The provided query already seems optimized, considering the use of a CTE to improve performance on JSON data. However, there are still some potential improvements that can be explored. Here’s an updated version of your query: WITH cf as ( SELECT cfiles.property_values::jsonb AS prop_vals, users.email, cfiles.name AS cfile_name, cfiles.id AS cfile_id FROM cfiles LEFT JOIN user_permissions ON (user_permissions.cfile_id = cfiles.id) LEFT JOIN users on users.id = user_permissions.user_id ORDER BY email NULLS LAST LIMIT 20 ) SELECT cf.

Understanding NaN in Numpy and Pandas ===================================================== In the world of numerical computing, it’s essential to understand how missing values are represented. Numpy and pandas, two popular libraries used for scientific computing and data analysis, have specific ways to handle missing values. In this article, we’ll delve into the details of NaN (Not a Number) in both Numpy and pandas. What is NaN? NaN is a special value that represents an undefined or missing result in numerical computations.

Navigation Bar Under Status Bar After Video Playback in Landscape Mode ================================================================================ In this article, we will explore a common issue encountered by iOS developers when creating applications that use web views to play videos. Specifically, we will discuss how to correct the navigation bar’s position under the status bar after video playback in landscape mode. Background and Context When developing iOS applications, it’s essential to understand how the operating system manages the user interface.

Understanding Plotting in R with a for Loop As a programmer, it’s not uncommon to encounter unexpected behavior when working with loops and plotting functions. In this article, we’ll delve into the world of plotting in R using a for loop and explore why subtracting from the counter doesn’t work as expected. Introduction to Plotting in R R is a popular programming language for statistical computing and graphics. The plot() function is used to create plots, which can be used to visualize data and trends.

Handling Comma and Double Quotes in CSV Files When Importing in Informatica As data analysts and administrators, we often encounter files with comma-separated values (CSV) that require careful handling when importing into various systems. One such scenario is when working with Informatica PowerCenter, a popular enterprise software for data integration and analytics. In this article, we’ll explore how to handle CSV files with both commas and double quotes in Informatica.

Understanding Objective-C and iOS Development When it comes to developing iOS applications, understanding the basics of Objective-C and its syntax is crucial. In this article, we will delve into the world of iOS development and explore how to send text field value to another class. What is Objective-C? Objective-C is a high-level, dynamically-typed programming language developed by Apple specifically for developing software for macOS and iOS operating systems. It was first released in 1983 and has since become one of the most widely used programming languages for iOS development.

Understanding Collision Detection with Rotated Rectangles Introduction When working with images, collision detection is an essential concept to consider, especially when dealing with rotated rectangles. In this article, we will explore how to use CGRectIntersectsRect and other techniques for collision detection with rotated rectangles. Background on CGRectIntersectsRect CGRectIntersectsRect is a function in Apple’s Cocoa framework that checks if two rectangles intersect. It takes two CGRect structs as arguments: the first rectangle, which defines its position and size, and the second rectangle, which defines its position and size relative to the first rectangle.

Grouping and Counting by Condition in Pandas Pandas is a powerful library for data manipulation and analysis in Python. One of its most versatile features is the ability to group data by multiple columns and perform various operations on the resulting groups. In this article, we’ll explore how to group data by condition using pandas’ groupby function. We’ll start with an example dataset and then move on to different approaches for achieving our goal.

Duplicate Records based on Field Value Access As a technical blogger, I’ve encountered numerous requests for help with creating duplicate records in databases. In this article, we’ll delve into the world of SQL and explore how to create duplicate records based on field value access. Introduction In today’s fast-paced business environments, data management is crucial for making informed decisions. One common requirement is to create duplicate records in a database table based on specific field values.

The issue here is that the ggraph function expects a graph object, but you’re providing an igraph layout object instead. To fix this, you need to transpose the layout using the layout_as_tree function from the igraph package. Here’s how you can do it: # desired transpose layout l_igraph <- ggraph::create_layout( g_tidy, layout = 'tree', root = igraph::get.vertex.attribute(g_tidy, "name") %>% stringr::str_detect(., "parent") %>% which(.) ) %>% .[, 2:1] ggraph::ggraph(graph = g_tidy, layout = l_igraph) + ggraph::geom_edge_link() + ggraph::geom_node_point() This will create a transposed version of the original top-down tree layout and then use that as the graph for the ggraph function.