To solve this problem in a more efficient manner, we can use the rowwise() function from the dplyr package to repeat elements a certain number of times and then use unnest() to convert the resulting list of vectors into separate rows. Here’s how you can do it: library(tidyverse) sched <- weekly_data %>% mutate(max_weeks = max(cd_dur_weeks + ca_dur_weeks)) %>% rowwise() %>% mutate( week = list( c(rep(hrs_per_week_cd, cd_dur_weeks), rep(0, (max_weeks - cd_dur_weeks)), rep(hrs_per_week_ca, ca_dur_weeks)), c(rep(0, (max_weeks - cd_dur_weeks)), rep(hrs_per_week_cd, cd_dur_weeks), rep(0, ca_dur_weeks)) ) ) %>% ungroup() %>% select(dsk_proj_number = dsk_proj_number) %>% # rename the columns pivot_wider(names_from = "dsk_proj_number", values_from = week) This code achieves the same result as your original code but with less manual repetition and error-prone logic.

Understanding POSIX Time and Its Conversion to Date-Time Format As a technical blogger, it’s essential to understand the intricacies of time formats, especially when working with various data sources. In this section, we’ll delve into the world of POSIX time and explore its conversion to date-time format. What is POSIX Time? POSIX (Portable Operating System Interface) time is a standard for representing dates and times in a portable and unambiguous manner.

Understanding the iPhone’s Modal View Hierarchy When it comes to accessing a modal view in an iPhone application, there are several key concepts to grasp. In this article, we will delve into the technical details of how modals work and explore strategies for accessing them from the app delegate. The Role of the App Delegate The app delegate is the entry point of your application and plays a crucial role in managing its lifecycle.

Understanding the Issue with pandas.to_datetime The Problem with Inferring Date Format in pandas The pandas.to_datetime function is a powerful tool for converting strings into datetime objects. However, it can be finicky about date formats, especially when they are not explicitly specified. In this article, we will explore an issue where the default inference of date format does not work as expected, even with the infer_datetime_format and exact parameters set. Background The problem at hand arises from a known bug in pandas, which affects how it handles date formats when reading files using read_csv or read_fwf.

Understanding Ionicons and flexdashboard Introduction to Ionicons Ionicons is a popular icon library used for building user interfaces. It offers a wide range of icons that can be easily integrated into various frameworks, including R Studio’s flexdashboard. Ionicons provides two main versions of its icons: v1 and v2. The v1 version is the older of the two and uses a different naming convention compared to the v2 version. Understanding the correct naming conventions for both versions is crucial when using Ionicons with flexdashboard.

Iterating Over Pandas Dataframe and Saving into Separate Sheets in XLSX File In this blog post, we will explore how to iterate over a pandas DataFrame and save it into separate sheets in an XLSX file. This can be achieved using the openpyxl library, which allows us to create and manipulate Excel files programmatically. Introduction The openpyxl library provides an easy-to-use interface for creating and editing Excel files. It supports various features, including reading and writing worksheets, formatting cells, and adding hyperlinks.

Understanding Pandas DataFrames and Indexing Issues Introduction to Pandas DataFrames Pandas is a powerful library in Python that provides data structures and functions designed to handle structured data, including tabular data such as spreadsheets and SQL tables. One of the key data structures in pandas is the DataFrame, which is a two-dimensional table of data with rows and columns. Indexing in Pandas DataFrames In pandas DataFrames, indexing allows you to access specific rows or columns.

SQL Data Insertion Best Practices: Avoiding Duplicate Rows =========================================================== As developers, we have encountered various challenges while working with databases, particularly when it comes to data insertion. In this article, we will explore a common issue involving duplicate rows in tables and provide solutions using SQL. Understanding the Problem The problem at hand is as follows: You have a table price with columns id, item_name, date, and price. The table has multiple prices for an item_name.

Understanding Non-Standard Evaluation in R ===================================================== Non-standard evaluation (NSE) is a feature of the R programming language that allows for more flexible and expressive syntax. In this answer, we will explore how to use NSE to achieve a specific goal. Background The original question provided a dataframe stage_refs with two columns new.diff.var and var.1 that were used as arguments in the difftime_fun function. The intention was to apply this function to each row of stage_refs, but the problem statement was encountering non-standard evaluation problems.

Understanding iOS Calendar Implementation: Positioning the Calendar View =========================================================== In this article, we will delve into the world of iOS calendar implementation and explore how to change the position of a calendar view on an iPhone. We will examine the underlying concepts and techniques involved in implementing this functionality. Introduction to Tapku Library The Tapkul library is a popular open-source library used for building iOS calendars. It provides an easy-to-use API for creating calendar views, handling events, and more.