The issue you’re facing seems related to the fact that multiple eahv-iv-2469-000101:decisionRef0 nodes are being processed and appended to a single row in your data frame. This can be resolved by identifying and handling each unique decisionRef0 node separately. Here’s an updated version of your code snippet, including some adjustments to handle the repeated occurrence of eahv-iv-2469-000101:decisionRef0 nodes: ################################################################################################## # Konvertierung von xml zu csv. ################################################################################################## doc <- read_xml(path/my_file) # Namespace bestimmen nmsp <- c(doc = "http://www.

To solve this problem in SQL Server, we can use a CASE statement within our GROUP BY clause. The key is using the YEAR function to separate events by year. Here’s how you could do it: SELECT WellType ,SUM(CASE WHEN YEAR(EventDate) = YEAR(GETDATE()) THEN 1 ELSE 0 END) [THIS YEAR] ,SUM(CASE WHEN YEAR(EventDate) = YEAR(DATEADD(YEAR,-1,GETDATE())) THEN 1 ELSE 0 END) [LAST YEAR] ,SUM(CASE WHEN YEAR(EventDate) = YEAR(DATEADD(YEAR,-2,GETDATE())) THEN 1 ELSE 0 END) [2 YEARS AGO] ,SUM(CASE WHEN YEAR(EventDate) = YEAR(DATEADD(YEAR,-3,GETDATE())) THEN 1 ELSE 0 END) [3 YEARS AGO] FROM #TEMP GROUP BY WellType This query calculates the number of events for each well type this year, last year, two years ago, and three years ago.

Splitting a Pandas DataFrame: A Deeper Dive ============================================= In this article, we will explore how to split a Pandas DataFrame into multiple separate DataFrames where one of the columns is evenly distributed among the resulting DataFrames. We’ll delve deeper into the world of groupby operations and random sampling to achieve this. Introduction Pandas is a powerful library for data manipulation and analysis in Python. One of its key features is the ability to group data by certain columns, also known as factors or variables.

Understanding the Problem and Requirements Overview of the Issue The problem at hand involves updating a single column in a table with dynamic rows based on user selections from an array of vegetables. The goal is to subtract specific values from each row amount based on the selected vegetable. Reviewing the Current Approach The original approach attempts to use a foreach loop to iterate over the $vegetable array and update the amount column in the ingredients table using an UPDATE query.

Filtering a Dataset in Shiny Using an Input Variable In this article, we will explore how to filter a dataset in Shiny using an input variable. We will dive into the details of how to achieve this, including the use of reactive expressions and filtering functions. Introduction Shiny is a popular R package for building web-based interactive applications. One of its key features is the ability to create dynamic interfaces that respond to user input.

Debugging Hidden Functions in R Packages ===================================================== Debugging functions within an R package can be challenging, especially when dealing with “hidden” or non-exported functions. In this article, we’ll delve into the world of R packages and explore how to debug these elusive functions. Understanding Package Structure Before diving into debugging, it’s essential to understand how R packages are structured. A typical R package consists of several files, including: R: The main file that defines the package’s namespace.

Lazy Image Load: A Common Pitfall in iOS Development Understanding the Problem When building an iPhone app, one common challenge developers face is loading images dynamically using lazy image load. The question at hand revolves around how to correctly load images from a documents directory, ensuring that the image URL returned by [NSURL URLWithString:] is not nil. Background on Image Loading and URLs In iOS development, images are typically loaded using the URL class, which provides methods for creating and manipulating URLs.

Extracting Day of Week from Timestamp in Pandas ===================================================== In this article, we will explore how to extract the day of week from a timestamp column in a pandas DataFrame. This is a common requirement in data analysis and data science tasks where you need to perform various operations based on the day of the week. Introduction Timestamps are commonly used to store dates and times in databases, file systems, and other applications.

You can try to use the position_stack() function with a small value for the horizontal margin (the second argument). For example: ggplot()+ geom_text(data=DF_TOT, aes(x=x, y=id_rev,label=word_split), position = position_stack(0.75),size=3) This will stack the text horizontally with a small margin between each letter. Alternatively, you can try to use paste0("\n", word_split) in your geom_text call: ggplot()+ geom_text(data=DF_TOT, aes(x=x, y=id_rev,label=paste0(word_split,"\n")), size=2) This will also add a line break between each letter. However, it may not be the most efficient solution if you have a large number of letters.

Understanding Function Acting on Lists of Arguments As developers, we often find ourselves working with data structures that require manipulation and processing. One common scenario is when we need to apply a function to multiple lists or arguments. However, the implementation can be tricky, especially when dealing with nested lists and complex data types. In this article, we’ll delve into the world of functional programming in R and explore how to write efficient functions that act on lists of arguments.