SQL Joins for Deleting Records from Multiple Tables As a developer, it’s not uncommon to work with multiple tables in a database, and performing actions on those tables simultaneously can be challenging. In this article, we’ll explore how to delete records from two or more tables (table A and table B) using SQL joins. Introduction to SQL Joins Before diving into the solution, let’s first discuss what SQL joins are.

Understanding the UPDATE Statement in Objective-C: Why initWithFormat Works and stringWithFormat Doesn’t In this article, we’ll delve into the world of Objective-C and explore why the UPDATE statement works with initWithFormat but not with stringWithFormat. We’ll examine the underlying mechanics of string formatting and memory management to provide a clear understanding of why this difference exists. Background: String Formatting in Objective-C When working with strings in Objective-C, developers often use the stringWithFormat: method or the initWithFormat: initializer to format strings.

Adding iPod Support to (previously) iPhone Only App Background When starting a new project, it’s not uncommon to inherit existing codebases or apps that were initially developed for one device type. In our case, the app we’re working with was originally designed for iPhones only, and we needed to modify it to also run on iPod Touch devices. Our journey began with Apple’s announcement that they removed the option to set device requirements in iTunes Connect, which had previously been used to specify compatibility for different devices.

Data Manipulation and Analysis in MySQL: Grouping or Eliminating Duplicate Fields in Columns In this article, we will explore a common data manipulation problem in MySQL where you want to group or eliminate duplicate fields in columns. This can be useful in various scenarios such as data cleansing, normalization, or when dealing with redundant information. Background and Problem Statement Imagine you have a table with multiple rows of data, each representing a single record.

Splitting a Data Frame by Location and Saving to Different Files In this article, we will explore how to programmatically split a data frame by location and create separate files for each location. We will use the R programming language and its built-in data structures to achieve this goal. Introduction The problem at hand is to take a large data frame with monthly temperature data for several locations and split it into smaller data frames, one for each location.