Based on the provided code, the issue seems to be with the validation and creation of the invoice. The not working columns are indeed name, PKWIU, quantity, unit, netunit, nettotal, VATrate, grossunit, and grosstotal. To fix this, you need to update the fillable array in the Invoice model to include these fields. The fillable array specifies which attributes can be mass-assigned during model creation. Here’s an updated version of the Invoice model:

Understanding the Issue with Legend Titles and Color Assignment for Geom Point Data In this blog post, we will delve into a common issue faced by data visualization enthusiasts using R’s ggplot2 library. The problem revolves around correctly assigning colors to geom_point objects within a plot, ensuring that these colors match those assigned to corresponding bars in a separate scale_fill_manual object. Background on Scales and Color Assignment To tackle this challenge, it is essential to understand how scales work in ggplot2.

Understanding SQL Syntax Errors in MariaDB When creating tables in MariaDB, users often encounter syntax errors that can be frustrating to resolve. In this article, we will delve into the specifics of the error encountered and provide a comprehensive explanation of the necessary adjustments to ensure successful table creation. Error Analysis The provided stack trace reveals an SQL syntax error (Error #1064) while attempting to create a table named classes. The exact issue lies in the definition of the primary key, specifically with the keyword PRIMARY.

Understanding the Behavior of SQL Server in the Presence of Power Outage When a machine with SQL Server installed experiences a power outage while an update query is executing, it can lead to inconsistent data behavior. In this article, we will delve into the specifics of how SQL Server handles transactions and updates during power outages, exploring two scenarios: one where the update query does not involve transactions, and another where it does.

Understanding Caret’s Coefficient Name Renaming in Machine Learning Models Introduction to the Problem In machine learning, the caret library is a popular package used for model training, tuning, and evaluation. One of its features is the automatic renaming of coefficient names in linear regression models. However, this feature can sometimes lead to unexpected results, as demonstrated by the example provided. The question posed in the Stack Overflow post raises an important concern: why does caret rename the coefficient name?

Implementing Login Screen in an iPhone App Using TabBarController =========================================================== In this article, we’ll explore how to implement a login screen in an iPhone app using a tabBarController. We’ll dive into the different approaches and provide code examples to help you achieve this. Understanding the Problem The question at hand is how to display a login screen when using a tabBarController instead of a navigationController. The goal is to create an authentication system that allows users to log in or out of the app without having to navigate through multiple screens.

Count Distinct IDs in a Date Range Given a Start and End Time In this article, we will explore how to count distinct IDs in a date range given a start and end time. We’ll delve into the world of BigQuery and provide an example solution using SQL. Understanding the Problem The problem at hand involves a table with multiple rows for each ID, where each row has a start_date, end_date, location, and type.

Here is a concise and readable code to achieve the desired output: library(dplyr) # Convert wide format to long format dat %>% unnest_longer(df_list, name = "value", remove_match = FALSE) # Remove rows with NA values mutate(value = as.integer(value)) This code uses the unnest_longer function from the dplyr library to convert the wide format into a long format. The name = "value" argument specifies that the column names in the long format should be named “value”.

Installing Pandas in Python 3 on macOS ===================================== As a developer, it’s common to encounter issues with package installations across different Python versions. In this article, we’ll explore the steps required to install the popular data analysis library, pandas, in Python 3 on macOS using pip and conda. Background: Understanding Package Installation In Python, packages are pre-written code that provides a specific functionality. Installing packages is crucial for extending the capabilities of our projects.

Understanding Presto SQL and Binning Data As a technical blogger, I’ve encountered numerous questions on optimizing queries and manipulating data. One such question that caught my attention was about creating bins in Presto SQL using programming techniques. In this article, we’ll delve into the world of Presto SQL and explore how to bin data into specified ranges programmatically. What is Presto SQL? Presto SQL is an open-source, distributed SQL query engine designed for large-scale data processing.