Fit a Normal Distribution to a Histogram with an X-Log Scale In this article, we will explore how to fit a normal distribution to a histogram that has been transformed on the x-axis using a logarithmic scale. This is often necessary when dealing with datasets that have long tails or skewed distributions. Background When a dataset has a long tail or skewed distribution, it can be challenging to visualize and analyze using traditional histograms.

You’re trying to map over sequences of hours that can be used for agg_time_period filter, but you want to create a wrapper function .f() that can accept various types and functions. Here is an alternative way of mapping the sequences: seq_hours <- list(1:5, 6:9, 10:15, 16:30) Map(function(i){ slice_of_data <- .f(i) #insert whatever function you want that #rasterizes/stores the grouped records that met condition here }, seq_hours) # if you still want to map directly on seq_hours Map(function(x){ return .

Understanding the iPhone Crash when Reloading a TableView Row When it comes to implementing a table view in an iOS application, one of the most common challenges developers face is handling cell updates and row reloads. In this article, we’ll delve into the technical details behind the crash you’re experiencing and explore the necessary steps to resolve the issue. The Problem: Crash when Reloading a TableView Row The error message “The number of rows in section zero is not equal to the number of rows in section zero before the update” indicates that the table view’s internal state has become inconsistent.

Representing a Single Linked List in Postgres and Fetching All Previous Nodes for the Specific Node In this blog post, we’ll explore how to represent a single linked list in PostgreSQL and fetch all previous nodes for a specific node. We’ll delve into the concepts of recursive Common Table Expressions (CTEs) and array manipulation to achieve this. Background on Linked Lists A linked list is a data structure consisting of nodes, each containing some data and a reference (or link) to the next node in the sequence.

Ranking by Partition with a Conditional Expression In this article, we’ll explore the concept of ranking in SQL and how to implement it using partitioning. We’ll also delve into the nuances of conditional expressions and how to use them to achieve specific results. Understanding Ranking Ranking is a popular feature in SQL that allows us to assign a rank or position to each row within a result set based on certain conditions.

Removing the First Column from a List of DataFrames in R Introduction In this article, we will explore how to remove the first column from a list of DataFrames in R. We will cover various approaches using different libraries and techniques. Background Data manipulation is an essential task when working with data in R. When dealing with lists of DataFrames, it can be challenging to perform operations that require modifying the structure of the data.

Understanding the Basics of Travis CI and GitHub Integration As a developer, it’s common to use version control systems like Git for managing changes to your codebase. Travis CI is a popular continuous integration platform that allows you to automate testing, building, and deployment of your projects. In this article, we’ll explore how to integrate Travis CI with your GitHub repository to ensure seamless deployment of your project. The Problem: Pushing to Master Branch from Dev Branch You’ve set up your R package in GitHub and want to ensure that every commit in the master branch has successfully built on Travis CI.

Installing the Latest Version of STAN in R ============================================= STAN (Stan Modeling Language) is a statistical modeling language used for Bayesian modeling and analysis. It has become increasingly popular due to its ability to handle complex models and large datasets efficiently. In this article, we will walk through the process of installing the latest version of STAN in R. Introduction to STAN STAN was first introduced by Edward Carpenter and Ben Goodrich in 2010 as a way to perform Bayesian modeling using Markov Chain Monte Carlo (MCMC) methods.

Airflow 1.10.9 - cannot import name ‘_check_google_client_version’ from ‘pandas_gbq.gbq’ Problem Overview In this blog post, we will delve into a specific issue that occurred on an Airflow cluster running version 1.10.9, where the pandas_gbqgbq 0.15.0 release caused problems due to changes in the import statement of _check_google_client_version from pandas_gbq.gbq. We’ll explore how this issue can be resolved by looking into Airflow’s packaging and constraint files. Background Airflow is a popular open-source platform for programmatically managing workflows and tasks.

Statement Binding/Execution Order in Snowflake One of the things I like about Snowflake is it’s not as strict about when clauses are made available to other clauses. For example in the following: WITH tbl (name, age) as ( SELECT * FROM values ('david',10), ('tom',20) ) select name, age, year(current_timestamp())-age as birthyear from tbl where birthyear > 2010; I can use birthyear in the WHERE clause. This would be in contrast to something like SQL Server, where the binding is much more strict, for example here.