Here is the complete code with formatting and comments for better readability: # Load necessary libraries library(ggplot2) library(data.table) # Create example dataframes seed1 <- data.frame(grp = c("data"), value = rnorm(10)) seed2 <- seed3 <- seed1 # Function to plot box plots for column types 'cr', 'pd' and 'st_po' plot_box_plots <- function(d) { # Reformat data before plotting dplot <- rbindlist( sapply(c("cr", "pd", "st_po"), function(i){ cols <- c("data", colnames(d)[ startsWith(colnames(d), i) ]) x <- melt(d[, .

Using wait=FALSE in system() with Multiline Commands Introduction The system() function in R is a powerful tool for executing shell commands. It allows developers to run external commands and scripts, capturing their output and errors as part of the R process. However, when dealing with multiline commands, the behavior of system() can be counterintuitive. In this article, we will explore why wait=FALSE in system() only waits for the first command, how to overcome this limitation, and provide alternative solutions.

Understanding Stacked Bar Plots and Reordering in ggplot2 Introduction to Stacked Bar Plots Stacked bar plots are a type of visualization used in data analysis to compare the proportion of different categories within a single group. They consist of multiple bars stacked on top of each other, with each bar representing a category or subgroup. Each point in the bar corresponds to a specific value or count. Using ggplot2 for Stacked Bar Plots ggplot2 is a popular R package for data visualization that provides a wide range of tools and techniques for creating high-quality plots.

SQL Select Data Based on One-to-Many Relations SQL is a powerful language for managing relational databases, and understanding how to effectively query data based on relationships between tables is crucial for any database administrator or developer. In this article, we’ll explore a common challenge many developers face: selecting data from multiple tables based on one-to-many relations. Introduction One-to-many relationships occur when one table (the “parent” table) contains a foreign key that references the primary key of another table (the “child” table).

Conditional Aggregation in SQL: Counting Occurrences of Values In this article, we will explore the concept of conditional aggregation in SQL and how it can be used to count occurrences of values in a column. We’ll take a closer look at using subqueries and Common Table Expressions (CTEs) to achieve this, as well as an alternative approach using grouping with aggregate functions. Introduction Conditional aggregation is a powerful feature in SQL that allows you to perform calculations on columns based on specific conditions.

Handling subview’s touch events within its parent while retaining screen coordinate data relative to window Overview In this article, we will discuss how to handle touch events for a subview (in this case, an UIImageView) that is covered by its parent view (UIImageView as well). The main goal is to be able to capture the touch events and use them to perform actions on either the child or parent view. We’ll explore two scenarios: one where the child touches send events to the parent, and another where the parent needs to receive touch events with coordinates relative to the window.

Customizing R’s Autocompletion for Custom Classes In this article, we will explore how to enable autocompletion in custom classes in R. We’ll delve into the setClass function, the names method, and the .DollarNames generic function, providing a comprehensive understanding of how to customize R’s autocompletion behavior. Introduction to Custom Classes In R, custom classes are created using the setClass function, which allows users to define their own class structure. This can be useful for creating specialized data structures that meet specific needs.

Python pandas.get_dummies Generates Duplicate Field Names When Handling 2D Arrays with Different Values Order In this article, we will delve into the issue of duplicate column names generated by the get_dummies function from pandas when dealing with 2D arrays. We’ll explore possible causes and solutions for handling such scenarios. Understanding get_dummies The get_dummies function in pandas is used to create dummy/one-hot encoding for categorical data. When applied to a DataFrame, it creates new columns representing the unique values in each column of the original DataFrame.

Frequencies by Date In this article, we’ll explore how to calculate daily frequencies of status variables in a dataframe. We’ll use the tidyverse packages and pivot_longer function to transform the data into a more suitable format for analysis. Problem Description We have a dataframe with thousands of rows, each case having a date and four status variables (yes/no answers) with some cases also missing values. The goal is to create daily distributions of these answers in bar graphs, showing the number of missing, ‘Yes’, and ‘No’ responses for each day.

Filtering Records in a Table by a Composite Primary Key in RedShift Introduction RedShift is an open-source column-store database that provides fast query performance for analytical workloads. While it offers many benefits, working with large datasets can be challenging, especially when dealing with composite primary keys. In this article, we’ll explore how to filter records in a table by a composite primary key and discuss the approaches and pitfalls of doing so.