Matching Lines Between Two Expressions Using Regex Introduction Regular expressions (regex) are a powerful tool for pattern matching and text processing. In this article, we will explore how to use regex to match lines between two expressions in a string. Understanding the Problem The problem is as follows: given a string with two useful sections separated by one or more lines of rubbish, we want to extract the useful sections while ignoring the rubbish.

The issue here is that the date range in returnTest is not complete. You are missing a row for June 2020. To fix this, you need to identify which dates are missing and add them manually. In your code, you used test2[, 'orderDate' := returnDate] which only sets the orderDate column in test2 to be the same as returnDate. However, when merging test1 and test2, you are using merge(test1, test2[, c('orderDate', 'totalReturns'), all = TRUE, with = FALSE]).

Understanding the Law Behind Using Sample Code Introduction When developing an iPhone application, one often comes across various sample projects and examples downloaded from the official Apple Developer website. These samples can be incredibly valuable resources for learning new technologies, exploring different features, and even incorporating specific functionality into your own app. However, a question that often arises among developers is: “Is it okay to use these sample codes in my application?

Introduction to Camera Preview inside Window with iPhone =========================================================== In this blog post, we’ll explore how to embed a camera preview into an application window using an iPhone’s built-in camera functionality. We’ll delve into the technical details of using UIImagePickerController and provide guidance on achieving a seamless camera preview experience. Understanding UIImagePickerController The UIImagePickerController class is a part of Apple’s iOS SDK, which allows developers to access and manage media (images and videos) on an iPhone or iPad device.

To combine the words in a document term matrix (DTM) using the tm package in R, you can create a custom function to replace the old words with the new ones and then apply it to each document. Here’s an example: library(tm) library(stringr) # Define the function to replace words replaceWords <- function(x, from, keep) { regex_pat <- paste(from, collapse = "|") x <- gsub(regex_pat, keep, x) return(x) } # Define the old and new words oldwords <- c("abroad", "access", "accid") newword <- "accid" # Create a corpus from the text data corpus <- Corpus(VectorSource(text_infos$my_docs)) # Convert all texts to lowercase corpus <- tm_map(corpus, tolower) # Remove punctuation and numbers corpus <- tm_map(corpus, removePunctuation) corpus <- tm_map(corpus, removeNumbers) # Create a dictionary of old words to new ones dict <- list(oldword=newword) # Map the function to each document in the corpus corpus <- tm_map(corpus, function(x) { # Remove stopwords x <- tm_remove(x, stopwords(kind = "en")) # Replace words based on the dictionary for (word in names(dict)) { if (grepl(word, x)) { x <- replaceWords(x, word, dict[[word]]) } } return(x) }) # View the updated corpus summary(corpus) This code defines a function replaceWords that takes an input string and two arguments: from and keep.

Working with Google Sheets APIv4 Comment Data in Pandas In this article, we’ll delve into the intricacies of working with comment data retrieved from the Google Sheets APIv4. We’ll explore how to transform this data into a pandas DataFrame that mirrors the original sheet’s range, including handling blank cells and creating a structured table. Introduction to Google Sheets APIv4 Comment Data When using the Google Sheets APIv4, you can retrieve comment data for specific ranges in a spreadsheet.

cell color by value and text transformations in gt Introduction The gt package is a popular data visualization library in R, known for its flexibility and customizability. One of its powerful features is the ability to transform cells based on specific conditions or values. In this article, we’ll explore how to use these capabilities to create tables with colored cells and apply text transformations. Background The gt package provides a high-level interface for creating interactive visualizations.

Working with Excel Files in Python Using Pandas In this article, we will explore how to work with Excel files using the popular Python library pandas. We’ll delve into the details of reading and manipulating Excel data, focusing on a specific scenario where rows from one Excel file need to be moved to the end of another. Introduction Python is an excellent language for data analysis, thanks in part to its ability to interact seamlessly with various libraries and frameworks, including pandas.

Removing Trailing Spaces and Newlines from an NSString in Objective-C Removing trailing spaces and newlines from a string is a common requirement in various applications, especially when dealing with user input or file paths. In this article, we will explore how to achieve this using Objective-C. Understanding the Problem When working with strings in Objective-C, it’s essential to understand that strings are immutable by design. This means that once a string is created, its contents cannot be modified directly.

Symbolic Computation Using Regex and Eval() in Python In this blog post, we will explore the use of regular expressions (regex) and the eval() function in Python to perform symbolic computation on financial models. We will delve into the details of how regex can be used to parse and evaluate mathematical expressions, and how this can be applied to build a generic cash flow model. Introduction Symbolic computation is a powerful technique that allows us to perform calculations using mathematical expressions rather than numerical values.