Running Call Columns Data of Another DataFrame Row by Row ===================================================================== Introduction In this article, we’ll explore how to run call columns data of another dataframe row by row using the sapply function from R’s base library. This process involves iterating over each unique value in a column and applying a custom function to it. We’ll start with an example where we have two dataframes: df1 and df2. The goal is to calculate the sum of values in each row of df1 for corresponding rows in df2, using the first three characters of the first column (a, b, or c) as a unique identifier.

Understanding the Limits of UIActivityViewController When it comes to sharing content from an app, UIActivityViewController is a popular choice for creating a seamless and intuitive user experience. However, there are some limitations and gotchas associated with this class that can lead to unexpected behavior if not handled correctly. In this article, we’ll delve into the world of UIActivityViewController, exploring its capabilities, limitations, and potential pitfalls. Specifically, we’ll focus on the issue of service names not appearing in the service picker when using UIActivityViewController to share an image from an app.

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.

Building R Packages from Loose Files on Windows ===================================================== As an R developer, creating and managing R packages can be a daunting task. One of the common questions asked by new developers is how to compile packages from loose files on Windows using the CMD INSTALL command. This blog post aims to provide a comprehensive guide on building R packages from loose files on Windows. Introduction R packages are a collection of R code, data, and documentation that can be easily installed and managed.

Understanding R Data Frames and Normalization Introduction to R Data Frames R is a popular programming language for statistical computing and graphics. It provides an environment in which to write, test, and execute code in R. In this article, we will explore how to manipulate data frames in R. A data frame in R is a two-dimensional table of values. Each column represents a variable, while each row represents an observation or record.

Creating Working Hyperlinks with Leaflet from CSV Input Column Introduction This article aims to provide step-by-step instructions on how to create working hyperlinks within a Leaflet map using information from a column in a CSV input file. We will explore the necessary steps, including data manipulation and string formatting, to achieve this goal. Understanding the Problem The problem at hand arises when trying to display hyperlinks within a Leaflet popup using information from a CSV input file.

Introduction to lmer and p-values in R ===================================================== In this article, we will delve into the world of linear mixed effects models using the lmer function in R, specifically focusing on how p-values are used to create the stars listed by the screenreg command. What is a Linear Mixed Effects Model? A linear mixed effects model (LME) is a statistical model that extends the traditional linear regression model to account for variation due to unobserved factors, such as individual differences in subjects or cluster effects.

Multiple Arg Names Barplot In this article, we’ll delve into the world of barplots and explore how to create a stacked barplot with multiple argument names. We’ll also discuss some common challenges that arise when creating these types of plots. Table of Contents Introduction Creating a Stacked Barplot Labeling Bars with Additional Names Example Code and Explanation Introduction Barplots are an excellent way to visualize categorical data. However, when working with stacked barplots, we often need to add additional information to the plot, such as timepoints or labels for each bar.

Dplyr Summarize: Combining values for certain groups Introduction In this post, we will explore how to use the dplyr library in R to summarize data based on certain conditions. We’ll focus on combining values for specific groups using the summarise function and its various options. We’ll use a simple example dataset representing hospital admissions per patient, where we want to calculate the total cost of care for patients who were re-admitted within 5 days of their initial admission.

Displaying Data Values in a Bar Chart with plotly ===================================================== In this article, we’ll explore how to display data values above each bar in a bar chart created using the plotly library in R. Introduction The plotly library is a powerful and interactive way to visualize data. It allows us to create complex plots with ease and customize them to suit our needs. In this article, we’ll focus on displaying data values above each bar in a bar chart.